1
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Pan C, Reinert K. Leaf: an ultrafast filter for population-scale long-read SV detection. Genome Biol 2024; 25:155. [PMID: 38872200 DOI: 10.1186/s13059-024-03297-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.
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Affiliation(s)
- Chenxu Pan
- Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany.
| | - Knut Reinert
- Department of Mathematics and Computer Science, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
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2
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Van Deynze K, Mumm C, Maltby CJ, Switzenberg JA, Todd PK, Boyle AP. Enhanced Detection and Genotyping of Disease-Associated Tandem Repeats Using HMMSTR and Targeted Long-Read Sequencing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306681. [PMID: 38746091 PMCID: PMC11092683 DOI: 10.1101/2024.05.01.24306681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Tandem repeat sequences comprise approximately 8% of the human genome and are linked to more than 50 neurodegenerative disorders. Accurate characterization of disease-associated repeat loci remains resource intensive and often lacks high resolution genotype calls. We introduce a multiplexed, targeted nanopore sequencing panel and HMMSTR, a sequence-based tandem repeat copy number caller. HMMSTR outperforms current signal- and sequence-based callers relative to two assemblies and we show it performs with high accuracy in heterozygous regions and at low read coverage. The flexible panel allows us to capture disease associated regions at an average coverage of >150x. Using these tools, we successfully characterize known or suspected repeat expansions in patient derived samples. In these samples we also identify unexpected expanded alleles at tandem repeat loci not previously associated with the underlying diagnosis. This genotyping approach for tandem repeat expansions is scalable, simple, flexible, and accurate, offering significant potential for diagnostic applications and investigation of expansion co-occurrence in neurodegenerative disorders. Abstract Figure
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3
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Espinosa E, Bautista R, Larrosa R, Plata O. Advancements in long-read genome sequencing technologies and algorithms. Genomics 2024; 116:110842. [PMID: 38608738 DOI: 10.1016/j.ygeno.2024.110842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/01/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
The recent advent of long read sequencing technologies, such as Pacific Biosciences (PacBio) and Oxford Nanopore technology (ONT), have led to substantial improvements in accuracy and computational cost in sequencing genomes. However, de novo whole-genome assembly still presents significant challenges related to the quality of the results. Pursuing de novo whole-genome assembly remains a formidable challenge, underscored by intricate considerations surrounding computational demands and result quality. As sequencing accuracy and throughput steadily advance, a continuous stream of innovative assembly tools floods the field. Navigating this dynamic landscape necessitates a reasonable choice of sequencing platform, depth, and assembly tools to orchestrate high-quality genome reconstructions. This comprehensive review delves into the intricate interplay between cutting-edge long read sequencing technologies, assembly methodologies, and the ever-evolving field of genomics. With a focus on addressing the pivotal challenges and harnessing the opportunities presented by these advancements, we provide an in-depth exploration of the crucial factors influencing the selection of optimal strategies for achieving robust and insightful genome assemblies.
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Affiliation(s)
- Elena Espinosa
- Department of Computer Architecture, University of Malaga, Louis Pasteur, 35, Campus de Teatinos, Malaga 29071, Spain.
| | - Rocio Bautista
- Supercomputing and Bioinnovation Center, University of Malaga, C. Severo Ochoa, 34, Malaga 29590, Spain.
| | - Rafael Larrosa
- Department of Computer Architecture, University of Malaga, Louis Pasteur, 35, Campus de Teatinos, Malaga 29071, Spain; Supercomputing and Bioinnovation Center, University of Malaga, C. Severo Ochoa, 34, Malaga 29590, Spain.
| | - Oscar Plata
- Department of Computer Architecture, University of Malaga, Louis Pasteur, 35, Campus de Teatinos, Malaga 29071, Spain.
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4
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Li H, Durbin R. Genome assembly in the telomere-to-telomere era. Nat Rev Genet 2024:10.1038/s41576-024-00718-w. [PMID: 38649458 DOI: 10.1038/s41576-024-00718-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2024] [Indexed: 04/25/2024]
Abstract
Genome sequences largely determine the biology and encode the history of an organism, and de novo assembly - the process of reconstructing the genome sequence of an organism from sequencing reads - has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best, but now technological advances in long-read sequencing enable the near-complete assembly of each chromosome - also known as telomere-to-telomere assembly - for many organisms. Here, we review recent progress on assembly algorithms and protocols, with a focus on how to derive near-telomere-to-telomere assemblies. We also discuss the additional developments that will be required to resolve remaining assembly gaps and to assemble non-diploid genomes.
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Affiliation(s)
- Heng Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Richard Durbin
- Department of Genetics, Cambridge University, Cambridge, UK.
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5
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Nie F, Ni P, Huang N, Zhang J, Wang Z, Xiao C, Luo F, Wang J. De novo diploid genome assembly using long noisy reads. Nat Commun 2024; 15:2964. [PMID: 38580638 PMCID: PMC10997618 DOI: 10.1038/s41467-024-47349-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/25/2024] [Indexed: 04/07/2024] Open
Abstract
The high sequencing error rate has impeded the application of long noisy reads for diploid genome assembly. Most existing assemblers failed to generate high-quality phased assemblies using long noisy reads. Here, we present PECAT, a Phased Error Correction and Assembly Tool, for reconstructing diploid genomes from long noisy reads. We design a haplotype-aware error correction method that can retain heterozygote alleles while correcting sequencing errors. We combine a corrected read SNP caller and a raw read SNP caller to further improve the identification of inconsistent overlaps in the string graph. We use a grouping method to assign reads to different haplotype groups. PECAT efficiently assembles diploid genomes using Nanopore R9, PacBio CLR or Nanopore R10 reads only. PECAT generates more contiguous haplotype-specific contigs compared to other assemblers. Especially, PECAT achieves nearly haplotype-resolved assembly on B. taurus (Bison×Simmental) using Nanopore R9 reads and phase block NG50 with 59.4/58.0 Mb for HG002 using Nanopore R10 reads.
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Affiliation(s)
- Fan Nie
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Xiangjiang Laboratory, Changsha, 410205, China
- National Center for Applied Mathematics in Hunan and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Peng Ni
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Xiangjiang Laboratory, Changsha, 410205, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Neng Huang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Xiangjiang Laboratory, Changsha, 410205, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Jun Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
- Xiangjiang Laboratory, Changsha, 410205, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China
| | - Zhenyu Wang
- Institute of Nanfan & Seed Industry, Guangdong Academy of Sciences, Guangdong, 510316, China
| | - Chuanle Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University #7 Jinsui Road, Tianhe District, Guangzhou, China.
| | - Feng Luo
- School of Computing, Clemson University, Clemson, SC, 29634-0974, USA.
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
- Xiangjiang Laboratory, Changsha, 410205, China.
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, 410083, China.
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6
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Darian JC, Kundu R, Rajaby R, Sung WK. Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly. Nat Methods 2024; 21:574-583. [PMID: 38459383 DOI: 10.1038/s41592-023-02141-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 11/30/2023] [Indexed: 03/10/2024]
Abstract
Draft genomes generated from Oxford Nanopore Technologies (ONT) long reads are known to have a higher error rate. Although existing genome polishers can enhance their quality, the error rate (including mismatches, indels and switching errors between paternal and maternal haplotypes) can be significant. Here, we develop two polishers, hypo-short and hypo-hybrid to address this issue. Hypo-short utilizes Illumina short reads to polish an ONT-based draft assembly, resulting in a high-quality assembly with low error rates and switching errors. Expanding on this, hypo-hybrid incorporates ONT long reads to further refine the assembly into a diploid representation. Leveraging on hypo-hybrid, we have created a diploid genome assembly pipeline called hypo-assembler. Hypo-assembler automates the generation of highly accurate, contiguous and nearly complete diploid assemblies using ONT long reads, Illumina short reads and optionally Hi-C reads. Notably, our solution even allows for the production of telomere-to-telomere diploid genomes with additional manual steps. As a proof of concept, we successfully assembled a fully phased telomere-to-telomere diploid genome of HG00733, achieving a quality value exceeding 50.
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Affiliation(s)
| | - Ritu Kundu
- School of Computing, National University of Singapore, Singapore, Singapore
| | | | - Wing-Kin Sung
- School of Computing, National University of Singapore, Singapore, Singapore.
- Genome Institute of Singapore, Singapore, Singapore.
- Department of Chemical Pathology, The Chinese University of Hong Kong, Hong Kong, China.
- JC STEM Laboratory of Computational Genomics, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
- Hong Kong Genome Institute, Hong Kong, China.
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7
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Olbrich M, Bartels L, Wohlers I. Sequencing technologies and hardware-accelerated parallel computing transform computational genomics research. FRONTIERS IN BIOINFORMATICS 2024; 4:1384497. [PMID: 38567256 PMCID: PMC10985184 DOI: 10.3389/fbinf.2024.1384497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Michael Olbrich
- Center for Biotechnology, Khalifa University for Science and Technology, Abu Dhabi, United Arab Emirates
| | - Lennart Bartels
- Biomolecular Data Science in Pneumology, Research Center Borstel, Borstel, Germany
| | - Inken Wohlers
- Biomolecular Data Science in Pneumology, Research Center Borstel, Borstel, Germany
- University of Lübeck, Lübeck, Germany
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8
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Koren S, Bao Z, Guarracino A, Ou S, Goodwin S, Jenike KM, Lucas J, McNulty B, Park J, Rautiainen M, Rhie A, Roelofs D, Schneiders H, Vrijenhoek I, Nijbroek K, Ware D, Schatz MC, Garrison E, Huang S, McCombie WR, Miga KH, Wittenberg AH, Phillippy AM. Gapless assembly of complete human and plant chromosomes using only nanopore sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585294. [PMID: 38529488 PMCID: PMC10962732 DOI: 10.1101/2024.03.15.585294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
The combination of ultra-long Oxford Nanopore (ONT) sequencing reads with long, accurate PacBio HiFi reads has enabled the completion of a human genome and spurred similar efforts to complete the genomes of many other species. However, this approach for complete, "telomere-to-telomere" genome assembly relies on multiple sequencing platforms, limiting its accessibility. ONT "Duplex" sequencing reads, where both strands of the DNA are read to improve quality, promise high per-base accuracy. To evaluate this new data type, we generated ONT Duplex data for three widely-studied genomes: human HG002, Solanum lycopersicum Heinz 1706 (tomato), and Zea mays B73 (maize). For the diploid, heterozygous HG002 genome, we also used "Pore-C" chromatin contact mapping to completely phase the haplotypes. We found the accuracy of Duplex data to be similar to HiFi sequencing, but with read lengths tens of kilobases longer, and the Pore-C data to be compatible with existing diploid assembly algorithms. This combination of read length and accuracy enables the construction of a high-quality initial assembly, which can then be further resolved using the ultra-long reads, and finally phased into chromosome-scale haplotypes with Pore-C. The resulting assemblies have a base accuracy exceeding 99.999% (Q50) and near-perfect continuity, with most chromosomes assembled as single contigs. We conclude that ONT sequencing is a viable alternative to HiFi sequencing for de novo genome assembly, and has the potential to provide a single-instrument solution for the reconstruction of complete genomes.
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Affiliation(s)
- Sergey Koren
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Zhigui Bao
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, Tübingen, BadenWürttemberg, Germany
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
- Human Technopole, Milan, Italy
| | - Shujun Ou
- Ohio State University, Columbus, OH, USA
| | - Sara Goodwin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Katharine M. Jenike
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Julian Lucas
- University of California Santa Cruz, Santa Cruz, CA, USA
| | - Brandy McNulty
- University of California Santa Cruz, Santa Cruz, CA, USA
| | - Jimin Park
- University of California Santa Cruz, Santa Cruz, CA, USA
| | - Mikko Rautiainen
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Arang Rhie
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dick Roelofs
- KeyGene, Agro Business Park 90, 6708 PW Wageningen, Netherlands
| | | | - Ilse Vrijenhoek
- KeyGene, Agro Business Park 90, 6708 PW Wageningen, Netherlands
| | - Koen Nijbroek
- KeyGene, Agro Business Park 90, 6708 PW Wageningen, Netherlands
| | - Doreen Ware
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Michael C. Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Sanwen Huang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- State Key Laboratory of Tropical Crop Breeding, Chinese Academy of Tropical Agricultural Sciences, Haikou, Hainan, China
| | | | - Karen H. Miga
- University of California Santa Cruz, Santa Cruz, CA, USA
| | | | - Adam M. Phillippy
- Genome Informatics Section, Center for Genomics and Data Science Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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9
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Porubsky D, Eichler EE. A 25-year odyssey of genomic technology advances and structural variant discovery. Cell 2024; 187:1024-1037. [PMID: 38290514 DOI: 10.1016/j.cell.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 02/01/2024]
Abstract
This perspective focuses on advances in genome technology over the last 25 years and their impact on germline variant discovery within the field of human genetics. The field has witnessed tremendous technological advances from microarrays to short-read sequencing and now long-read sequencing. Each technology has provided genome-wide access to different classes of human genetic variation. We are now on the verge of comprehensive variant detection of all forms of variation for the first time with a single assay. We predict that this transition will further transform our understanding of human health and biology and, more importantly, provide novel insights into the dynamic mutational processes shaping our genomes.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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10
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Henglin M, Ghareghani M, Harvey W, Porubsky D, Koren S, Eichler EE, Ebert P, Marschall T. Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.15.580432. [PMID: 38529499 PMCID: PMC10962706 DOI: 10.1101/2024.02.15.580432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.
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Affiliation(s)
- Mir Henglin
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
| | - Maryam Ghareghani
- Department of Mathematics and Computer Science, Freie Universität Berlin, Germany
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - William Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Germany
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11
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Serra Mari R, Schrinner S, Finkers R, Ziegler FMR, Arens P, Schmidt MHW, Usadel B, Klau GW, Marschall T. Haplotype-resolved assembly of a tetraploid potato genome using long reads and low-depth offspring data. Genome Biol 2024; 25:26. [PMID: 38243222 PMCID: PMC10797741 DOI: 10.1186/s13059-023-03160-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/27/2023] [Indexed: 01/21/2024] Open
Abstract
Potato is one of the world's major staple crops, and like many important crop plants, it has a polyploid genome. Polyploid haplotype assembly poses a major computational challenge. We introduce a novel strategy for the assembly of polyploid genomes and present an assembly of the autotetraploid potato cultivar Altus. Our method uses low-depth sequencing data from an offspring population to achieve chromosomal clustering and haplotype phasing on the assembly graph. Our approach generates high-quality assemblies of individual chromosomes with haplotype-specific sequence resolution of whole chromosome arms and can be applied in common breeding scenarios where collections of offspring are available.
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Affiliation(s)
- Rebecca Serra Mari
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sven Schrinner
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Richard Finkers
- Gennovation B.V., Agro Business Park 10, 6708, PW, Wageningen, The Netherlands
- Plant Breeding, Wageningen University & Research, Wageningen, The Netherlands
| | - Freya Maria Rosemarie Ziegler
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Forschungszentrum Jülich, Institute of Bio and Geosciences, Bioinformatics (IBG-4), Jülich, Germany
- Bioeconomy Science Center, c/o Forschungszentrum Jülich, Jülich, Germany
- Biological Data Science, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Paul Arens
- Plant Breeding, Wageningen University & Research, Wageningen, The Netherlands
| | - Maximilian H-W Schmidt
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Forschungszentrum Jülich, Institute of Bio and Geosciences, Bioinformatics (IBG-4), Jülich, Germany
| | - Björn Usadel
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Forschungszentrum Jülich, Institute of Bio and Geosciences, Bioinformatics (IBG-4), Jülich, Germany.
- Bioeconomy Science Center, c/o Forschungszentrum Jülich, Jülich, Germany.
- Biological Data Science, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Gunnar W Klau
- Algorithmic Bioinformatics, Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Center for Digital Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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12
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Harvey WT, Ebert P, Ebler J, Audano PA, Munson KM, Hoekzema K, Porubsky D, Beck CR, Marschall T, Garimella K, Eichler EE. Whole-genome long-read sequencing downsampling and its effect on variant-calling precision and recall. Genome Res 2023; 33:2029-2040. [PMID: 38190646 PMCID: PMC10760522 DOI: 10.1101/gr.278070.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 11/03/2023] [Indexed: 01/10/2024]
Abstract
Advances in long-read sequencing (LRS) technologies continue to make whole-genome sequencing more complete, affordable, and accurate. LRS provides significant advantages over short-read sequencing approaches, including phased de novo genome assembly, access to previously excluded genomic regions, and discovery of more complex structural variants (SVs) associated with disease. Limitations remain with respect to cost, scalability, and platform-dependent read accuracy and the tradeoffs between sequence coverage and sensitivity of variant discovery are important experimental considerations for the application of LRS. We compare the genetic variant-calling precision and recall of Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) HiFi platforms over a range of sequence coverages. For read-based applications, LRS sensitivity begins to plateau around 12-fold coverage with a majority of variants called with reasonable accuracy (F1 score above 0.5), and both platforms perform well for SV detection. Genome assembly increases variant-calling precision and recall of SVs and indels in HiFi data sets with HiFi outperforming ONT in quality as measured by the F1 score of assembly-based variant call sets. While both technologies continue to evolve, our work offers guidance to design cost-effective experimental strategies that do not compromise on discovering novel biology.
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Affiliation(s)
- William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195-5065, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Peter A Audano
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195-5065, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195-5065, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195-5065, USA
| | - Christine R Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, Connecticut 06030-6403, USA
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Kiran Garimella
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195-5065, USA;
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
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13
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Chaisson MJP, Sulovari A, Valdmanis PN, Miller DE, Eichler EE. Advances in the discovery and analyses of human tandem repeats. Emerg Top Life Sci 2023; 7:361-381. [PMID: 37905568 PMCID: PMC10806765 DOI: 10.1042/etls20230074] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
Long-read sequencing platforms provide unparalleled access to the structure and composition of all classes of tandemly repeated DNA from STRs to satellite arrays. This review summarizes our current understanding of their organization within the human genome, their importance with respect to disease, as well as the advances and challenges in understanding their genetic diversity and functional effects. Novel computational methods are being developed to visualize and associate these complex patterns of human variation with disease, expression, and epigenetic differences. We predict accurate characterization of this repeat-rich form of human variation will become increasingly relevant to both basic and clinical human genetics.
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Affiliation(s)
- Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, U.S.A
- The Genomic and Epigenomic Regulation Program, USC Norris Cancer Center, University of Southern California, Los Angeles, CA 90089, U.S.A
| | - Arvis Sulovari
- Computational Biology, Cajal Neuroscience Inc, Seattle, WA 98102, U.S.A
| | - Paul N Valdmanis
- Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, U.S.A
| | - Danny E Miller
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, U.S.A
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, U.S.A
- Department of Pediatrics, University of Washington, Seattle, WA 98195, U.S.A
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, U.S.A
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, U.S.A
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14
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Jia P, Dong L, Yang X, Wang B, Bush SJ, Wang T, Lin J, Wang S, Zhao X, Xu T, Che Y, Dang N, Ren L, Zhang Y, Wang X, Liang F, Wang Y, Ruan J, Xia H, Zheng Y, Shi L, Lv Y, Wang J, Ye K. Haplotype-resolved assemblies and variant benchmark of a Chinese Quartet. Genome Biol 2023; 24:277. [PMID: 38049885 PMCID: PMC10694985 DOI: 10.1186/s13059-023-03116-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Recent state-of-the-art sequencing technologies enable the investigation of challenging regions in the human genome and expand the scope of variant benchmarking datasets. Herein, we sequence a Chinese Quartet, comprising two monozygotic twin daughters and their biological parents, using four short and long sequencing platforms (Illumina, BGI, PacBio, and Oxford Nanopore Technology). RESULTS The long reads from the monozygotic twin daughters are phased into paternal and maternal haplotypes using the parent-child genetic map and for each haplotype. We also use long reads to generate haplotype-resolved whole-genome assemblies with completeness and continuity exceeding that of GRCh38. Using this Quartet, we comprehensively catalogue the human variant landscape, generating a dataset of 3,962,453 SNVs, 886,648 indels (< 50 bp), 9726 large deletions (≥ 50 bp), 15,600 large insertions (≥ 50 bp), 40 inversions, 31 complex structural variants, and 68 de novo mutations which are shared between the monozygotic twin daughters. Variants underrepresented in previous benchmarks owing to their complexity-including those located at long repeat regions, complex structural variants, and de novo mutations-are systematically examined in this study. CONCLUSIONS In summary, this study provides high-quality haplotype-resolved assemblies and a comprehensive set of benchmarking resources for two Chinese monozygotic twin samples which, relative to existing benchmarks, offers expanded genomic coverage and insight into complex variant categories.
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Affiliation(s)
- Peng Jia
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Lianhua Dong
- National Institute of Metrology, Beijing, 100029, China
| | - Xiaofei Yang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Bo Wang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Stephen J Bush
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Tingjie Wang
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiadong Lin
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Songbo Wang
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xixi Zhao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Tun Xu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yizhuo Che
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ningxin Dang
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yujing Zhang
- National Institute of Metrology, Beijing, 100029, China
| | - Xia Wang
- National Institute of Metrology, Beijing, 100029, China
| | - Fan Liang
- GrandOmics Biosciences, Beijing, 100089, China
| | - Yang Wang
- GrandOmics Biosciences, Beijing, 100089, China
| | - Jue Ruan
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China
| | - Han Xia
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, 200438, China
| | - Yi Lv
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
| | - Jing Wang
- National Institute of Metrology, Beijing, 100029, China.
| | - Kai Ye
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
- Faculty of Science, Leiden University, Leiden, 2311EZ, The Netherlands.
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15
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Chen H, Naseri A, Zhi D. FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts. PLoS Genet 2023; 19:e1011057. [PMID: 38039339 PMCID: PMC10718418 DOI: 10.1371/journal.pgen.1011057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 12/13/2023] [Accepted: 11/07/2023] [Indexed: 12/03/2023] Open
Abstract
Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.
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Affiliation(s)
- Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Ardalan Naseri
- Center for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Degui Zhi
- Center for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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16
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Kang X, Xu J, Luo X, Schönhuth A. Hybrid-hybrid correction of errors in long reads with HERO. Genome Biol 2023; 24:275. [PMID: 38041098 PMCID: PMC10690975 DOI: 10.1186/s13059-023-03112-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023] Open
Abstract
Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first "hybrid-hybrid" approach, to make use of both de Bruijn graphs and overlap graphs for optimal catering to the particular strengths of NGS and TGS reads. Extensive benchmarking experiments demonstrate that HERO improves indel and mismatch error rates by on average 65% (27[Formula: see text]95%) and 20% (4[Formula: see text]61%). Using HERO prior to genome assembly significantly improves the assemblies in the majority of the relevant categories.
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Affiliation(s)
- Xiongbin Kang
- College of Biology, Hunan University, Changsha, China
- Genome Data Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Jialu Xu
- College of Biology, Hunan University, Changsha, China
| | - Xiao Luo
- College of Biology, Hunan University, Changsha, China.
| | - Alexander Schönhuth
- Genome Data Science, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
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17
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Miga KH, Eichler EE. Envisioning a new era: Complete genetic information from routine, telomere-to-telomere genomes. Am J Hum Genet 2023; 110:1832-1840. [PMID: 37922882 PMCID: PMC10645551 DOI: 10.1016/j.ajhg.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 11/07/2023] Open
Abstract
Advances in long-read sequencing and assembly now mean that individual labs can generate phased genomes that are more accurate and more contiguous than the original human reference genome. With declining costs and increasing democratization of technology, we suggest that complete genome assemblies, where both parental haplotypes are phased telomere to telomere, will become standard in human genetics. Soon, even in clinical settings where rigorous sample-handling standards must be met, affected individuals could have reference-grade genomes fully sequenced and assembled in just a few hours given advances in technology, computational processing, and annotation. Complete genetic variant discovery will transform how we map, catalog, and associate variation with human disease and fundamentally change our understanding of the genetic diversity of all humans.
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Affiliation(s)
- Karen H Miga
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA.
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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18
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Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. Bioinformatics 2023; 39:btad633. [PMID: 37851409 PMCID: PMC10628386 DOI: 10.1093/bioinformatics/btad633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/23/2023] [Accepted: 10/17/2023] [Indexed: 10/19/2023] Open
Abstract
SUMMARY Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardized workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalization module, integration of the single-cell nucleosome occupancy and genetic variation analysis SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single-cell genomics, toward production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data. AVAILABILITY AND IMPLEMENTATION MosaiCatcher v2 is a standardized workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.
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Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
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19
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Rautiainen M, Nurk S, Walenz BP, Logsdon GA, Porubsky D, Rhie A, Eichler EE, Phillippy AM, Koren S. Telomere-to-telomere assembly of diploid chromosomes with Verkko. Nat Biotechnol 2023; 41:1474-1482. [PMID: 36797493 PMCID: PMC10427740 DOI: 10.1038/s41587-023-01662-6] [Citation(s) in RCA: 68] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023]
Abstract
The Telomere-to-Telomere consortium recently assembled the first truly complete sequence of a human genome. To resolve the most complex repeats, this project relied on manual integration of ultra-long Oxford Nanopore sequencing reads with a high-resolution assembly graph built from long, accurate PacBio high-fidelity reads. We have improved and automated this strategy in Verkko, an iterative, graph-based pipeline for assembling complete, diploid genomes. Verkko begins with a multiplex de Bruijn graph built from long, accurate reads and progressively simplifies this graph by integrating ultra-long reads and haplotype-specific markers. The result is a phased, diploid assembly of both haplotypes, with many chromosomes automatically assembled from telomere to telomere. Running Verkko on the HG002 human genome resulted in 20 of 46 diploid chromosomes assembled without gaps at 99.9997% accuracy. The complete assembly of diploid genomes is a critical step towards the construction of comprehensive pangenome databases and chromosome-scale comparative genomics.
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Affiliation(s)
- Mikko Rautiainen
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sergey Nurk
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Oxford Nanopore Technologies, Oxford, UK
| | - Brian P Walenz
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
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20
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Pollen AA, Kilik U, Lowe CB, Camp JG. Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution. Nat Rev Genet 2023; 24:687-711. [PMID: 36737647 PMCID: PMC9897628 DOI: 10.1038/s41576-022-00568-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 02/05/2023]
Abstract
Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these changes will provide insights into how we became human. Connecting human-specific genetic changes to species differences has been challenging owing to an abundance of low-effect size genetic changes, limited descriptions of phenotypic differences across development at the level of cell types and lack of experimental models. Emerging approaches for single-cell sequencing, genetic manipulation and stem cell culture now support descriptive and functional studies in defined cell types with a human or ape genetic background. In this Review, we describe how the sequencing of genomes from modern and archaic hominins, great apes and other primates is revealing human-specific genetic changes and how new molecular and cellular approaches - including cell atlases and organoids - are enabling exploration of the candidate causal factors that underlie human-specific traits.
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Affiliation(s)
- Alex A Pollen
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
| | - Umut Kilik
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Craig B Lowe
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
| | - J Gray Camp
- Institute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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21
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Bonnet K, Marschall T, Doerr D. Constructing founder sets under allelic and non-allelic homologous recombination. Algorithms Mol Biol 2023; 18:15. [PMID: 37775806 PMCID: PMC10543304 DOI: 10.1186/s13015-023-00241-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/23/2023] [Indexed: 10/01/2023] Open
Abstract
Homologous recombination between the maternal and paternal copies of a chromosome is a key mechanism for human inheritance and shapes population genetic properties of our species. However, a similar mechanism can also act between different copies of the same sequence, then called non-allelic homologous recombination (NAHR). This process can result in genomic rearrangements-including deletion, duplication, and inversion-and is underlying many genomic disorders. Despite its importance for genome evolution and disease, there is a lack of computational models to study genomic loci prone to NAHR. In this work, we propose such a computational model, providing a unified framework for both (allelic) homologous recombination and NAHR. Our model represents a set of genomes as a graph, where haplotypes correspond to walks through this graph. We formulate two founder set problems under our recombination model, provide flow-based algorithms for their solution, describe exact methods to characterize the number of recombinations, and demonstrate scalability to problem instances arising in practice.
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Affiliation(s)
- Konstantinn Bonnet
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, and Center for Digital Medicine, Heinrich Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, and Center for Digital Medicine, Heinrich Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany.
| | - Daniel Doerr
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, and Center for Digital Medicine, Heinrich Heine University, Moorenstr. 5, 40225, Düsseldorf, Germany.
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22
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Wang J, Veldsman WP, Fang X, Huang Y, Xie X, Lyu A, Zhang L. Benchmarking multi-platform sequencing technologies for human genome assembly. Brief Bioinform 2023; 24:bbad300. [PMID: 37594299 DOI: 10.1093/bib/bbad300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 07/12/2023] [Accepted: 07/26/2023] [Indexed: 08/19/2023] Open
Abstract
Genome assembly is a computational technique that involves piecing together deoxyribonucleic acid (DNA) fragments generated by sequencing technologies to create a comprehensive and precise representation of the entire genome. Generating a high-quality human reference genome is a crucial prerequisite for comprehending human biology, and it is also vital for downstream genomic variation analysis. Many efforts have been made over the past few decades to create a complete and gapless reference genome for humans by using a diverse range of advanced sequencing technologies. Several available tools are aimed at enhancing the quality of haploid and diploid human genome assemblies, which include contig assembly, polishing of contig errors, scaffolding and variant phasing. Selecting the appropriate tools and technologies remains a daunting task despite several studies have investigated the pros and cons of different assembly strategies. The goal of this paper was to benchmark various strategies for human genome assembly by combining sequencing technologies and tools on two publicly available samples (NA12878 and NA24385) from Genome in a Bottle. We then compared their performances in terms of continuity, accuracy, completeness, variant calling and phasing. We observed that PacBio HiFi long-reads are the optimal choice for generating an assembly with low base errors. On the other hand, we were able to produce the most continuous contigs with Oxford Nanopore long-reads, but they may require further polishing to improve on quality. We recommend using short-reads rather than long-reads themselves to improve the base accuracy of contigs from Oxford Nanopore long-reads. Hi-C is the best choice for chromosome-level scaffolding because it can capture the longest-range DNA connectedness compared to 10× linked-reads and Bionano optical maps. However, a combination of multiple technologies can be used to further improve the quality and completeness of genome assembly. For diploid assembly, hifiasm is the best tool for human diploid genome assembly using PacBio HiFi and Hi-C data. Looking to the future, we expect that further advancements in human diploid assemblers will leverage the power of PacBio HiFi reads and other technologies with long-range DNA connectedness to enable the generation of high-quality, chromosome-level and haplotype-resolved human genome assemblies.
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Affiliation(s)
- Jingjing Wang
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Werner Pieter Veldsman
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | | | | | | | - Aiping Lyu
- School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Lu Zhang
- Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
- Institute for Research and Continuing Education, Hong Kong Baptist University, Shenzhen, China
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23
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Xie X, Sun X, Wang Y, Lehner B, Li X. Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles. Nat Commun 2023; 14:5551. [PMID: 37689712 PMCID: PMC10492795 DOI: 10.1038/s41467-023-41188-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/25/2023] [Indexed: 09/11/2023] Open
Abstract
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
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Affiliation(s)
- Xuan Xie
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
| | - Xia Sun
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Yuheng Wang
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain.
- ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
| | - Xianghua Li
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Biomedical and Health Translational Centre of Zhejiang Province, Haizhou East Road 718, Haining, 314400, P. R. China.
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24
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van Dijk EL, Naquin D, Gorrichon K, Jaszczyszyn Y, Ouazahrou R, Thermes C, Hernandez C. Genomics in the long-read sequencing era. Trends Genet 2023; 39:649-671. [PMID: 37230864 DOI: 10.1016/j.tig.2023.04.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/27/2023]
Abstract
Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.
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Affiliation(s)
- Erwin L van Dijk
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France.
| | - Delphine Naquin
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Kévin Gorrichon
- National Center of Human Genomics Research (CNRGH), 91000 Évry-Courcouronnes, France
| | - Yan Jaszczyszyn
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Rania Ouazahrou
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Claude Thermes
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
| | - Céline Hernandez
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198, Gif-sur-Yvette, France
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25
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Hård J, Mold JE, Eisfeldt J, Tellgren-Roth C, Häggqvist S, Bunikis I, Contreras-Lopez O, Chin CS, Nordlund J, Rubin CJ, Feuk L, Michaëlsson J, Ameur A. Long-read whole-genome analysis of human single cells. Nat Commun 2023; 14:5164. [PMID: 37620373 PMCID: PMC10449900 DOI: 10.1038/s41467-023-40898-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Long-read sequencing has dramatically increased our understanding of human genome variation. Here, we demonstrate that long-read technology can give new insights into the genomic architecture of individual cells. Clonally expanded CD8+ T-cells from a human donor were subjected to droplet-based multiple displacement amplification (dMDA) to generate long molecules with reduced bias. PacBio sequencing generated up to 40% genome coverage per single-cell, enabling detection of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, also in regions inaccessible by short reads. 28 somatic SNVs were detected, including one case of mitochondrial heteroplasmy. 5473 high-confidence SVs/cell were discovered, a sixteen-fold increase compared to Illumina-based results from clonally related cells. Single-cell de novo assembly generated a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, our work shows the promise of long-read sequencing toward characterization of the full spectrum of genetic variation in single cells.
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Affiliation(s)
- Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- ETH AI Center, ETH Zurich, Zurich, Switzerland.
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jesper Eisfeldt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Christian Tellgren-Roth
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Susana Häggqvist
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ignas Bunikis
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | | | - Jessica Nordlund
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Carl-Johan Rubin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adam Ameur
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
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26
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Li H, Durbin R. Genome assembly in the telomere-to-telomere era. ARXIV 2023:arXiv:2308.07877v1. [PMID: 37645045 PMCID: PMC10462168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
De novo assembly is the process of reconstructing the genome sequence of an organism from sequencing reads. Genome sequences are essential to biology, and assembly has been a central problem in bioinformatics for four decades. Until recently, genomes were typically assembled into fragments of a few megabases at best but technological advances in long-read sequencing now enable near complete chromosome-level assembly, also known as telomere-to-telomere assembly, for many organisms. Here we review recent progress on assembly algorithms and protocols. We focus on how to derive near telomere-to-telomere assemblies and discuss potential future developments.
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Affiliation(s)
- Heng Li
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Richard Durbin
- Department of Genetics, Cambridge University, Cambridge, UK
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27
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Weber T, Cosenza MR, Korbel J. MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548805. [PMID: 37503087 PMCID: PMC10370012 DOI: 10.1101/2023.07.13.548805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardised workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalisation module, integration of the scNOVA SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enables reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single cell genomics, towards production environments.
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Affiliation(s)
- Thomas Weber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | | | - Jan Korbel
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Bridging Research Division on Mechanisms of Genomic Variation and Data Science, German Cancer Research Center (DKFZ), Heidelberg, Germany
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28
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Laufer VA, Glover TW, Wilson TE. Applications of advanced technologies for detecting genomic structural variation. MUTATION RESEARCH. REVIEWS IN MUTATION RESEARCH 2023; 792:108475. [PMID: 37931775 PMCID: PMC10792551 DOI: 10.1016/j.mrrev.2023.108475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 09/07/2023] [Accepted: 11/02/2023] [Indexed: 11/08/2023]
Abstract
Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.
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Affiliation(s)
- Vincent A Laufer
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas W Glover
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
| | - Thomas E Wilson
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
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29
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Kosugi S, Kamatani Y, Harada K, Tomizuka K, Momozawa Y, Morisaki T, Terao C. Detection of trait-associated structural variations using short-read sequencing. CELL GENOMICS 2023; 3:100328. [PMID: 37388916 PMCID: PMC10300613 DOI: 10.1016/j.xgen.2023.100328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 02/17/2023] [Accepted: 04/25/2023] [Indexed: 07/01/2023]
Abstract
Genomic structural variation (SV) affects genetic and phenotypic characteristics in diverse organisms, but the lack of reliable methods to detect SV has hindered genetic analysis. We developed a computational algorithm (MOPline) that includes missing call recovery combined with high-confidence SV call selection and genotyping using short-read whole-genome sequencing (WGS) data. Using 3,672 high-coverage WGS datasets, MOPline stably detected ∼16,000 SVs per individual, which is over ∼1.7-3.3-fold higher than previous large-scale projects while exhibiting a comparable level of statistical quality metrics. We imputed SVs from 181,622 Japanese individuals for 42 diseases and 60 quantitative traits. A genome-wide association study with the imputed SVs revealed 41 top-ranked or nearly top-ranked genome-wide significant SVs, including 8 exonic SVs with 5 novel associations and enriched mobile element insertions. This study demonstrates that short-read WGS data can be used to identify rare and common SVs associated with a variety of traits.
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Affiliation(s)
- Shunichi Kosugi
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
| | - Yoichiro Kamatani
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba 277-8562, Japan
| | - Katsutoshi Harada
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa 230-0045, Japan
| | - Takayuki Morisaki
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, 4-6-1, Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan
| | | | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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30
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Mastrorosa FK, Miller DE, Eichler EE. Applications of long-read sequencing to Mendelian genetics. Genome Med 2023; 15:42. [PMID: 37316925 DOI: 10.1186/s13073-023-01194-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 05/18/2023] [Indexed: 06/16/2023] Open
Abstract
Advances in clinical genetic testing, including the introduction of exome sequencing, have uncovered the molecular etiology for many rare and previously unsolved genetic disorders, yet more than half of individuals with a suspected genetic disorder remain unsolved after complete clinical evaluation. A precise genetic diagnosis may guide clinical treatment plans, allow families to make informed care decisions, and permit individuals to participate in N-of-1 trials; thus, there is high interest in developing new tools and techniques to increase the solve rate. Long-read sequencing (LRS) is a promising technology for both increasing the solve rate and decreasing the amount of time required to make a precise genetic diagnosis. Here, we summarize current LRS technologies, give examples of how they have been used to evaluate complex genetic variation and identify missing variants, and discuss future clinical applications of LRS. As costs continue to decrease, LRS will find additional utility in the clinical space fundamentally changing how pathological variants are discovered and eventually acting as a single-data source that can be interrogated multiple times for clinical service.
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Affiliation(s)
| | - Danny E Miller
- Division of Genetic Medicine, Department of Pediatrics, University of Washington and Seattle Children's Hospital, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA.
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31
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Nakamichi K, Van Gelder RN, Chao JR, Mustafi D. Targeted adaptive long-read sequencing for discovery of complex phased variants in inherited retinal disease patients. Sci Rep 2023; 13:8535. [PMID: 37237007 PMCID: PMC10219926 DOI: 10.1038/s41598-023-35791-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/24/2023] [Indexed: 05/28/2023] Open
Abstract
Inherited retinal degenerations (IRDs) are a heterogeneous group of predominantly monogenic disorders with over 300 causative genes identified. Short-read exome sequencing is commonly used to genotypically diagnose patients with clinical features of IRDs, however, in up to 30% of patients with autosomal recessive IRDs, one or no disease-causing variants are identified. Furthermore, chromosomal maps cannot be reconstructed for allelic variant discovery with short-reads. Long-read genome sequencing can provide complete coverage of disease loci and a targeted approach can focus sequencing bandwidth to a genomic region of interest to provide increased depth and haplotype reconstruction to uncover cases of missing heritability. We demonstrate that targeted adaptive long-read sequencing on the Oxford Nanopore Technologies (ONT) platform of the USH2A gene from three probands in a family with the most common cause of the syndromic IRD, Usher Syndrome, resulted in greater than 12-fold target gene sequencing enrichment on average. This focused depth of sequencing allowed for haplotype reconstruction and phased variant identification. We further show that variants obtained from the haplotype-aware genotyping pipeline can be heuristically ranked to focus on potential pathogenic candidates without a priori knowledge of the disease-causing variants. Moreover, consideration of the variants unique to targeted long-read sequencing that are not covered by short-read technology demonstrated higher precision and F1 scores for variant discovery by long-read sequencing. This work establishes that targeted adaptive long-read sequencing can generate targeted, chromosome-phased data sets for identification of coding and non-coding disease-causing alleles in IRDs and can be applicable to other Mendelian diseases.
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Affiliation(s)
- Kenji Nakamichi
- Department of Ophthalmology, Roger and Karalis Johnson Retina Center, University of Washington, Seattle, WA, 98109, USA
| | - Russell N Van Gelder
- Department of Ophthalmology, Roger and Karalis Johnson Retina Center, University of Washington, Seattle, WA, 98109, USA
| | - Jennifer R Chao
- Department of Ophthalmology, Roger and Karalis Johnson Retina Center, University of Washington, Seattle, WA, 98109, USA
| | - Debarshi Mustafi
- Department of Ophthalmology, Roger and Karalis Johnson Retina Center, University of Washington, Seattle, WA, 98109, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, 98195, USA.
- Division of Ophthalmology, Seattle Children's Hospital, Seattle, WA, 98105, USA.
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32
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Chen NC, Kolesnikov A, Goel S, Yun T, Chang PC, Carroll A. Improving variant calling using population data and deep learning. BMC Bioinformatics 2023; 24:197. [PMID: 37173615 PMCID: PMC10182612 DOI: 10.1186/s12859-023-05294-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 04/17/2023] [Indexed: 05/15/2023] Open
Abstract
Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.
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Affiliation(s)
- Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
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33
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Harvey WT, Ebert P, Ebler J, Audano PA, Munson KM, Hoekzema K, Porubsky D, Beck CR, Marschall T, Garimella K, Eichler EE. Whole-genome long-read sequencing downsampling and its effect on variant calling precision and recall. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.04.539448. [PMID: 37205567 PMCID: PMC10187267 DOI: 10.1101/2023.05.04.539448] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Advances in long-read sequencing (LRS) technology continue to make whole-genome sequencing more complete, affordable, and accurate. LRS provides significant advantages over short-read sequencing approaches, including phased de novo genome assembly, access to previously excluded genomic regions, and discovery of more complex structural variants (SVs) associated with disease. Limitations remain with respect to cost, scalability, and platform-dependent read accuracy and the tradeoffs between sequence coverage and sensitivity of variant discovery are important experimental considerations for the application of LRS. We compare the genetic variant calling precision and recall of Oxford Nanopore Technologies (ONT) and PacBio HiFi platforms over a range of sequence coverages. For read-based applications, LRS sensitivity begins to plateau around 12-fold coverage with a majority of variants called with reasonable accuracy (F1 score above 0.5), and both platforms perform well for SV detection. Genome assembly increases variant calling precision and recall of SVs and indels in HiFi datasets with HiFi outperforming ONT in quality as measured by the F1 score of assembly-based variant callsets. While both technologies continue to evolve, our work offers guidance to design cost-effective experimental strategies that do not compromise on discovering novel biology.
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Affiliation(s)
- William T. Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Peter A. Audano
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Katherine M. Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Christine R. Beck
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT 06032 USA
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Kiran Garimella
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan E. Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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34
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Gonzalez-Garcia L, Guevara-Barrientos D, Lozano-Arce D, Gil J, Díaz-Riaño J, Duarte E, Andrade G, Bojacá JC, Hoyos-Sanchez MC, Chavarro C, Guayazan N, Chica LA, Buitrago Acosta MC, Bautista E, Trujillo M, Duitama J. New algorithms for accurate and efficient de novo genome assembly from long DNA sequencing reads. Life Sci Alliance 2023; 6:e202201719. [PMID: 36813568 PMCID: PMC9946810 DOI: 10.26508/lsa.202201719] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 02/10/2023] [Accepted: 02/13/2023] [Indexed: 02/24/2023] Open
Abstract
Building de novo genome assemblies for complex genomes is possible thanks to long-read DNA sequencing technologies. However, maximizing the quality of assemblies based on long reads is a challenging task that requires the development of specialized data analysis techniques. We present new algorithms for assembling long DNA sequencing reads from haploid and diploid organisms. The assembly algorithm builds an undirected graph with two vertices for each read based on minimizers selected by a hash function derived from the k-mer distribution. Statistics collected during the graph construction are used as features to build layout paths by selecting edges, ranked by a likelihood function. For diploid samples, we integrated a reimplementation of the ReFHap algorithm to perform molecular phasing. We ran the implemented algorithms on PacBio HiFi and Nanopore sequencing data taken from haploid and diploid samples of different species. Our algorithms showed competitive accuracy and computational efficiency, compared with other currently used software. We expect that this new development will be useful for researchers building genome assemblies for different species.
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Affiliation(s)
- Laura Gonzalez-Garcia
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | | | - Daniela Lozano-Arce
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Juanita Gil
- Department of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR, USA
| | - Jorge Díaz-Riaño
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Erick Duarte
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Germán Andrade
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Juan Camilo Bojacá
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | | | - Christian Chavarro
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Natalia Guayazan
- Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Luis Alberto Chica
- Research Group on Computational Biology and Microbial Ecology, Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
- Max Planck Tandem Group in Computational Biology, Universidad de los Andes, Bogotá, Colombia
| | | | - Edwin Bautista
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Miller Trujillo
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
| | - Jorge Duitama
- Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia
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35
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Liao WW, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, Lu S, Lucas JK, Monlong J, Abel HJ, Buonaiuto S, Chang XH, Cheng H, Chu J, Colonna V, Eizenga JM, Feng X, Fischer C, Fulton RS, Garg S, Groza C, Guarracino A, Harvey WT, Heumos S, Howe K, Jain M, Lu TY, Markello C, Martin FJ, Mitchell MW, Munson KM, Mwaniki MN, Novak AM, Olsen HE, Pesout T, Porubsky D, Prins P, Sibbesen JA, Sirén J, Tomlinson C, Villani F, Vollger MR, Antonacci-Fulton LL, Baid G, Baker CA, Belyaeva A, Billis K, Carroll A, Chang PC, Cody S, Cook DE, Cook-Deegan RM, Cornejo OE, Diekhans M, Ebert P, Fairley S, Fedrigo O, Felsenfeld AL, Formenti G, Frankish A, Gao Y, Garrison NA, Giron CG, Green RE, Haggerty L, Hoekzema K, Hourlier T, Ji HP, Kenny EE, Koenig BA, Kolesnikov A, Korbel JO, Kordosky J, Koren S, Lee H, Lewis AP, Magalhães H, Marco-Sola S, Marijon P, McCartney A, McDaniel J, Mountcastle J, Nattestad M, Nurk S, Olson ND, Popejoy AB, Puiu D, Rautiainen M, Regier AA, Rhie A, Sacco S, Sanders AD, Schneider VA, Schultz BI, Shafin K, Smith MW, Sofia HJ, Abou Tayoun AN, Thibaud-Nissen F, Tricomi FF, Wagner J, Walenz B, Wood JMD, Zimin AV, Bourque G, Chaisson MJP, Flicek P, Phillippy AM, Zook JM, Eichler EE, Haussler D, Wang T, Jarvis ED, Miga KH, Garrison E, Marschall T, Hall IM, Li H, Paten B. A draft human pangenome reference. Nature 2023; 617:312-324. [PMID: 37165242 PMCID: PMC10172123 DOI: 10.1038/s41586-023-05896-x] [Citation(s) in RCA: 182] [Impact Index Per Article: 182.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 02/28/2023] [Indexed: 05/12/2023]
Abstract
Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals1. These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.
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Affiliation(s)
- Wen-Wei Liao
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA
- Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Mobin Asri
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Daniel Doerr
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Marina Haukness
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Glenn Hickey
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Shuangjia Lu
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA
| | - Julian K Lucas
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Jean Monlong
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Haley J Abel
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Silvia Buonaiuto
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy
| | - Xian H Chang
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Haoyu Cheng
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Justin Chu
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council, Naples, Italy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jordan M Eizenga
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Xiaowen Feng
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Christian Fischer
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Robert S Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Shilpa Garg
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Copenhagen, Denmark
| | - Cristian Groza
- Quantitative Life Sciences, McGill University, Montréal, Québec, Canada
| | - Andrea Guarracino
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Kerstin Howe
- Tree of Life, Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Miten Jain
- Northeastern University, Boston, MA, USA
| | - Tsung-Yu Lu
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Charles Markello
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Fergal J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Adam M Novak
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Hugh E Olsen
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Trevor Pesout
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Pjotr Prins
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Jonas A Sibbesen
- Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
| | - Jouni Sirén
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Chad Tomlinson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Flavia Villani
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Division of Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA
| | | | | | - Carl A Baker
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Konstantinos Billis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | | | - Sarah Cody
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Robert M Cook-Deegan
- Barrett and O'Connor Washington Center, Arizona State University, Washington, DC, USA
| | - Omar E Cornejo
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Mark Diekhans
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
- Core Unit Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Olivier Fedrigo
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
| | - Adam L Felsenfeld
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Giulio Formenti
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yan Gao
- Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nanibaa' A Garrison
- Institute for Society and Genetics, College of Letters and Science, University of California, Los Angeles, CA, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Carlos Garcia Giron
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Richard E Green
- Department of Biomolecular Engineering, University of California, Santa Cruz, CA, USA
- Dovetail Genomics, Scotts Valley, CA, USA
| | - Leanne Haggerty
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Thibaut Hourlier
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara A Koenig
- Program in Bioethics and Institute for Human Genetics, University of California, San Francisco, CA, USA
| | | | - Jan O Korbel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jennifer Kordosky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Sergey Koren
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - HoJoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Hugo Magalhães
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Santiago Marco-Sola
- Computer Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
- Departament d'Arquitectura de Computadors i Sistemes Operatius, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Pierre Marijon
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany
| | - Ann McCartney
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer McDaniel
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | | | - Sergey Nurk
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Alice B Popejoy
- Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Daniela Puiu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mikko Rautiainen
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Allison A Regier
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Arang Rhie
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samuel Sacco
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA
| | - Ashley D Sanders
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Valerie A Schneider
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Baergen I Schultz
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | | | - Michael W Smith
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Heidi J Sofia
- National Institutes of Health (NIH)-National Human Genome Research Institute, Bethesda, MD, USA
| | - Ahmad N Abou Tayoun
- Al Jalila Genomics Center of Excellence, Al Jalila Children's Specialty Hospital, Dubai, UAE
- Center for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Françoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Francesca Floriana Tricomi
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Brian Walenz
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Aleksey V Zimin
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Canadian Center for Computational Genomics, McGill University, Montréal, Québec, Canada
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Mark J P Chaisson
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Adam M Phillippy
- Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - David Haussler
- Genomics Institute, University of California, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Ting Wang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Erich D Jarvis
- Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Karen H Miga
- Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Erik Garrison
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
- Center for Digital Medicine, Heinrich Heine University, Düsseldorf, Germany.
| | - Ira M Hall
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
- Center for Genomic Health, Yale University School of Medicine, New Haven, CT, USA.
| | - Heng Li
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Benedict Paten
- Genomics Institute, University of California, Santa Cruz, CA, USA.
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36
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Vollger MR, Dishuck PC, Harvey WT, DeWitt WS, Guitart X, Goldberg ME, Rozanski AN, Lucas J, Asri M, Munson KM, Lewis AP, Hoekzema K, Logsdon GA, Porubsky D, Paten B, Harris K, Hsieh P, Eichler EE. Increased mutation and gene conversion within human segmental duplications. Nature 2023; 617:325-334. [PMID: 37165237 PMCID: PMC10172114 DOI: 10.1038/s41586-023-05895-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 02/28/2023] [Indexed: 05/12/2023]
Abstract
Single-nucleotide variants (SNVs) in segmental duplications (SDs) have not been systematically assessed because of the limitations of mapping short-read sequencing data1,2. Here we constructed 1:1 unambiguous alignments spanning high-identity SDs across 102 human haplotypes and compared the pattern of SNVs between unique and duplicated regions3,4. We find that human SNVs are elevated 60% in SDs compared to unique regions and estimate that at least 23% of this increase is due to interlocus gene conversion (IGC) with up to 4.3 megabase pairs of SD sequence converted on average per human haplotype. We develop a genome-wide map of IGC donors and acceptors, including 498 acceptor and 454 donor hotspots affecting the exons of about 800 protein-coding genes. These include 171 genes that have 'relocated' on average 1.61 megabase pairs in a subset of human haplotypes. Using a coalescent framework, we show that SD regions are slightly evolutionarily older when compared to unique sequences, probably owing to IGC. SNVs in SDs, however, show a distinct mutational spectrum: a 27.1% increase in transversions that convert cytosine to guanine or the reverse across all triplet contexts and a 7.6% reduction in the frequency of CpG-associated mutations when compared to unique DNA. We reason that these distinct mutational properties help to maintain an overall higher GC content of SD DNA compared to that of unique DNA, probably driven by GC-biased conversion between paralogous sequences5,6.
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Affiliation(s)
- Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Division of Medical Genetics, University of Washington School of Medicine, Seattle, WA, USA
| | - Philip C Dishuck
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - William S DeWitt
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Xavi Guitart
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Michael E Goldberg
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Allison N Rozanski
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Julian Lucas
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mobin Asri
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Katherine M Munson
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Alexandra P Lewis
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Glennis A Logsdon
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Kelley Harris
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - PingHsun Hsieh
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
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37
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Porubsky D, Harvey WT, Rozanski AN, Ebler J, Höps W, Ashraf H, Hasenfeld P, Paten B, Sanders AD, Marschall T, Korbel JO, Eichler EE. Inversion polymorphism in a complete human genome assembly. Genome Biol 2023; 24:100. [PMID: 37122002 PMCID: PMC10150506 DOI: 10.1186/s13059-023-02919-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/31/2023] [Indexed: 05/02/2023] Open
Abstract
The telomere-to-telomere (T2T) complete human reference has significantly improved our ability to characterize genome structural variation. To understand its impact on inversion polymorphisms, we remapped data from 41 genomes against the T2T reference genome and compared it to the GRCh38 reference. We find a ~ 21% increase in sensitivity improving mapping of 63 inversions on the T2T reference. We identify 26 misorientations within GRCh38 and show that the T2T reference is three times more likely to represent the correct orientation of the major human allele. Analysis of 10 additional samples reveals novel rare inversions at chromosomes 15q25.2, 16p11.2, 16q22.1-23.1, and 22q11.21.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Allison N Rozanski
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Jana Ebler
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Wolfram Höps
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
| | - Hufsah Ashraf
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Ashley D Sanders
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Helmholtz Association, 10115, Berlin, Germany
- Berlin Institute of Health (BIH), 10178, Berlin, Germany
- Charité-Universitätsmedizin, 10117, Berlin, Germany
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, Moorenstraße 5, 40225, Düsseldorf, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Meyerhofstr. 1, 69117, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA.
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA.
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38
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Olson ND, Wagner J, Dwarshuis N, Miga KH, Sedlazeck FJ, Salit M, Zook JM. Variant calling and benchmarking in an era of complete human genome sequences. Nat Rev Genet 2023:10.1038/s41576-023-00590-0. [PMID: 37059810 DOI: 10.1038/s41576-023-00590-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2023] [Indexed: 04/16/2023]
Abstract
Genetic variant calling from DNA sequencing has enabled understanding of germline variation in hundreds of thousands of humans. Sequencing technologies and variant-calling methods have advanced rapidly, routinely providing reliable variant calls in most of the human genome. We describe how advances in long reads, deep learning, de novo assembly and pangenomes have expanded access to variant calls in increasingly challenging, repetitive genomic regions, including medically relevant regions, and how new benchmark sets and benchmarking methods illuminate their strengths and limitations. Finally, we explore the possible future of more complete characterization of human genome variation in light of the recent completion of a telomere-to-telomere human genome reference assembly and human pangenomes, and we consider the innovations needed to benchmark their newly accessible repetitive regions and complex variants.
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Affiliation(s)
- Nathan D Olson
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Justin Wagner
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nathan Dwarshuis
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Karen H Miga
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Fritz J Sedlazeck
- Baylor College of Medicine, Human Genome Sequencing Center, Houston, TX, USA
| | | | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
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39
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Porubsky D, Vollger MR, Harvey WT, Rozanski AN, Ebert P, Hickey G, Hasenfeld P, Sanders AD, Stober C, Korbel JO, Paten B, Marschall T, Eichler EE. Gaps and complex structurally variant loci in phased genome assemblies. Genome Res 2023; 33:496-510. [PMID: 37164484 PMCID: PMC10234299 DOI: 10.1101/gr.277334.122] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 12/07/2022] [Indexed: 05/12/2023]
Abstract
There has been tremendous progress in phased genome assembly production by combining long-read data with parental information or linked-read data. Nevertheless, a typical phased genome assembly generated by trio-hifiasm still generates more than 140 gaps. We perform a detailed analysis of gaps, assembly breaks, and misorientations from 182 haploid assemblies obtained from a diversity panel of 77 unique human samples. Although trio-based approaches using HiFi are the current gold standard, chromosome-wide phasing accuracy is comparable when using Strand-seq instead of parental data. Importantly, the majority of assembly gaps cluster near the largest and most identical repeats (including segmental duplications [35.4%], satellite DNA [22.3%], or regions enriched in GA/AT-rich DNA [27.4%]). Consequently, 1513 protein-coding genes overlap assembly gaps in at least one haplotype, and 231 are recurrently disrupted or missing from five or more haplotypes. Furthermore, we estimate that 6-7 Mbp of DNA are misorientated per haplotype irrespective of whether trio-free or trio-based approaches are used. Of these misorientations, 81% correspond to bona fide large inversion polymorphisms in the human species, most of which are flanked by large segmental duplications. We also identify large-scale alignment discontinuities consistent with 11.9 Mbp of deletions and 161.4 Mbp of insertions per haploid genome. Although 99% of this variation corresponds to satellite DNA, we identify 230 regions of euchromatic DNA with frequent expansions and contractions, nearly half of which overlap with 197 protein-coding genes. Such variable and incompletely assembled regions are important targets for future algorithmic development and pangenome representation.
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Affiliation(s)
- David Porubsky
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - Mitchell R Vollger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - William T Harvey
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - Allison N Rozanski
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA
| | - Peter Ebert
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Glenn Hickey
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA
| | - Patrick Hasenfeld
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Ashley D Sanders
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany
- Berlin Institute of Health (BIH), 10178 Berlin, Germany
- Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Catherine Stober
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA
| | - Tobias Marschall
- Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, 40225 Düsseldorf, Germany
- Center for Digital Medicine, Heinrich Heine University, 40225 Düsseldorf, Germany
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA;
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, USA
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Roy S, Ghosh S, Ray J, Ray K, Sengupta M. Missing heritability of Wilson disease: a search for the uncharacterized mutations. Mamm Genome 2023; 34:1-11. [PMID: 36462057 DOI: 10.1007/s00335-022-09971-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022]
Abstract
Wilson disease (WD), a copper metabolism disorder caused by mutations in ATP7B, manifests heterogeneous clinical features. Interestingly, in a fraction of clinically diagnosed WD patients, mutations in ATP7B appears to be missing. In this review we discuss the plausible explanations of this missing heritability and propose a workflow that can identify the hidden mutations. Mutation analyses of WD generally includes targeted sequencing of ATP7B exons, exon-intron boundaries, and rarely, the proximal promoter region. We propose that variants in the distal cis-regulatory elements and/or deep intronic variants that impact splicing might well represent the hidden mutations. Heterozygous del/ins that remain refractory to conventional PCR-sequencing method may also represent such mutations. In this review, we also hypothesize that mutations in the key copper metabolism genes, like, ATOX1, COMMD1, and SLC31A1, could possibly lead to a WD-like phenotype. In fact, WD does present overlapping symptoms with other rare genetic disorders; hence, the possibility of a misdiagnosis and thus adding to missing heritability cannot be excluded. In this regard, it seems that whole-genome analysis will provide a comprehensive and rapid molecular diagnosis of WD. However, considering the associated cost for such a strategy, we propose an alternative customized screening schema of WD which include targeted sequencing of ATP7B locus as well as other key copper metabolism genes. Success of such a schema has been tested in a pilot study.
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Affiliation(s)
- Shubhrajit Roy
- S. N. Pradhan Centre for Neurosciences, University of Calcutta, Kolkata, India
- Post-doctoral Fellow, Physiology Department, Johns Hopkins University, Baltimore, USA
| | - Sampurna Ghosh
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India
- Department of Biochemistry, Microbiology and Immunology, University of Saskatchewan, Saskatoon, Canada
| | - Jharna Ray
- S. N. Pradhan Centre for Neurosciences, University of Calcutta, Kolkata, India
| | - Kunal Ray
- Ramakrishna Mission Vivekananda Educational and Research Institute, Narendrapur, Kolkata, 700 103, India.
| | - Mainak Sengupta
- Department of Genetics, University of Calcutta, 35 Ballygunge Circular Road, Kolkata, 700019, India.
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41
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Behera S, LeFaive J, Orchard P, Mahmoud M, Paulin LF, Farek J, Soto DC, Parker SCJ, Smith AV, Dennis MY, Zook JM, Sedlazeck FJ. FixItFelix: improving genomic analysis by fixing reference errors. Genome Biol 2023; 24:31. [PMID: 36810122 PMCID: PMC9942314 DOI: 10.1186/s13059-023-02863-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/20/2023] [Indexed: 02/23/2023] Open
Abstract
The current version of the human reference genome, GRCh38, contains a number of errors including 1.2 Mbp of falsely duplicated and 8.04 Mbp of collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. Here, we present FixItFelix, an efficient remapping approach, together with a modified version of the GRCh38 reference genome that improves the subsequent analysis across these genes within minutes for an existing alignment file while maintaining the same coordinates. We showcase these improvements over multi-ethnic control samples, demonstrating improvements for population variant calling as well as eQTL studies.
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Affiliation(s)
- Sairam Behera
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Medhat Mahmoud
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Luis F Paulin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jesse Farek
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Daniela C Soto
- Genome Center, MIND Institute, Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
| | - Stephen C J Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Megan Y Dennis
- Genome Center, MIND Institute, Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, CA, USA
| | - Justin M Zook
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA.
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Computer Science, Rice University, Houston, TX, USA.
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Wang H, Wang LS, Schellenberg G, Lee WP. The role of structural variations in Alzheimer's disease and other neurodegenerative diseases. Front Aging Neurosci 2023; 14:1073905. [PMID: 36846102 PMCID: PMC9944073 DOI: 10.3389/fnagi.2022.1073905] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/31/2022] [Indexed: 02/10/2023] Open
Abstract
Dozens of single nucleotide polymorphisms (SNPs) related to Alzheimer's disease (AD) have been discovered by large scale genome-wide association studies (GWASs). However, only a small portion of the genetic component of AD can be explained by SNPs observed from GWAS. Structural variation (SV) can be a major contributor to the missing heritability of AD; while SV in AD remains largely unexplored as the accurate detection of SVs from the widely used array-based and short-read technology are still far from perfect. Here, we briefly summarized the strengths and weaknesses of available SV detection methods. We reviewed the current landscape of SV analysis in AD and SVs that have been found associated with AD. Particularly, the importance of currently less explored SVs, including insertions, inversions, short tandem repeats, and transposable elements in neurodegenerative diseases were highlighted.
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Affiliation(s)
- Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Gerard Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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43
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Hoehe MR, Herwig R. Analysis of 1276 Haplotype-Resolved Genomes Allows Characterization of Cis- and Trans-Abundant Genes. Methods Mol Biol 2023; 2590:237-272. [PMID: 36335503 DOI: 10.1007/978-1-0716-2819-5_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Many methods for haplotyping have materialized, but their application on a significant scale has been rare to date. Here we summarize analyses that were carried out in 1092 genomes from the 1000 Genomes Consortium and validated in an unprecedented number of 184 PGP genomes that have been experimentally haplotype-resolved by application of the Long-Fragment Read (LFR) technology. These analyses provided first insights into the diplotypic nature of human genomes and its potential functional implications. Thus, protein-changing variants were not randomly distributed between the two homologues of 18,121 autosomal protein-coding genes but occurred significantly more frequently in cis than in trans configurations in virtually each of the 1276 phased genomes. This resulted in global cis/trans ratios of ~60:40, establishing "cis abundance" as a universal characteristic of diploid human genomes. This phenomenon was based on two different classes of genes, a larger one exhibiting cis configurations of protein-changing variants in excess, so-called "cis-abundant" genes, and a smaller one of "trans-abundant" genes. These two gene classes, which together constitute a common diplotypic exome, were further functionally distinguished by means of gene ontology (GO) and pathway enrichment analysis. Moreover, they were distinguishable in terms of their effects on the human interactome, where they constitute distinct cis and trans modules, as shown with network propagation on a large integrated protein-protein interaction network. These analyses, recently performed with updated database and analysis tools, further consolidated the characterization of cis- and trans-abundant genes while expanding previous results. In this chapter, we present the key results along with the materials and methods to motivate readers to investigate these findings independently and gain further insights into the diplotypic nature of genes and genomes.
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Affiliation(s)
- Margret R Hoehe
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.
| | - Ralf Herwig
- Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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44
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Qi H, Cong R, Wang Y, Li L, Zhang G. Construction and analysis of the chromosome-level haplotype-resolved genomes of two Crassostrea oyster congeners: Crassostrea angulata and Crassostrea gigas. Gigascience 2022; 12:giad077. [PMID: 37787064 PMCID: PMC10546077 DOI: 10.1093/gigascience/giad077] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/24/2023] [Accepted: 08/30/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND The Portuguese oyster Crassostrea angulata and the Pacific oyster C. gigas are two major Crassostrea species that are naturally distributed along the Northwest Pacific coast and possess great ecological and economic value. Here, we report the construction and comparative analysis of the chromosome-level haplotype-resolved genomes of the two oyster congeners. FINDINGS Based on a trio-binning strategy, the PacBio high-fidelity and Illumina Hi-C reads of the offspring of the hybrid cross C. angulata (♂) × C. gigas (♀) were partitioned and independently assembled to construct two chromosome-level fully phased genomes. The assembly size (contig N50 size, BUSCO completeness) of the two genomes were 582.4 M (12.8 M, 99.1%) and 606.4 M (5.46 M, 98.9%) for C. angulata and C. gigas, respectively, ranking at the top of mollusk genomes with high contiguity and integrity. The general features of the two genomes were highly similar, and 15,475 highly conserved ortholog gene pairs shared identical gene structures and similar genomic locations. Highly similar sequences can be primarily identified in the coding regions, whereas most noncoding regions and introns of genes in the same ortholog group contain substantial small genomic and/or structural variations. Based on population resequencing analysis, a total of 2,756 species-specific single-nucleotide polymorphisms and 1,088 genes possibly under selection were identified. CONCLUSIONS This is the first report of trio-binned fully phased chromosome-level genomes in marine invertebrates. The study provides fundamental resources for the research on mollusk genetics, comparative genomics, and molecular evolution.
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Affiliation(s)
- Haigang Qi
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Laboratory for Marine Biology and Biotechnology, Laoshan Laboratory, Qingdao 266237, China
- National and Local Joint Engineering Key Laboratory of Ecological Mariculture, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Shandong Technology Innovation Center of Oyster Seed Industry, Qingdao 266105, China
| | - Rihao Cong
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Shandong Technology Innovation Center of Oyster Seed Industry, Qingdao 266105, China
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- The Innovation of Seed Design, Chinese Academy of Sciences, Wuhan 430072, China
| | - Yanjun Wang
- Marine Science Data Center, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
| | - Li Li
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Shandong Technology Innovation Center of Oyster Seed Industry, Qingdao 266105, China
- Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- The Innovation of Seed Design, Chinese Academy of Sciences, Wuhan 430072, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guofan Zhang
- CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Laboratory for Marine Biology and Biotechnology, Laoshan Laboratory, Qingdao 266237, China
- National and Local Joint Engineering Key Laboratory of Ecological Mariculture, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
- Shandong Technology Innovation Center of Oyster Seed Industry, Qingdao 266105, China
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Huang N, Xu M, Nie F, Ni P, Xiao CL, Luo F, Wang J. NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data. Bioinformatics 2022; 39:6957086. [PMID: 36548365 PMCID: PMC9822538 DOI: 10.1093/bioinformatics/btac824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/16/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. RESULTS We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. AVAILABILITY AND IMPLEMENTATION https://github.com/huangnengCSU/NanoSNP.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Neng Huang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Minghua Xu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Fan Nie
- School of Computer Science and Engineering, Central South University, Changsha 410083, China,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Peng Ni
- School of Computer Science and Engineering, Central South University, Changsha 410083, China,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Chuan-Le Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Feng Luo
- School of Computing, Clemson University, Clemson, SC 29634, USA
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Akbari V, Hanlon VC, O’Neill K, Lefebvre L, Schrader KA, Lansdorp PM, Jones SJ. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq. CELL GENOMICS 2022; 3:100233. [PMID: 36777186 PMCID: PMC9903809 DOI: 10.1016/j.xgen.2022.100233] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/08/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all human autosomes without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds. The parent of origin was correctly inferred for all autosomes with an average mismatch error rate of 0.31% for SNVs and 1.89% for insertions or deletions (indels). Because our method can determine whether an inherited disease allele originated from the mother or the father, we predict that it will improve the diagnosis and management of many genetic diseases.
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Affiliation(s)
- Vahid Akbari
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Kieran O’Neill
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada
| | - Louis Lefebvre
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Kasmintan A. Schrader
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada
| | - Peter M. Lansdorp
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Terry Fox Laboratory, BC Cancer, Vancouver, BC, Canada,Corresponding author
| | - Steven J.M. Jones
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada,Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada,Corresponding author
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Rabanal FA, Gräff M, Lanz C, Fritschi K, Llaca V, Lang M, Carbonell-Bejerano P, Henderson I, Weigel D. Pushing the limits of HiFi assemblies reveals centromere diversity between two Arabidopsis thaliana genomes. Nucleic Acids Res 2022; 50:12309-12327. [PMID: 36453992 PMCID: PMC9757041 DOI: 10.1093/nar/gkac1115] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/13/2022] [Accepted: 11/10/2022] [Indexed: 12/05/2022] Open
Abstract
Although long-read sequencing can often enable chromosome-level reconstruction of genomes, it is still unclear how one can routinely obtain gapless assemblies. In the model plant Arabidopsis thaliana, other than the reference accession Col-0, all other accessions de novo assembled with long-reads until now have used PacBio continuous long reads (CLR). Although these assemblies sometimes achieved chromosome-arm level contigs, they inevitably broke near the centromeres, excluding megabases of DNA from analysis in pan-genome projects. Since PacBio high-fidelity (HiFi) reads circumvent the high error rate of CLR technologies, albeit at the expense of read length, we compared a CLR assembly of accession Eyach15-2 to HiFi assemblies of the same sample. The use of five different assemblers starting from subsampled data allowed us to evaluate the impact of coverage and read length. We found that centromeres and rDNA clusters are responsible for 71% of contig breaks in the CLR scaffolds, while relatively short stretches of GA/TC repeats are at the core of >85% of the unfilled gaps in our best HiFi assemblies. Since the HiFi technology consistently enabled us to reconstruct gapless centromeres and 5S rDNA clusters, we demonstrate the value of the approach by comparing these previously inaccessible regions of the genome between the Eyach15-2 accession and the reference accession Col-0.
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Affiliation(s)
| | | | - Christa Lanz
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
| | - Katrin Fritschi
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
| | - Victor Llaca
- Genomics Technologies, Corteva Agriscience, Johnston, IA 50131, USA
| | - Michelle Lang
- Genomics Technologies, Corteva Agriscience, Johnston, IA 50131, USA
| | - Pablo Carbonell-Bejerano
- Department of Molecular Biology, Max Planck Institute for Biology Tübingen, 72076 Tübingen, Germany
| | - Ian Henderson
- Department of Plant Sciences, University of Cambridge, Cambridge, CB2 3EA, UK
| | - Detlef Weigel
- Correspondence may also be addressed to Detlef Weigel. Tel: +49 7071 601 1410;
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Jarvis ED, Formenti G, Rhie A, Guarracino A, Yang C, Wood J, Tracey A, Thibaud-Nissen F, Vollger MR, Porubsky D, Cheng H, Asri M, Logsdon GA, Carnevali P, Chaisson MJP, Chin CS, Cody S, Collins J, Ebert P, Escalona M, Fedrigo O, Fulton RS, Fulton LL, Garg S, Gerton JL, Ghurye J, Granat A, Green RE, Harvey W, Hasenfeld P, Hastie A, Haukness M, Jaeger EB, Jain M, Kirsche M, Kolmogorov M, Korbel JO, Koren S, Korlach J, Lee J, Li D, Lindsay T, Lucas J, Luo F, Marschall T, Mitchell MW, McDaniel J, Nie F, Olsen HE, Olson ND, Pesout T, Potapova T, Puiu D, Regier A, Ruan J, Salzberg SL, Sanders AD, Schatz MC, Schmitt A, Schneider VA, Selvaraj S, Shafin K, Shumate A, Stitziel NO, Stober C, Torrance J, Wagner J, Wang J, Wenger A, Xiao C, Zimin AV, Zhang G, Wang T, Li H, Garrison E, Haussler D, Hall I, Zook JM, Eichler EE, Phillippy AM, Paten B, Howe K, Miga KH. Semi-automated assembly of high-quality diploid human reference genomes. Nature 2022; 611:519-531. [PMID: 36261518 PMCID: PMC9668749 DOI: 10.1038/s41586-022-05325-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 09/06/2022] [Indexed: 01/01/2023]
Abstract
The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.
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Affiliation(s)
- Erich D. Jarvis
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA ,grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA
| | - Giulio Formenti
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA
| | - Arang Rhie
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Andrea Guarracino
- grid.510779.d0000 0004 9414 6915Genomics Research Centre, Human Technopole, Viale Rita Levi-Montalcini, Milan, Italy
| | - Chentao Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | - Jonathan Wood
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Alan Tracey
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Francoise Thibaud-Nissen
- grid.94365.3d0000 0001 2297 5165National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
| | - Mitchell R. Vollger
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - David Porubsky
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Haoyu Cheng
- grid.65499.370000 0001 2106 9910Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA USA
| | - Mobin Asri
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Glennis A. Logsdon
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Paolo Carnevali
- grid.507326.50000 0004 6090 4941Chan Zuckerberg Initiative, Redwood City, CA USA
| | - Mark J. P. Chaisson
- grid.42505.360000 0001 2156 6853Quantitative and Computational Biology, University of Southern California, Los Angeles, CA USA
| | | | - Sarah Cody
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Joanna Collins
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Peter Ebert
- grid.411327.20000 0001 2176 9917Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Merly Escalona
- grid.205975.c0000 0001 0740 6917Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA USA
| | - Olivier Fedrigo
- grid.134907.80000 0001 2166 1519Vertebrate Genome Laboratory, The Rockefeller University, New York, NY USA
| | - Robert S. Fulton
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Lucinda L. Fulton
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Shilpa Garg
- grid.5254.60000 0001 0674 042XDepartment of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jennifer L. Gerton
- grid.250820.d0000 0000 9420 1591Stowers Institute for Medical Research, Kansas City, MO USA
| | - Jay Ghurye
- grid.504403.6Dovetail Genomics, Scotts Valley, CA USA
| | | | - Richard E. Green
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - William Harvey
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Patrick Hasenfeld
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Alex Hastie
- grid.470262.50000 0004 0473 1353Bionano Genomics, San Diego, CA USA
| | - Marina Haukness
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Erich B. Jaeger
- grid.185669.50000 0004 0507 3954Illumina, Inc., San Diego, CA USA
| | - Miten Jain
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Melanie Kirsche
- grid.21107.350000 0001 2171 9311Department of Computer Science, Johns Hopkins University, Baltimore, MD USA
| | - Mikhail Kolmogorov
- grid.266100.30000 0001 2107 4242Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Sergey Koren
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Jonas Korlach
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Joyce Lee
- grid.470262.50000 0004 0473 1353Bionano Genomics, San Diego, CA USA
| | - Daofeng Li
- grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO USA
| | - Tina Lindsay
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA
| | - Julian Lucas
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Feng Luo
- grid.26090.3d0000 0001 0665 0280School of Computing, Clemson University, Clemson, SC USA
| | - Tobias Marschall
- grid.411327.20000 0001 2176 9917Institute for Medical Biometry and Bioinformatics, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Matthew W. Mitchell
- grid.282012.b0000 0004 0627 5048Coriell Institute for Medical Research, Camden, NJ USA
| | - Jennifer McDaniel
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Fan Nie
- grid.216417.70000 0001 0379 7164Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Hugh E. Olsen
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Nathan D. Olson
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Trevor Pesout
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Tamara Potapova
- grid.250820.d0000 0000 9420 1591Stowers Institute for Medical Research, Kansas City, MO USA
| | - Daniela Puiu
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Allison Regier
- grid.511991.40000 0004 4910 5831DNAnexus, Mountain View, CA USA
| | - Jue Ruan
- grid.410727.70000 0001 0526 1937Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Steven L. Salzberg
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Ashley D. Sanders
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michael C. Schatz
- grid.21107.350000 0001 2171 9311Department of Computer Science, Johns Hopkins University, Baltimore, MD USA
| | | | - Valerie A. Schneider
- grid.94365.3d0000 0001 2297 5165National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD USA
| | | | - Kishwar Shafin
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Alaina Shumate
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Nathan O. Stitziel
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Cardiovascular Division, John T. Milliken Department of Internal Medicine, Washington University School of Medicine, St. Louis, USA
| | - Catherine Stober
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - James Torrance
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Justin Wagner
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Jianxin Wang
- grid.216417.70000 0001 0379 7164Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Aaron Wenger
- grid.423340.20000 0004 0640 9878Pacific Biosciences, Menlo Park, CA USA
| | - Chuanle Xiao
- grid.12981.330000 0001 2360 039XState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Aleksey V. Zimin
- grid.21107.350000 0001 2171 9311Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Guojie Zhang
- grid.13402.340000 0004 1759 700XCenter for Evolutionary & Organismal Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Wang
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University School of Medicine, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO USA
| | - Heng Li
- grid.65499.370000 0001 2106 9910Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA USA
| | - Erik Garrison
- grid.267301.10000 0004 0386 9246Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN USA
| | - David Haussler
- grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA ,grid.205975.c0000 0001 0740 6917Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA USA
| | - Ira Hall
- grid.47100.320000000419368710Yale School of Medicine, New Haven, CT USA
| | - Justin M. Zook
- grid.94225.38000000012158463XMaterial Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD USA
| | - Evan E. Eichler
- grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA ,grid.34477.330000000122986657Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA USA
| | - Adam M. Phillippy
- grid.94365.3d0000 0001 2297 5165Genome Informatics Section, Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Benedict Paten
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
| | - Kerstin Howe
- grid.10306.340000 0004 0606 5382Tree of Life, Wellcome Sanger Institute, Cambridge, UK
| | - Karen H. Miga
- grid.205975.c0000 0001 0740 6917UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA USA
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49
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Bentz EJ, Ophir AG. Chromosome-scale genome assembly of the African giant pouched rat (Cricetomys ansorgei) and evolutionary analysis reveals evidence of olfactory specialization. Genomics 2022; 114:110521. [PMID: 36351561 DOI: 10.1016/j.ygeno.2022.110521] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/28/2022] [Accepted: 11/04/2022] [Indexed: 11/07/2022]
Abstract
The Southern giant pouched rat, Cricetomys ansorgei, is a large rodent best known for its ability to detect landmines using its impressive sense of smell. Their powerful chemosensory abilities enable subtle discrimination of chemical social signals, and female pouched rats demonstrate a unique reproductive physiology hypothesized to be mediated by pheromonal mechanisms. Thus, C. ansorgei represents a novel mammalian model for chemosensory physiology, social behavior, and pheromonal control of reproductive physiology. We present the first chromosome-scale genomic sequence of the pouched rat encoding 22,671 protein coding genes, including 1571 olfactory receptors, and provide a glance into the evolutionary history of this species. Functional enrichment analysis reveals genetic expansions specific to the pouched rat are enriched for functions related to olfactory specialization. Overall, this assembly is of reference-quality, and will serve as a useful and informative genomic sequence on which we can confidently base future molecular research involving the pouched rat.
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Affiliation(s)
- Ehren J Bentz
- Department of Psychology, Cornell University, Ithaca, NY, USA.
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50
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Lin MJ, Lin YC, Chen NC, Luo AC, Lai SK, Hsu CL, Hsu JS, Chen CY, Yang WS, Chen PL. Profiling genes encoding the adaptive immune receptor repertoire with gAIRR Suite. Front Immunol 2022; 13:922513. [PMID: 36159868 PMCID: PMC9496171 DOI: 10.3389/fimmu.2022.922513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Adaptive immune receptor repertoire (AIRR) is encoded by T cell receptor (TR) and immunoglobulin (IG) genes. Profiling these germline genes encoding AIRR (abbreviated as gAIRR) is important in understanding adaptive immune responses but is challenging due to the high genetic complexity. Our gAIRR Suite comprises three modules. gAIRR-seq, a probe capture-based targeted sequencing pipeline, profiles gAIRR from individual DNA samples. gAIRR-call and gAIRR-annotate call alleles from gAIRR-seq reads and annotate whole-genome assemblies, respectively. We gAIRR-seqed TRV and TRJ of seven Genome in a Bottle (GIAB) DNA samples with 100% accuracy and discovered novel alleles. We also gAIRR-seqed and gAIRR-called the TR and IG genes of a subject from both the peripheral blood mononuclear cells (PBMC) and oral mucosal cells. The calling results from these two cell types have a high concordance (99% for all known gAIRR alleles). We gAIRR-annotated 36 genomes to unearth 325 novel TRV alleles and 29 novel TRJ alleles. We could further profile the flanking sequences, including the recombination signal sequence (RSS). We validated two structural variants for HG002 and uncovered substantial differences of gAIRR genes in references GRCh37 and GRCh38. gAIRR Suite serves as a resource to sequence, analyze, and validate germline TR and IG genes to study various immune-related phenotypes.
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Affiliation(s)
- Mao-Jan Lin
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Yu-Chun Lin
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | - Nae-Chyun Chen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - Allen Chilun Luo
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | - Sheng-Kai Lai
- Academia Sinica and National Taiwan University, Taipei, Taiwan
| | - Chia-Lang Hsu
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Oncology, School of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
| | - Jacob Shujui Hsu
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
| | - Chien-Yu Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Wei-Shiung Yang
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Academia Sinica and National Taiwan University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Pei-Lung Chen
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, Taiwan
- Academia Sinica and National Taiwan University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
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