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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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2
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Singh G, Alser M, Denolf K, Firtina C, Khodamoradi A, Cavlak MB, Corporaal H, Mutlu O. RUBICON: a framework for designing efficient deep learning-based genomic basecallers. Genome Biol 2024; 25:49. [PMID: 38365730 PMCID: PMC10870431 DOI: 10.1186/s13059-024-03181-2] [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: 04/24/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024] Open
Abstract
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later steps in genome analysis. Therefore, there is a need to reduce the computation and memory cost of basecalling while maintaining accuracy. We present RUBICON, a framework to develop efficient hardware-optimized basecallers. We demonstrate the effectiveness of RUBICON by developing RUBICALL, the first hardware-optimized mixed-precision basecaller that performs efficient basecalling, outperforming the state-of-the-art basecallers. We believe RUBICON offers a promising path to develop future hardware-optimized basecallers.
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Affiliation(s)
- Gagandeep Singh
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
- Research and Advanced Development, AMD, Longmont, USA
| | - Mohammed Alser
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
| | | | - Can Firtina
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland.
| | | | - Meryem Banu Cavlak
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland
| | - Henk Corporaal
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Onur Mutlu
- Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland.
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3
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Yu PL, Fulton JC, Hudson OH, Huguet-Tapia JC, Brawner JT. Next-generation fungal identification using target enrichment and Nanopore sequencing. BMC Genomics 2023; 24:581. [PMID: 37784013 PMCID: PMC10544392 DOI: 10.1186/s12864-023-09691-w] [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: 05/15/2023] [Accepted: 09/21/2023] [Indexed: 10/04/2023] Open
Abstract
BACKGROUND Rapid and accurate pathogen identification is required for disease management. Compared to sequencing entire genomes, targeted sequencing may be used to direct sequencing resources to genes of interest for microbe identification and mitigate the low resolution that single-locus molecular identification provides. This work describes a broad-spectrum fungal identification tool developed to focus high-throughput Nanopore sequencing on genes commonly employed for disease diagnostics and phylogenetic inference. RESULTS Orthologs of targeted genes were extracted from 386 reference genomes of fungal species spanning six phyla to identify homologous regions that were used to design the baits used for enrichment. To reduce the cost of producing probes without diminishing the phylogenetic power, DNA sequences were first clustered, and then consensus sequences within each cluster were identified to produce 26,000 probes that targeted 114 genes. To test the efficacy of our probes, we applied the technique to three species representing Ascomycota and Basidiomycota fungi. The efficiency of enrichment, quantified as mean target coverage over the mean genome-wide coverage, ranged from 200 to 300. Furthermore, enrichment of long reads increased the depth of coverage across the targeted genes and into non-coding flanking sequence. The assemblies generated from enriched samples provided well-resolved phylogenetic trees for taxonomic assignment and molecular identification. CONCLUSIONS Our work provides data to support the utility of targeted Nanopore sequencing for fungal identification and provides a platform that may be extended for use with other phytopathogens.
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Affiliation(s)
- Pei-Ling Yu
- Department of Plant Pathology, University of Florida, Gainesville, FL, 32611, USA
| | - James C Fulton
- Department of Plant Pathology, University of Florida, Gainesville, FL, 32611, USA
- Florida Department of Agriculture and Consumer Services, Division of Plant Industry, Gainesville, FL, 32608, USA
| | - Owen H Hudson
- Department of Plant Pathology, University of Florida, Gainesville, FL, 32611, USA
| | - Jose C Huguet-Tapia
- Department of Plant Pathology, University of Florida, Gainesville, FL, 32611, USA
| | - Jeremy T Brawner
- Department of Plant Pathology, University of Florida, Gainesville, FL, 32611, USA.
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4
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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: 3] [Impact Index Per Article: 3.0] [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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5
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Kishi-Kaboshi M, Tanaka T, Sasaki K, Noda N, Aida R. Combination of long-read and short-read sequencing provides comprehensive transcriptome and new insight for Chrysanthemum morifolium ray-floret colorization. Sci Rep 2022; 12:17874. [PMID: 36284128 PMCID: PMC9596691 DOI: 10.1038/s41598-022-22589-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/17/2022] [Indexed: 01/20/2023] Open
Abstract
Chrysanthemum morifolium is one of the most popular ornamental plants globally. Owing to its large and complex genome (around 10 Gb, segmental hexaploid), it has been difficult to obtain comprehensive transcriptome, which will promote to perform new breeding technique, such as genome editing, in C. morifolium. In this study, we used single-molecule real-time (SMRT) sequencing and RNA-seq technologies, combined them with an error-correcting process, and obtained high-coverage ray-floret transcriptome. The SMRT-seq data increased the ratio of long mRNAs containing complete open-reading frames, and the combined dataset provided a more complete transcriptomic data than those produced from either SMRT-seq or RNA-seq-derived transcripts. We finally obtained 'Sei Arabella' transcripts containing 928,645 non-redundant mRNA, which showed 96.6% Benchmarking Universal Single-Copy Orthologs (BUSCO) score. We also validated the reliability of the dataset by analyzing a mapping rate, annotation and transcript expression. Using the dataset, we searched anthocyanin biosynthesis gene orthologs and performed a qRT-PCR experiment to assess the usability of the dataset. The assessment of the dataset and the following analysis indicated that our dataset is reliable and useful for molecular biology. The combination of sequencing methods provided genetic information and a way to analyze the complicated C. morifolium transcriptome.
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Affiliation(s)
- Mitsuko Kishi-Kaboshi
- grid.416835.d0000 0001 2222 0432Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization (NARO), Fujimoto 2-1, Tsukuba, Ibaraki 305-0852 Japan ,grid.416835.d0000 0001 2222 0432Present Address: Institute of Crop Science, National Agriculture and Food Research Organization (NARO), Kannondai 2-1-2, Tsukuba, Ibaraki 305-8518 Japan
| | - Tsuyoshi Tanaka
- grid.416835.d0000 0001 2222 0432Research Center for Advanced Analysis, National Agriculture and Food Research Organization (NARO), Kannondai 2-1-2, Tsukuba, Ibaraki 305-8518 Japan
| | - Katsutomo Sasaki
- grid.416835.d0000 0001 2222 0432Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization (NARO), Fujimoto 2-1, Tsukuba, Ibaraki 305-0852 Japan
| | - Naonobu Noda
- grid.416835.d0000 0001 2222 0432Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization (NARO), Fujimoto 2-1, Tsukuba, Ibaraki 305-0852 Japan
| | - Ryutaro Aida
- grid.416835.d0000 0001 2222 0432Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization (NARO), Fujimoto 2-1, Tsukuba, Ibaraki 305-0852 Japan
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6
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Genome sequence assembly algorithms and misassembly identification methods. Mol Biol Rep 2022; 49:11133-11148. [PMID: 36151399 DOI: 10.1007/s11033-022-07919-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 09/05/2022] [Indexed: 10/14/2022]
Abstract
The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.
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7
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Nanopore sequencing technology, bioinformatics and applications. Nat Biotechnol 2021; 39:1348-1365. [PMID: 34750572 PMCID: PMC8988251 DOI: 10.1038/s41587-021-01108-x] [Citation(s) in RCA: 407] [Impact Index Per Article: 135.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 09/22/2021] [Indexed: 12/13/2022]
Abstract
Rapid advances in nanopore technologies for sequencing single long DNA and RNA molecules have led to substantial improvements in accuracy, read length and throughput. These breakthroughs have required extensive development of experimental and bioinformatics methods to fully exploit nanopore long reads for investigations of genomes, transcriptomes, epigenomes and epitranscriptomes. Nanopore sequencing is being applied in genome assembly, full-length transcript detection and base modification detection and in more specialized areas, such as rapid clinical diagnoses and outbreak surveillance. Many opportunities remain for improving data quality and analytical approaches through the development of new nanopores, base-calling methods and experimental protocols tailored to particular applications.
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Music of metagenomics-a review of its applications, analysis pipeline, and associated tools. Funct Integr Genomics 2021; 22:3-26. [PMID: 34657989 DOI: 10.1007/s10142-021-00810-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/25/2021] [Accepted: 10/03/2021] [Indexed: 10/20/2022]
Abstract
This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."
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9
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Delahaye C, Nicolas J. Sequencing DNA with nanopores: Troubles and biases. PLoS One 2021; 16:e0257521. [PMID: 34597327 PMCID: PMC8486125 DOI: 10.1371/journal.pone.0257521] [Citation(s) in RCA: 161] [Impact Index Per Article: 53.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 09/06/2021] [Indexed: 12/03/2022] Open
Abstract
Oxford Nanopore Technologies' (ONT) long read sequencers offer access to longer DNA fragments than previous sequencer generations, at the cost of a higher error rate. While many papers have studied read correction methods, few have addressed the detailed characterization of observed errors, a task complicated by frequent changes in chemistry and software in ONT technology. The MinION sequencer is now more stable and this paper proposes an up-to-date view of its error landscape, using the most mature flowcell and basecaller. We studied Nanopore sequencing error biases on both bacterial and human DNA reads. We found that, although Nanopore sequencing is expected not to suffer from GC bias, it is a crucial parameter with respect to errors. In particular, low-GC reads have fewer errors than high-GC reads (about 6% and 8% respectively). The error profile for homopolymeric regions or regions with short repeats, the source of about half of all sequencing errors, also depends on the GC rate and mainly shows deletions, although there are some reads with long insertions. Another interesting finding is that the quality measure, although over-estimated, offers valuable information to predict the error rate as well as the abundance of reads. We supplemented this study with an analysis of a rapeseed RNA read set and shown a higher level of errors with a higher level of deletion in these data. Finally, we have implemented an open source pipeline for long-term monitoring of the error profile, which enables users to easily compute various analysis presented in this work, including for future developments of the sequencing device. Overall, we hope this work will provide a basis for the design of better error-correction methods.
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10
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Alser M, Rotman J, Deshpande D, Taraszka K, Shi H, Baykal PI, Yang HT, Xue V, Knyazev S, Singer BD, Balliu B, Koslicki D, Skums P, Zelikovsky A, Alkan C, Mutlu O, Mangul S. Technology dictates algorithms: recent developments in read alignment. Genome Biol 2021; 22:249. [PMID: 34446078 PMCID: PMC8390189 DOI: 10.1186/s13059-021-02443-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 07/28/2021] [Indexed: 01/08/2023] Open
Abstract
Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.
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Affiliation(s)
- Mohammed Alser
- Computer Science Department, ETH Zürich, 8092, Zürich, Switzerland
- Computer Engineering Department, Bilkent University, 06800 Bilkent, Ankara, Turkey
- Information Technology and Electrical Engineering Department, ETH Zürich, Zürich, 8092, Switzerland
| | - Jeremy Rotman
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Dhrithi Deshpande
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, 90089, USA
| | - Kodi Taraszka
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Huwenbo Shi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Pelin Icer Baykal
- Department of Computer Science, Georgia State University, Atlanta, GA, 30302, USA
| | - Harry Taegyun Yang
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Ph.D. Program, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Victor Xue
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Sergey Knyazev
- Department of Computer Science, Georgia State University, Atlanta, GA, 30302, USA
| | - Benjamin D Singer
- Division of Pulmonary and Critical Care Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
- Department of Biochemistry & Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, USA
- Simpson Querrey Institute for Epigenetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Brunilda Balliu
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - David Koslicki
- Computer Science and Engineering, Pennsylvania State University, University Park, PA, 16801, USA
- Biology Department, Pennsylvania State University, University Park, PA, 16801, USA
- The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16801, USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, GA, 30302, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, 30302, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Can Alkan
- Computer Engineering Department, Bilkent University, 06800 Bilkent, Ankara, Turkey
- Bilkent-Hacettepe Health Sciences and Technologies Program, Ankara, Turkey
| | - Onur Mutlu
- Computer Science Department, ETH Zürich, 8092, Zürich, Switzerland
- Computer Engineering Department, Bilkent University, 06800 Bilkent, Ankara, Turkey
- Information Technology and Electrical Engineering Department, ETH Zürich, Zürich, 8092, Switzerland
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, 90089, USA.
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11
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Morisse P, Marchet C, Limasset A, Lecroq T, Lefebvre A. Scalable long read self-correction and assembly polishing with multiple sequence alignment. Sci Rep 2021; 11:761. [PMID: 33436980 PMCID: PMC7804095 DOI: 10.1038/s41598-020-80757-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/22/2020] [Indexed: 11/09/2022] Open
Abstract
Third-generation sequencing technologies allow to sequence long reads of tens of kbp, that are expected to solve various problems. However, they display high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads, and allows to process a full human dataset, containing reads reaching up to 1.5 Mbp, in 10 days. Moreover, our experiments show that error correction with CONSENT improves the quality of Flye assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools, while providing comparable results. Furthermore, we show that, on a human dataset, assembling the raw data and polishing the assembly is less resource consuming than correcting and then assembling the reads, while providing better results. CONSENT is available at https://github.com/morispi/CONSENT .
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12
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Zhang H, Jain C, Aluru S. A comprehensive evaluation of long read error correction methods. BMC Genomics 2020; 21:889. [PMID: 33349243 PMCID: PMC7751105 DOI: 10.1186/s12864-020-07227-0] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 11/12/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. RESULTS In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research. CONCLUSIONS Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE .
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Affiliation(s)
- Haowen Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
| | - Chirag Jain
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA
| | - Srinivas Aluru
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332, GA, USA. .,Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, 30332, GA, USA.
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