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Moon Y, Hong CH, Kim YH, Kim JK, Ye SH, Kang EK, Choi HW, Cho H, Choi H, Lee DE, Choi Y, Kim TM, Heo SG, Han N, Hong KM. Enhancing Clinical Applications by Evaluation of Sensitivity and Specificity in Whole Exome Sequencing. Int J Mol Sci 2024; 25:13250. [PMID: 39769013 PMCID: PMC11678496 DOI: 10.3390/ijms252413250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 12/04/2024] [Accepted: 12/06/2024] [Indexed: 01/11/2025] Open
Abstract
The cost-effectiveness of whole exome sequencing (WES) remains controversial due to variant call variability, necessitating sensitivity and specificity evaluation. WES was performed by three companies (AA, BB, and CC) using reference standards composed of DNA from hydatidiform mole and individual blood at various ratios. Sensitivity was assessed by the detection rate of null-homozygote (N-H) alleles at expected variant allelic fractions, while false positive (FP) errors were counted for unexpected alleles. Sensitivity was approximately 20% for in-house results from BB and CC and around 5% for AA. Dynamic Read Analysis for GENomics (DRAGEN) analyses identified 1.34 to 1.71 times more variants, detecting over 96% of in-house variants, with sensitivity for common variants increasing to 5%. In-house FP errors varied significantly among companies (up to 13.97 times), while DRAGEN minimized this variation. Despite DRAGEN showing higher FP errors for BB and CC, the increased sensitivity highlights the importance of effective bioinformatic conditions. We also assessed the potential effects of target enrichment and proposed optimal cutoff values for the read depth and variant allele fraction in WES. Optimizing bioinformatic analysis based on sensitivity and specificity from reference standards can enhance variant detection and improve the clinical utility of WES.
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Affiliation(s)
- Youngbeen Moon
- Bioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.M.); (J.-K.K.)
| | - Chung Hwan Hong
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (C.H.H.); (S.-H.Y.); (E.-K.K.); (H.W.C.)
| | - Young-Ho Kim
- Diagnostic and Therapeutics Technology Branch, Division of Technology Convergence, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.-H.K.); (H.C.); (H.C.)
| | - Jong-Kwang Kim
- Bioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.M.); (J.-K.K.)
| | - Seo-Hyeon Ye
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (C.H.H.); (S.-H.Y.); (E.-K.K.); (H.W.C.)
| | - Eun-Kyung Kang
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (C.H.H.); (S.-H.Y.); (E.-K.K.); (H.W.C.)
| | - Hye Won Choi
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (C.H.H.); (S.-H.Y.); (E.-K.K.); (H.W.C.)
| | - Hyeri Cho
- Diagnostic and Therapeutics Technology Branch, Division of Technology Convergence, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.-H.K.); (H.C.); (H.C.)
| | - Hana Choi
- Diagnostic and Therapeutics Technology Branch, Division of Technology Convergence, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.-H.K.); (H.C.); (H.C.)
| | - Dong-eun Lee
- Biostatistics Collaboration Team, Research Core Center, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea;
| | - Yongdoo Choi
- Division of Technology Convergence, National Cancer Center, 323 Ilsan-ro, Goyang 10408, Gyeonggi-do, Republic of Korea;
| | - Tae-Min Kim
- Department of Medical Informatics and Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Gyeonggi-do, Republic of Korea;
| | - Seong Gu Heo
- Dana Farber Cancer Institute, Boston, MA 02215, USA;
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Namshik Han
- Milner Therapeutics Institute, University of Cambridge, Cambridge CB2 0AW, UK;
- Cambridge Centre for AI in Medicine, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK
- Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| | - Kyeong-Man Hong
- Bioinformatics Analysis Team, Research Core Center, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (Y.M.); (J.-K.K.)
- Cancer Molecular Biology Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Gyeonggi-do, Republic of Korea; (C.H.H.); (S.-H.Y.); (E.-K.K.); (H.W.C.)
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Moeckel C, Mareboina M, Konnaris MA, Chan CS, Mouratidis I, Montgomery A, Chantzi N, Pavlopoulos GA, Georgakopoulos-Soares I. A survey of k-mer methods and applications in bioinformatics. Comput Struct Biotechnol J 2024; 23:2289-2303. [PMID: 38840832 PMCID: PMC11152613 DOI: 10.1016/j.csbj.2024.05.025] [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: 03/13/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/07/2024] Open
Abstract
The rapid progression of genomics and proteomics has been driven by the advent of advanced sequencing technologies, large, diverse, and readily available omics datasets, and the evolution of computational data processing capabilities. The vast amount of data generated by these advancements necessitates efficient algorithms to extract meaningful information. K-mers serve as a valuable tool when working with large sequencing datasets, offering several advantages in computational speed and memory efficiency and carrying the potential for intrinsic biological functionality. This review provides an overview of the methods, applications, and significance of k-mers in genomic and proteomic data analyses, as well as the utility of absent sequences, including nullomers and nullpeptides, in disease detection, vaccine development, therapeutics, and forensic science. Therefore, the review highlights the pivotal role of k-mers in addressing current genomic and proteomic problems and underscores their potential for future breakthroughs in research.
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Affiliation(s)
- Camille Moeckel
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Manvita Mareboina
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Maxwell A. Konnaris
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Candace S.Y. Chan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ioannis Mouratidis
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
| | - Austin Montgomery
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikol Chantzi
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | | | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Huck Institute of the Life Sciences, Penn State University, University Park, Pennsylvania, USA
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [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: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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Tang T, Liu Y, Zheng B, Li R, Zhang X, Liu Y. Integration of hybrid and self-correction method improves the quality of long-read sequencing data. Brief Funct Genomics 2024; 23:249-255. [PMID: 37340778 DOI: 10.1093/bfgp/elad026] [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: 02/16/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/22/2023] Open
Abstract
Third-generation sequencing (TGS) technologies have revolutionized genome science in the past decade. However, the long-read data produced by TGS platforms suffer from a much higher error rate than that of the previous technologies, thus complicating the downstream analysis. Several error correction tools for long-read data have been developed; these tools can be categorized into hybrid and self-correction tools. So far, these two types of tools are separately investigated, and their interplay remains understudied. Here, we integrate hybrid and self-correction methods for high-quality error correction. Our procedure leverages the inter-similarity between long-read data and high-accuracy information from short reads. We compare the performance of our method and state-of-the-art error correction tools on Escherichia coli and Arabidopsis thaliana datasets. The result shows that the integration approach outperformed the existing error correction methods and holds promise for improving the quality of downstream analyses in genomic research.
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Affiliation(s)
- Tao Tang
- School of Mordern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Qixia District, 210023, Jiangsu, China
| | - Yiping Liu
- College of Computer Science and Electronic Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
| | - Binshuang Zheng
- School of Mordern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Qixia District, 210023, Jiangsu, China
| | - Rong Li
- School of Mordern Posts, Nanjing University of Posts and Telecommunications, 9 Wenyuan Rd, Qixia District, 210023, Jiangsu, China
| | - Xiaocai Zhang
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), 138632, Singapore, Singapore
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, 2 Lushan S Rd, Yuelu District, 410086, Changsha, China
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Kallenborn F, Schmidt B. CAREx: context-aware read extension of paired-end sequencing data. BMC Bioinformatics 2024; 25:186. [PMID: 38730374 PMCID: PMC11088031 DOI: 10.1186/s12859-024-05802-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: 06/23/2023] [Accepted: 05/03/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Commonly used next generation sequencing machines typically produce large amounts of short reads of a few hundred base-pairs in length. However, many downstream applications would generally benefit from longer reads. RESULTS We present CAREx-an algorithm for the generation of pseudo-long reads from paired-end short-read Illumina data based on the concept of repeatedly computing multiple-sequence-alignments to extend a read until its partner is found. Our performance evaluation on both simulated data and real data shows that CAREx is able to connect significantly more read pairs (up to 99 % for simulated data) and to produce more error-free pseudo-long reads than previous approaches. When used prior to assembly it can achieve superior de novo assembly results. Furthermore, the GPU-accelerated version of CAREx exhibits the fastest execution times among all tested tools. CONCLUSION CAREx is a new MSA-based algorithm and software for producing pseudo-long reads from paired-end short read data. It outperforms other state-of-the-art programs in terms of (i) percentage of connected read pairs, (ii) reduction of error rates of filled gaps, (iii) runtime, and (iv) downstream analysis using de novo assembly. CAREx is open-source software written in C++ (CPU version) and in CUDA/C++ (GPU version). It is licensed under GPLv3 and can be downloaded at ( https://github.com/fkallen/CAREx ).
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Affiliation(s)
- Felix Kallenborn
- Department of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Bertil Schmidt
- Department of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
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Sami A, El-Metwally S, Rashad MZ. MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads. BMC Bioinformatics 2024; 25:61. [PMID: 38321434 PMCID: PMC10848413 DOI: 10.1186/s12859-024-05681-1] [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: 09/12/2023] [Accepted: 01/29/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. RESULTS We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. CONCLUSIONS This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.
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Affiliation(s)
- Amira Sami
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt
| | - Sara El-Metwally
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt.
- Biomedical Informatics Department, Faculty of Computer Science and Engineering, New Mansoura University, Gamasa, 35712, Egypt.
| | - M Z Rashad
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt
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Długosz M, Deorowicz S. Illumina reads correction: evaluation and improvements. Sci Rep 2024; 14:2232. [PMID: 38278837 PMCID: PMC11222498 DOI: 10.1038/s41598-024-52386-9] [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: 03/20/2023] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
The paper focuses on the correction of Illumina WGS sequencing reads. We provide an extensive evaluation of the existing correctors. To this end, we measure an impact of the correction on variant calling (VC) as well as de novo assembly. It shows, that in selected cases read correction improves the VC results quality. We also examine the algorithms behaviour in a processing of Illumina NovaSeq reads, with different reads quality characteristics than in older sequencers. We show that most of the algorithms are ready to cope with such reads. Finally, we introduce a new version of RECKONER, our read corrector, by optimizing it and equipping with a new correction strategy. Currently, RECKONER allows to correct high-coverage human reads in less than 2.5 h, is able to cope with two types of reads errors: indels and substitutions, and utilizes a new, based on a two lengths of oligomers, correction verification technique.
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Affiliation(s)
- Maciej Długosz
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100, Gliwice, Poland
| | - Sebastian Deorowicz
- Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100, Gliwice, Poland.
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Yan L, Yin Z, Zhang H, Zhao Z, Wang M, Müller A, Kallenborn F, Wichmann A, Wei Y, Niu B, Schmidt B, Liu W. RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data. Methods 2023; 216:39-50. [PMID: 37330158 DOI: 10.1016/j.ymeth.2023.06.007] [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: 03/27/2023] [Revised: 05/26/2023] [Accepted: 06/12/2023] [Indexed: 06/19/2023] Open
Abstract
Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.
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Affiliation(s)
- Lifeng Yan
- School of Software, Shandong University, Jinan, China
| | - Zekun Yin
- School of Software, Shandong University, Jinan, China.
| | - Hao Zhang
- School of Software, Shandong University, Jinan, China
| | - Zhan Zhao
- School of Software, Shandong University, Jinan, China
| | - Mingkai Wang
- School of Software, Shandong University, Jinan, China
| | - André Müller
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Felix Kallenborn
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Alexander Wichmann
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Yanjie Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Beifang Niu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
| | - Bertil Schmidt
- Institute for Computer Science, Johannes Gutenberg University, Mainz, Germany
| | - Weiguo Liu
- School of Software, Shandong University, Jinan, China
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