1
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Liu Z, Xie Z, Li M. Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data. Genome Biol 2024; 25:188. [PMID: 39010145 PMCID: PMC11247875 DOI: 10.1186/s13059-024-03324-5] [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: 06/27/2023] [Accepted: 06/26/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection. RESULTS This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking . CONCLUSIONS This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.
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Affiliation(s)
- Zhi Liu
- Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Ministry of Education, Guangzhou, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China
| | - Miaoxin Li
- Program in Bioinformatics, Zhongshan School of Medicine, The Fifth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
- Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Ministry of Education, Guangzhou, China.
- Center for Precision Medicine, Sun Yat-Sen University, Guangzhou, China.
- Department of Psychiatry, The University of Hong Kong, Hong Kong, SAR, China.
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-Sen University, Zhuhai, China.
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2
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Wei ZG, Zhang XD, Fan XG, Qian Y, Liu F, Wu FX. pathMap: a path-based mapping tool for long noisy reads with high sensitivity. Brief Bioinform 2024; 25:bbae107. [PMID: 38517696 PMCID: PMC10959152 DOI: 10.1093/bib/bbae107] [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: 07/05/2023] [Revised: 12/25/2023] [Accepted: 02/28/2024] [Indexed: 03/24/2024] Open
Abstract
With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.
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Affiliation(s)
- Ze-Gang Wei
- School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
- Division of Biomedical Engineering, Department of Computer Science and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Xiao-Dan Zhang
- School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
| | - Xing-Guo Fan
- School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
| | - Yu Qian
- School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
| | - Fei Liu
- School of Physics and Opto-Electronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, Department of Computer Science and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
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3
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LoTempio J, Delot E, Vilain E. Benchmarking long-read genome sequence alignment tools for human genomics applications. PeerJ 2023; 11:e16515. [PMID: 38130927 PMCID: PMC10734412 DOI: 10.7717/peerj.16515] [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: 07/25/2023] [Accepted: 11/02/2023] [Indexed: 12/23/2023] Open
Abstract
Background The utility of long-read genome sequencing platforms has been shown in many fields including whole genome assembly, metagenomics, and amplicon sequencing. Less clear is the applicability of long reads to reference-guided human genomics, which is the foundation of genomic medicine. Here, we benchmark available platform-agnostic alignment tools on datasets from nanopore and single-molecule real-time platforms to understand their suitability in producing a genome representation. Results For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and sample NA24385 on Pacific Biosciences platforms. We employed state of the art sequence alignment tools including GraphMap2, long-read aligner (LRA), Minimap2, CoNvex Gap-cost alignMents for Long Reads (NGMLR), and Winnowmap2. Minimap2 and Winnowmap2 were computationally lightweight enough for use at scale, while GraphMap2 was not. NGMLR took a long time and required many resources, but produced alignments each time. LRA was fast, but only worked on Pacific Biosciences data. Each tool widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number of discoverable breakpoints. No alignment tool independently resolved all large structural variants (1,001-100,000 base pairs) present in the Database of Genome Variants (DGV) for sample NA12878 or the truthset for NA24385. Conclusions These results suggest a combined approach is needed for LRS alignments for human genomics. Specifically, leveraging alignments from three tools will be more effective in generating a complete picture of genomic variability. It should be best practice to use an analysis pipeline that generates alignments with both Minimap2 and Winnowmap2 as they are lightweight and yield different views of the genome. Depending on the question at hand, the data available, and the time constraints, NGMLR and LRA are good options for a third tool. If computational resources and time are not a factor for a given case or experiment, NGMLR will provide another view, and another chance to resolve a case. LRA, while fast, did not work on the nanopore data for our cluster, but PacBio results were promising in that those computations completed faster than Minimap2. Due to its significant burden on computational resources and slow run time, Graphmap2 is not an ideal tool for exploration of a whole human genome generated on a long-read sequencing platform.
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Affiliation(s)
- Jonathan LoTempio
- Institute for Clinical and Translational Science, University of California, Irvine, CA, United States of America
- International Research Laboratory (IRL2006) “Epigenetics, Data, Politics (EpiDaPo)”, Centre National de la Recherche Scientifique, Washington, DC, United States of America
| | - Emmanuele Delot
- Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC, United States of America
- Department of Genomics and Precision Medicine, George Washington University, Washington, DC, United States of America
| | - Eric Vilain
- Institute for Clinical and Translational Science, University of California, Irvine, CA, United States of America
- International Research Laboratory (IRL2006) “Epigenetics, Data, Politics (EpiDaPo)”, Centre National de la Recherche Scientifique, Washington, DC, United States of America
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4
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Wei ZG, Bu PY, Zhang XD, Liu F, Qian Y, Wu FX. invMap: a sensitive mapping tool for long noisy reads with inversion structural variants. BIOINFORMATICS (OXFORD, ENGLAND) 2023; 39:btad726. [PMID: 38058196 DOI: 10.1093/bioinformatics/btad726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/02/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023]
Abstract
MOTIVATION Longer reads produced by PacBio or Oxford Nanopore sequencers could more frequently span the breakpoints of structural variations (SVs) than shorter reads. Therefore, existing long-read mapping methods often generate wrong alignments and variant calls. Compared to deletions and insertions, inversion events are more difficult to be detected since the anchors in inversion regions are nonlinear to those in SV-free regions. To address this issue, this study presents a novel long-read mapping algorithm (named as invMap). RESULTS For each long noisy read, invMap first locates the aligned region with a specifically designed scoring method for chaining, then checks the remaining anchors in the aligned region to discover potential inversions. We benchmark invMap on simulated datasets across different genomes and sequencing coverages, experimental results demonstrate that invMap is more accurate to locate aligned regions and call SVs for inversions than the competing methods. The real human genome sequencing dataset of NA12878 illustrates that invMap can effectively find more candidate variant calls for inversions than the competing methods. AVAILABILITY AND IMPLEMENTATION The invMap software is available at https://github.com/zhang134/invMap.git.
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Affiliation(s)
- Ze-Gang Wei
- School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China
- Division of Biomedical Engineering, Department of Computer Science and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Peng-Yu Bu
- School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China
| | - Xiao-Dan Zhang
- School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China
| | - Fei Liu
- School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China
| | - Yu Qian
- School of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji 721016, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, Department of Computer Science and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
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5
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Liu Y, Shen X, Gong Y, Liu Y, Song B, Zeng X. Sequence Alignment/Map format: a comprehensive review of approaches and applications. Brief Bioinform 2023; 24:bbad320. [PMID: 37668049 DOI: 10.1093/bib/bbad320] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023] Open
Abstract
The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.
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Affiliation(s)
- Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, 410086, Changsha, China
| | - Xiangzhen Shen
- College of Computer Science and Electronic Engineering, Hunan University, 410086, Changsha, China
| | - Yongshun Gong
- School of Software, Shandong University, 250100, Jinan, China
| | - Yiping Liu
- College of Computer Science and Electronic Engineering, Hunan University, 410086, Changsha, China
| | - Bosheng Song
- College of Computer Science and Electronic Engineering, Hunan University, 410086, Changsha, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, 410086, Changsha, China
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6
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Sahlin K, Baudeau T, Cazaux B, Marchet C. A survey of mapping algorithms in the long-reads era. Genome Biol 2023; 24:133. [PMID: 37264447 PMCID: PMC10236595 DOI: 10.1186/s13059-023-02972-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 05/12/2023] [Indexed: 06/03/2023] Open
Abstract
It has been over a decade since the first publication of a method dedicated entirely to mapping long-reads. The distinctive characteristics of long reads resulted in methods moving from the seed-and-extend framework used for short reads to a seed-and-chain framework due to the seed abundance in each read. The main novelties are based on alternative seed constructs or chaining formulations. Dozens of tools now exist, whose heuristics have evolved considerably. We provide an overview of the methods used in long-read mappers. Since they are driven by implementation-specific parameters, we develop an original visualization tool to understand the parameter settings ( http://bcazaux.polytech-lille.net/Minimap2/ ).
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Affiliation(s)
- Kristoffer Sahlin
- Department of Mathematics, Science for Life Laboratory, Stockholm University, 106 91, Stockholm, Sweden.
| | - Thomas Baudeau
- Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France
| | - Bastien Cazaux
- Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France
| | - Camille Marchet
- Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000, Lille, France.
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7
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Jain C, Rhie A, Hansen NF, Koren S, Phillippy AM. Long-read mapping to repetitive reference sequences using Winnowmap2. Nat Methods 2022; 19:705-710. [PMID: 35365778 PMCID: PMC10510034 DOI: 10.1038/s41592-022-01457-8] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 03/17/2022] [Indexed: 01/10/2023]
Abstract
Approximately 5-10% of the human genome remains inaccessible due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. We show that existing long-read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a nonreference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy. To address this limitation, we developed a new long-read mapping method, Winnowmap2, by using minimal confidently alignable substrings. Winnowmap2 computes each read mapping through a collection of confident subalignments. This approach is more tolerant of structural variation and more sensitive to paralog-specific variants within repeats. Our experiments highlight that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences.
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Affiliation(s)
- Chirag Jain
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India.
- Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA.
| | - Arang Rhie
- Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA
| | - Nancy F Hansen
- Comparative Genomics Analysis Unit, National Human Genome Research Institute, Bethesda, MD, USA
| | - Sergey Koren
- Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA
| | - Adam M Phillippy
- Genome Informatics Section, National Human Genome Research Institute, Bethesda, MD, USA
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8
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Wei ZG, Fan XG, Zhang H, Zhang XD, Liu F, Qian Y, Zhang SW. kngMap: Sensitive and Fast Mapping Algorithm for Noisy Long Reads Based on the K-Mer Neighborhood Graph. Front Genet 2022; 13:890651. [PMID: 35601495 PMCID: PMC9117619 DOI: 10.3389/fgene.2022.890651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
With the rapid development of single molecular sequencing (SMS) technologies such as PacBio single-molecule real-time and Oxford Nanopore sequencing, the output read length is continuously increasing, which has dramatical potentials on cutting-edge genomic applications. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. However, these long reads contain higher sequencing errors and could more frequently span the breakpoints of structural variants (SVs) than those of shorter reads, leading to many unaligned reads or reads that are partially aligned for most state-of-the-art mappers. As a result, these methods usually focus on producing local mapping results for the query read rather than obtaining the whole end-to-end alignment. We introduce kngMap, a novel k-mer neighborhood graph-based mapper that is specifically designed to align long noisy SMS reads to a reference sequence. By benchmarking exhaustive experiments on both simulated and real-life SMS datasets to assess the performance of kngMap with ten other popular SMS mapping tools (e.g., BLASR, BWA-MEM, and minimap2), we demonstrated that kngMap has higher sensitivity that can align more reads and bases to the reference genome; meanwhile, kngMap can produce consecutive alignments for the whole read and span different categories of SVs in the reads. kngMap is implemented in C++ and supports multi-threading; the source code of kngMap can be downloaded for free at: https://github.com/zhang134/kngMap for academic usage.
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Affiliation(s)
- Ze-Gang Wei
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
| | - Xing-Guo Fan
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
| | - Hao Zhang
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
| | - Xiao-Dan Zhang
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
| | - Fei Liu
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
| | - Yu Qian
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, China
- *Correspondence: Yu Qian, ; Shao-Wu Zhang,
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Yu Qian, ; Shao-Wu Zhang,
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9
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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: 458] [Impact Index Per Article: 152.7] [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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10
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Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells. Exp Mol Med 2021; 53:1344-1355. [PMID: 34521988 PMCID: PMC8492700 DOI: 10.1038/s12276-021-00668-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/04/2021] [Accepted: 06/21/2021] [Indexed: 12/16/2022] Open
Abstract
DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance. Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.
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11
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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: 33] [Impact Index Per Article: 11.0] [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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12
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Chakraborty A, Morgenstern B, Bandyopadhyay S. S-conLSH: alignment-free gapped mapping of noisy long reads. BMC Bioinformatics 2021; 22:64. [PMID: 33573603 PMCID: PMC7879691 DOI: 10.1186/s12859-020-03918-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 12/02/2020] [Indexed: 11/16/2022] Open
Abstract
Background The advancement of SMRT technology has unfolded new opportunities of genome analysis with its longer read length and low GC bias. Alignment of the reads to their appropriate positions in the respective reference genome is the first but costliest step of any analysis pipeline based on SMRT sequencing. However, the state-of-the-art aligners often fail to identify distant homologies due to lack of conserved regions, caused by frequent genetic duplication and recombination. Therefore, we developed a novel alignment-free method of sequence mapping that is fast and accurate. Results We present a new mapper called S-conLSH that uses Spaced context based Locality Sensitive Hashing. With multiple spaced patterns, S-conLSH facilitates a gapped mapping of noisy long reads to the corresponding target locations of a reference genome. We have examined the performance of the proposed method on 5 different real and simulated datasets. S-conLSH is at least 2 times faster than the recently developed method lordFAST. It achieves a sensitivity of 99%, without using any traditional base-to-base alignment, on human simulated sequence data. By default, S-conLSH provides an alignment-free mapping in PAF format. However, it has an option of generating aligned output as SAM-file, if it is required for any downstream processing. Conclusions S-conLSH is one of the first alignment-free reference genome mapping tools achieving a high level of sensitivity. The spaced-context is especially suitable for extracting distant similarities. The variable-length spaced-seeds or patterns add flexibility to the proposed algorithm by introducing gapped mapping of the noisy long reads. Therefore, S-conLSH may be considered as a prominent direction towards alignment-free sequence analysis.
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Affiliation(s)
- Angana Chakraborty
- Department of Computer Science, West Bengal Education Service, Kolkata, India
| | - Burkhard Morgenstern
- Department of Bioinformatics (IMG), University of Göttingen, 37077, Göttingen, Germany.
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13
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Haghshenas E, Asghari H, Stoye J, Chauve C, Hach F. HASLR: Fast Hybrid Assembly of Long Reads. iScience 2020; 23:101389. [PMID: 32781410 PMCID: PMC7419660 DOI: 10.1016/j.isci.2020.101389] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/18/2019] [Accepted: 01/21/2020] [Indexed: 01/27/2023] Open
Abstract
Third-generation sequencing technologies from companies such as Oxford Nanopore and Pacific Biosciences have paved the way for building more contiguous and potentially gap-free assemblies. The larger effective length of their reads has provided a means to overcome the challenges of short to mid-range repeats. Currently, accurate long read assemblers are computationally expensive, whereas faster methods are not as accurate. Moreover, despite recent advances in third-generation sequencing, researchers still tend to generate accurate short reads for many of the analysis tasks. Here, we present HASLR, a hybrid assembler that uses error-prone long reads together with high-quality short reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on most of the samples, while being on par with other assemblers in terms of contiguity and accuracy.
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Affiliation(s)
- Ehsan Haghshenas
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada; Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
| | - Hossein Asghari
- School of Computing Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada; Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada
| | - Jens Stoye
- Faculty of Technology and Center for Biotechnology, Bielefeld University, Bielefeld, Germany
| | - Cedric Chauve
- Department of Mathematics, Simon Fraser University, Burnaby, BC V5A1S6, Canada; LaBRI, Université de Bordeaux, Bordeaux, France
| | - Faraz Hach
- Vancouver Prostate Centre, Vancouver, BC V6H3Z6, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, BC V5Z1M9, Canada.
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14
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Wei ZG, Zhang SW, Liu F. smsMap: mapping single molecule sequencing reads by locating the alignment starting positions. BMC Bioinformatics 2020; 21:341. [PMID: 32753028 PMCID: PMC7430848 DOI: 10.1186/s12859-020-03698-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 07/23/2020] [Indexed: 01/09/2023] Open
Abstract
Background Single Molecule Sequencing (SMS) technology can produce longer reads with higher sequencing error rate. Mapping these reads to a reference genome is often the most fundamental and computing-intensive step for downstream analysis. Most existing mapping tools generally adopt the traditional seed-and-extend strategy, and the candidate aligned regions for each query read are selected either by counting the number of matched seeds or chaining a group of seeds. However, for all the existing mapping tools, the coverage ratio of the alignment region to the query read is lower, and the read alignment quality and efficiency need to be improved. Here, we introduce smsMap, a novel mapping tool that is specifically designed to map the long reads of SMS to a reference genome. Results smsMap was evaluated with other existing seven SMS mapping tools (e.g., BLASR, minimap2, and BWA-MEM) on both simulated and real-life SMS datasets. The experimental results show that smsMap can efficiently achieve higher aligned read coverage ratio and has higher sensitivity that can align more sequences and bases to the reference genome. Additionally, smsMap is more robust to sequencing errors. Conclusions smsMap is computationally efficient to align SMS reads, especially for the larger size of the reference genome (e.g., H. sapiens genome with over 3 billion base pairs). The source code of smsMap can be freely downloaded from https://github.com/NWPU-903PR/smsMap.
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Affiliation(s)
- Ze-Gang Wei
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.,Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Fei Liu
- Institute of Physics and Optoelectronics Technology, Baoji University of Arts and Sciences, Baoji, 721016, China
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15
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Xin H, Shao M, Kingsford C. Context-aware seeds for read mapping. Algorithms Mol Biol 2020; 15:10. [PMID: 32489399 PMCID: PMC7245042 DOI: 10.1186/s13015-020-00172-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 05/15/2020] [Indexed: 11/10/2022] Open
Abstract
MOTIVATION Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows, this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. RESULTS We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mappings but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS significantly reduces seed frequencies when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver. AVAILABILITY https://github.com/Kingsford-Group/CAS_code.
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Affiliation(s)
- Hongyi Xin
- Computer Science Department, Carnegie Mellon University, Pittsburgh, 15213 USA
- Present Address: UM-SJTU Joint Institute, Shanghai Jiaotong University, Shanghai, 200240 China
| | - Mingfu Shao
- Department of Computer Science and Engineering, Pennsylvania State University, State College, 16801 USA
| | - Carl Kingsford
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213 USA
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O'Neill K, Brocks D, Hammell MG. Mobile genomics: tools and techniques for tackling transposons. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190345. [PMID: 32075565 PMCID: PMC7061981 DOI: 10.1098/rstb.2019.0345] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2019] [Indexed: 12/22/2022] Open
Abstract
Next-generation sequencing approaches have fundamentally changed the types of questions that can be asked about gene function and regulation. With the goal of approaching truly genome-wide quantifications of all the interaction partners and downstream effects of particular genes, these quantitative assays have allowed for an unprecedented level of detail in exploring biological interactions. However, many challenges remain in our ability to accurately describe and quantify the interactions that take place in those hard to reach and extremely repetitive regions of our genome comprised mostly of transposable elements (TEs). Tools dedicated to TE-derived sequences have lagged behind, making the inclusion of these sequences in genome-wide analyses difficult. Recent improvements, both computational and experimental, allow for the better inclusion of TE sequences in genomic assays and a renewed appreciation for the importance of TE biology. This review will discuss the recent improvements that have been made in the computational analysis of TE-derived sequences as well as the areas where such analysis still proves difficult. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.
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Affiliation(s)
- Kathryn O'Neill
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - David Brocks
- Department of Computer Science and Applied Mathematics, The Weizmann Institute of Science, Rehovot, Israel
| | - Molly Gale Hammell
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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conLSH: Context based Locality Sensitive Hashing for mapping of noisy SMRT reads. Comput Biol Chem 2020; 85:107206. [PMID: 32000034 DOI: 10.1016/j.compbiolchem.2020.107206] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 01/14/2020] [Indexed: 11/20/2022]
Abstract
Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of long and noisy reads demanding cutting edge research to get most out of it. To deal with the high error probability of SMRT data, a novel contextual Locality Sensitive Hashing (conLSH) based algorithm is proposed in this article, which can effectively align the noisy SMRT reads to the reference genome. Here, sequences are hashed together based not only on their closeness, but also on similarity of context. The algorithm has O(nρ+1) space requirement, where n is the number of sequences in the corpus and ρ is a constant. The indexing time and querying time are bounded by Onρ+1·lnnln1P2 and O(nρ) respectively, where P2 > 0, is a probability value. This algorithm is particularly useful for retrieving similar sequences, a widely used task in biology. The proposed conLSH based aligner is compared with rHAT, popularly used for aligning SMRT reads, and is found to comprehensively beat it in speed as well as in memory requirements. In particular, it takes approximately 24.2% less processing time, while saving about 70.3% in peak memory requirement for H.sapiens PacBio dataset.
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