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Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM

16 Mar 2013-arXiv: Genomics (figshare)-
TL;DR: BWA-MEM automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment, which is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases.
Abstract: Summary: BWA-MEM is a new alignment algorithm for aligning sequence reads or long query sequences against a large reference genome such as human. It automatically chooses between local and end-to-end alignments, supports paired-end reads and performs chimeric alignment. The algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70bp to a few megabases. For mapping 100bp sequences, BWA-MEM shows better performance than several state-of-art read aligners to date. Availability and implementation: BWA-MEM is implemented as a component of BWA, which is available at this http URL. Contact: hengli@broadinstitute.org
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Journal ArticleDOI
Adam Auton1, Gonçalo R. Abecasis2, David Altshuler3, Richard Durbin4  +514 moreInstitutions (90)
01 Oct 2015-Nature
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

12,661 citations


Cites background from "Aligning sequence reads, clone sequ..."

  • ...…Leader), David M. Altshuler3, Eric Banks13, Gaurav Bhatia13, Guillermo del Angel13, Stacey B. Gabriel13, Giulio Genovese13, Namrata Gupta13, Heng Li13, Seva Kashin13,40, Eric S. Lander13, Steven A. McCarroll13,40, James C. Nemesh13, Ryan E. Poplin13; Cold Spring Harbor Laboratory Seungtai C.…...

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  • ...…Robert E. Handsaker13,40 (Project Leader), David M. Altshuler3, Eric Banks13, Guillermo del Angel13, Giulio Genovese13, Chris Hartl13, Heng Li13, Seva Kashin13,40, James C. Nemesh13, Khalid Shakir13; Cold Spring Harbor Laboratory Seungtai C. Yoon42 (Principal Investigator), Jayon…...

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  • ...Garrison4 (Project Lead),Deniz Kural37,Wan-PingLee37, Wen Fung Leong38, Michael Stromberg39, Alistair N. Ward23, Jiantao Wu39, Mengyao Zhang40; Broad Institute of MIT and Harvard Mark J. Daly13 (Principal Investigator), Mark A. DePristo41 (Project Leader), Robert E. Handsaker13,40 (Project Leader), David M. Altshuler3, Eric Banks13, Gaurav Bhatia13, Guillermo del Angel13, Stacey B. Gabriel13, Giulio Genovese13, Namrata Gupta13, Heng Li13, Seva Kashin13,40, Eric S. Lander13, Steven A. McCarroll13,40, James C. Nemesh13, Ryan E. Poplin13; Cold Spring Harbor Laboratory Seungtai C. Yoon42 (Principal Investigator), Jayon Lihm42, Vladimir Makarov43; Cornell University Andrew G. Clark7 (Principal Investigator), Srikanth Gottipati44, Alon Keinan7, Juan L. Rodriguez-Flores45; European Molecular Biology Laboratory Jan O. Korbel12,17 (Principal Investigator), Tobias Rausch17,46 (Project Leader),Markus H. Fritz46, Adrian M. Stütz17; European Molecular Biology Laboratory, European Bioinformatics Institute Paul Flicek12 (Principal Investigator), Kathryn Beal12, Laura Clarke12, AvikDatta12, JavierHerrero47, William M. McLaren12, Graham R. S. Ritchie12, Richard E. Smith12, Daniel Zerbino12, Xiangqun Zheng-Bradley12; Harvard University Pardis C. Sabeti13,48 (Principal Investigator), Ilya Shlyakhter13,48, Stephen F. Schaffner13,48, Joseph Vitti13,49; Human Gene Mutation Database David N. Cooper50 (Principal Investigator), Edward V. Ball50, Peter D. Stenson50; Illumina David R. Bentley5 (Principal Investigator), Bret Barnes39, Markus Bauer5, R. Keira Cheetham5, Anthony Cox5, Michael Eberle5, Sean Humphray5, Scott Kahn39, Lisa Murray5, John Peden5, Richard Shaw5; Icahn School of Medicine at Mount Sinai Eimear E. Kenny51 (Principal Investigator); Louisiana State University Mark A. Batzer52 (Principal Investigator), Miriam K. Konkel52, Jerilyn A. Walker52; Massachusetts General Hospital Daniel G. MacArthur53 (Principal Investigator), Monkol Lek53; Max Planck Institute for Molecular Genetics Ralf Sudbrak32 (Project Leader), Vyacheslav S. Amstislavskiy20, Ralf Herwig20; McDonnell Genome Institute at Washington University Elaine R. Mardis22 (Co-Principal Investigator), Li Ding22, Daniel C. Koboldt22, David Larson22, Kai Ye22; McGill University Simon Gravel54; National Eye Institute, NIH Anand Swaroop55, EmilyChew55; New YorkGenome CenterTuuli Lappalainen56,57 (Principal Investigator), Yaniv Erlich56,58 (Principal Investigator), Melissa Gymrek13,56,59,60, Thomas Frederick Willems61; Ontario Institute for Cancer Research Jared T. Simpson62; Pennsylvania State University Mark D. Shriver63 (Principal Investigator); Rutgers Cancer Institute of New Jersey Jeffrey A. Rosenfeld64 (Principal Investigator); Stanford University Carlos D. Bustamante65 (Principal Investigator), Stephen B. Montgomery66 (Principal Investigator), Francisco M. De La Vega65 (Principal Investigator), Jake K. Byrnes67, Andrew W. Carroll68, Marianne K. DeGorter66, Phil Lacroute65, Brian K. Maples65, Alicia R. Martin65, Andres Moreno-Estrada65,69, Suyash S. Shringarpure65, Fouad Zakharia65; Tel-Aviv University Eran Halperin70,71,72 (Principal Investigator), Yael Baran70; The Jackson Laboratory for Genomic Medicine Charles Lee18,19 (Principal Investigator), Eliza Cerveira18, Jaeho Hwang18, Ankit Malhotra18 (Co-Project Lead), Dariusz Plewczynski18, Kamen Radew18, Mallory Romanovitch18, Chengsheng Zhang18 (Co-Project Lead); Thermo Fisher Scientific Fiona C. L. Hyland73; Translational Genomics Research Institute David W. Craig74 (Principal Investigator), Alexis Christoforides74, Nils Homer75, Tyler Izatt74, Ahmet A. Kurdoglu74, Shripad A. Sinari74, Kevin Squire76; US National Institutes of Health Stephen T. Sherry25 (Principal Investigator), Chunlin Xiao25; University of California, San Diego Jonathan Sebat77,78 (Principal Investigator), Danny Antaki77, Madhusudan Gujral77, Amina Noor77, Kenny Ye79; University of California, SanFrancisco Esteban G. Burchard80 (Principal Investigator), Ryan D. Hernandez80,81,82 (Principal Investigator), Christopher R. Gignoux80; University of California, Santa Cruz David Haussler83,84 (Principal Investigator), Sol J. Katzman83, W. James Kent83; University of Chicago Bryan Howie85; University College London Andres Ruiz-Linares86 (Principal Investigator); University of Geneva Emmanouil T. Dermitzakis87,88,89 (Principal Investigator); University of Maryland School of Medicine Scott E. Devine90 (Principal Investigator); University of Michigan Gonçalo R. Abecasis2 (Principal Investigator) (Co-Chair), Hyun Min Kang2 (Project Leader), Jeffrey M. Kidd91,92 (Principal Investigator), Tom Blackwell2, Sean Caron2, Wei Chen93, Sarah Emery92, Lars Fritsche2, Christian Fuchsberger2, Goo Jun2,94, Bingshan Li95, Robert Lyons96, Chris Scheller2, Carlo Sidore2,97,98, Shiya Song91, Elzbieta Sliwerska92, Daniel Taliun2, Adrian Tan2, Ryan Welch2, Mary Kate Wing2, Xiaowei Zhan99; University of Montréal Philip Awadalla62,100 (Principal Investigator), Alan Hodgkinson100; University of North Carolina at Chapel Hill Yun Li101; University of North Carolina at Charlotte Xinghua Shi102 (Principal Investigator), Andrew Quitadamo102; University of Oxford Gerton Lunter8 (Principal Investigator), Gil A. McVean8,9 (Principal Investigator) (Co-Chair), Jonathan L. Marchini8,9 (Principal Investigator), Simon Myers8,9 (Principal Investigator), Claire Churchhouse9, Olivier Delaneau9,87, Anjali Gupta-Hinch8, Warren Kretzschmar8, Zamin Iqbal8, Iain Mathieson8, Androniki Menelaou9,103, Andy Rimmer87, Dionysia K. Xifara8,9; University of Puerto Rico Taras K. Oleksyk104 (Principal Investigator); University of Texas Health Sciences Center at Houston Yunxin Fu94 (Principal Investigator), Xiaoming Liu94, Momiao Xiong94; University of Utah Lynn Jorde105 (Principal Investigator), David Witherspoon105, Jinchuan Xing106; University of Washington Evan E. Eichler10,11 (Principal Investigator), Brian L. Browning107 (Principal Investigator), Sharon R. Browning108 (Principal Investigator), Fereydoun Hormozdiari10, Peter H. Sudmant10; Weill Cornell Medical College, Ekta Khurana109 (Principal Investigator); Wellcome Trust Sanger Institute Richard M. Durbin4 (Principal Investigator), Matthew E. Hurles4 (Principal Investigator), Chris Tyler-Smith4 (Principal Investigator), Cornelis A. Albers110,111, Qasim Ayub4, Senduran Balasubramaniam4, Yuan Chen4, Vincenza Colonna4,112, Petr Danecek4, Luke Jostins8, Thomas M. Keane4, Shane McCarthy4, Klaudia Walter4, Yali Xue4; Yale University Mark B. Gerstein113,114,115 (Principal Investigator), Alexej Abyzov116, Suganthi Balasubramanian115, Jieming Chen113, Declan Clarke117, Yao Fu113, Arif O. Harmanci113, Mike Jin115, Donghoon Lee113, Jeremy Liu115, Xinmeng Jasmine Mu13,113, Jing Zhang113,115, Yan Zhang113,115 Structural variation group: BGI-Shenzhen Yingrui Li26, Ruibang Luo26, Hongmei Zhu26; Bilkent University Can Alkan36, Elif Dal36, Fatma Kahveci36; Boston College Gabor T. Marth23 (Principal Investigator), Erik P. Garrison4, Deniz Kural37, Wan-Ping Lee37, Alistair N. Ward23, Jiantao Wu23, Mengyao Zhang23; Broad Institute of MIT and Harvard Steven A. McCarroll13,40 (Principal Investigator), Robert E. Handsaker13,40 (Project Leader), David M. Altshuler3, Eric Banks13, Guillermo del Angel13, Giulio Genovese13, Chris Hartl13, Heng Li13, Seva Kashin13,40, James C. Nemesh13, Khalid Shakir13; Cold Spring Harbor Laboratory Seungtai C. Yoon42 (Principal Investigator), Jayon Lihm42, Vladimir Makarov43; Cornell University Jeremiah Degenhardt7; European Molecular Biology Laboratory Jan O. Korbel12,17 (Principal Investigator) (Co-Chair), Markus H. Fritz46, Sascha Meiers17, Benjamin Raeder17, Tobias Rausch17,46, Adrian M. Stütz17; European Molecular Biology Laboratory, European Bioinformatics Institute Paul Flicek12 (Principal Investigator), Francesco Paolo Casale12, Laura Clarke12, Richard E. Smith12, Oliver Stegle12, Xiangqun Zheng-Bradley12; Illumina David R. Bentley5 (Principal Investigator), Bret Barnes39, R. Keira Cheetham5, Michael Eberle5, Sean Humphray5, Scott Kahn39, Lisa Murray5, Richard Shaw5; Leiden University Medical Center Eric-Wubbo Lameijer118; Louisiana State University Mark A. Batzer52 (Principal Investigator), Miriam K. Konkel52, Jerilyn A. Walker52; McDonnell Genome Institute at Washington University Li Ding22 (Principal Investigator), Ira Hall22, Kai Ye22; Stanford University Phil Lacroute65; The Jackson Laboratory for Genomic Medicine Charles Lee18,19 (Principal Investigator) (Co-Chair), Eliza Cerveira18, Ankit Malhotra18, Jaeho Hwang18, Dariusz Plewczynski18, Kamen Radew18, Mallory Romanovitch18, Chengsheng Zhang18; Translational Genomics Research Institute David W. Craig74 (Principal Investigator), Nils Homer75; US National Institutes of Health Deanna Church34, Chunlin Xiao25; University of California,San Diego JonathanSebat77 (Principal Investigator),Danny Antaki77, Vineet Bafna119, Jacob Michaelson120, Kenny Ye79; University of Maryland School of Medicine Scott E. Devine90 (Principal Investigator), Eugene J. Gardner90 (Project Leader); University of Michigan Gonçalo R. Abecasis2 (Principal Investigator), Jeffrey M. Kidd91,92 (Principal Investigator), Ryan E. Mills91,92 (Principal Investigator), Gargi G2015 Macmillan Publishers Limited....

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Journal ArticleDOI
Heng Li1
TL;DR: Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database and is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mapper at higher accuracy, surpassing most aligners specialized in one type of alignment.
Abstract: Motivation Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Results Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3-4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. Availability and implementation https://github.com/lh3/minimap2. Supplementary information Supplementary data are available at Bioinformatics online.

6,264 citations


Cites background or methods from "Aligning sequence reads, clone sequ..."

  • ...Several aligners have been developed for such data (Chaisson and Tesler, 2012; Li, 2013; Liu et al., 2016; Sović et al., 2016; Liu et al., 2017; Lin and Hsu, 2017; Sedlazeck et al., 2017)....

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  • ...) sequencing technology and Oxford Nanopore technologies (ONT) produce reads over 10kbp in length at an error rate ˘15%. Several aligners have been developed for such data (Chaisson and Tesler, 2012; Li, 2013; Liu et al., 2016; Sovic et al., 2016; Liu et al., 2017; Lin and Hsu, 2017; Sedlazeck´ et al., 2017). Most of them were five times as slow as mainstream short-read aligners (Langmead and Salzberg, 201...

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  • ...7.15; Li, 2013), GraphMap (v0....

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  • ...Most of them were five times as slow as mainstream short-read aligners (Langmead and Salzberg, 2012; Li, 2013) in terms of the number of bases mapped per second....

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  • ...ses such as nt from NCBI. 3.1 Aligning long genomic reads As a sanity check, we evaluated minimap2 on simulated human reads along with BLASR (v1.MC.rc64; Chaisson and Tesler, 2012), BWA-MEM (v0.7.15; Li, 2013), GraphMap (v0.5.2; Sovic et al.,´ 2016), Kart (v2.2.5; Lin and Hsu, 2017), minialign (v0.5.3; https://github.com/ocxtal/minialign) and NGMLR (v0.2.5; Sedlazeck et al., 2017). We excluded rHAT (Liu et...

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Journal ArticleDOI
TL;DR: This work presents a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index, and uses it to represent and search an expanded model of the human reference genome.
Abstract: The human reference genome represents only a small number of individuals, which limits its usefulness for genotyping. We present a method named HISAT2 (hierarchical indexing for spliced alignment of transcripts 2) that can align both DNA and RNA sequences using a graph Ferragina Manzini index. We use HISAT2 to represent and search an expanded model of the human reference genome in which over 14.5 million genomic variants in combination with haplotypes are incorporated into the data structure used for searching and alignment. We benchmark HISAT2 using simulated and real datasets to demonstrate that our strategy of representing a population of genomes, together with a fast, memory-efficient search algorithm, provides more detailed and accurate variant analyses than other methods. We apply HISAT2 for HLA typing and DNA fingerprinting; both applications form part of the HISAT-genotype software that enables analysis of haplotype-resolved genes or genomic regions. HISAT-genotype outperforms other computational methods and matches or exceeds the performance of laboratory-based assays. A graph-based genome indexing scheme enables variant-aware alignment of sequences with very low memory requirements.

4,855 citations

Journal ArticleDOI
TL;DR: Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low.
Abstract: The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

2,245 citations

Journal ArticleDOI
05 Jan 2018-Science
TL;DR: The results suggest that the commensal microbiome may have a mechanistic impact on antitumor immunity in human cancer patients and could lead to improved tumor control, augmented T cell responses, and greater efficacy of anti–PD-L1 therapy.
Abstract: Anti–PD-1–based immunotherapy has had a major impact on cancer treatment but has only benefited a subset of patients. Among the variables that could contribute to interpatient heterogeneity is differential composition of the patients’ microbiome, which has been shown to affect antitumor immunity and immunotherapy efficacy in preclinical mouse models. We analyzed baseline stool samples from metastatic melanoma patients before immunotherapy treatment, through an integration of 16 S ribosomal RNA gene sequencing, metagenomic shotgun sequencing, and quantitative polymerase chain reaction for selected bacteria. A significant association was observed between commensal microbial composition and clinical response. Bacterial species more abundant in responders included Bifidobacterium longum , Collinsella aerofaciens , and Enterococcus faecium. Reconstitution of germ-free mice with fecal material from responding patients could lead to improved tumor control, augmented T cell responses, and greater efficacy of anti–PD-L1 therapy. Our results suggest that the commensal microbiome may have a mechanistic impact on antitumor immunity in human cancer patients.

1,820 citations

References
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Journal ArticleDOI
TL;DR: A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original.
Abstract: The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

70,111 citations

Journal ArticleDOI
TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected]

43,862 citations


"Aligning sequence reads, clone sequ..." refers background in this paper

  • ...2.2.1 Rescuing missing hitsLike BWA (Li and Durbin, 2009), BWAMEM processes a batch of reads at a time....

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Journal ArticleDOI
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

37,898 citations


"Aligning sequence reads, clone sequ..." refers methods in this paper

  • ...Bowtie2 and Cushaw2 are slower for 600bp reads (see Results)....

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  • ...In this background, a few long-read alignment algorithms, notably including BWA-SW (Li and Durbin, 2010), Bowtie2 (Langmead and Salzberg, 2012), Cushaw2 (Liu and Schmidt, 2012) and GEM (Marco-Sola et al., 2012), have been developed....

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  • ...On speed, BWA-MEM is similar to GEM and Bowtie2 for this data set, but is about 6 times as fast as Bowtie2 and Cushaw2 for a 650bp long-read data set....

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  • ...We evaluated the performance of BWA-MEM on simulated data together with NovoAlign (http://novocraft.com), GEM, Bowtie2, Cushaw2, SeqAlto (Mu et al., 2012), BWA-SW and BWA (Figure 1)....

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Journal ArticleDOI
TL;DR: The newest version of MUMmer easily handles comparisons of large eukaryotic genomes at varying evolutionary distances, as demonstrated by applications to multiple genomes.
Abstract: The newest version of MUMmer easily handles comparisons of large eukaryotic genomes at varying evolutionary distances, as demonstrated by applications to multiple genomes. Two new graphical viewing tools provide alternative ways to analyze genome alignments. The new system is the first version of MUMmer to be released as open-source software. This allows other developers to contribute to the code base and freely redistribute the code. The MUMmer sources are available at http://www.tigr.org/software/mummer.

4,886 citations

Book
01 Jan 1998

1,639 citations