Fast and accurate short read alignment with Burrows–Wheeler transform
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]
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TL;DR: Timmomatic is developed as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data and is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Abstract: Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.
Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic
Contact: ed.nehcaa-htwr.1oib@ledasu
Supplementary information: Supplementary data are available at Bioinformatics online.
39,291 citations
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
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
20,557 citations
Cites background or methods from "Fast and accurate short read alignm..."
...…for the Service Email Alerting click here.top right corner of the article or Receive free email alerts when new articles cite this article - sign up in the box at the http://genome.cshlp.org/subscriptions go to: Genome Research To subscribe to Copyright © 2010 by Cold Spring Harbor Laboratory Press...
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...Many tools have been created to work with next-generation sequencer data, from read based aligners like MAQ (Li et al. 2008a), BWA (Li and Durbin 2009), and SOAP (Li et al. 2008b), to single nucleotide polymorphism and structural variation detection tools like BreakDancer (Chen et al. 2009),…...
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...Many tools have been created to work with next-generation sequencer data, from read based aligners like MAQ (Li et al. 2008a), BWA (Li and Durbin 2009), and SOAP (Li et al. 2008b), to single nucleotide polymorphism and structural variation detection tools like BreakDancer (Chen et al. 2009), VarScan (Koboldt et al. 2009), and MAQ....
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...…article cites 26 articles, 13 of which can be accessed free at: License Commons Creative http://creativecommons.org/licenses/by-nc/3.0/.described at a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as )....
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...Bayesian estimation of the most likely genotype from next-generation DNA resequencing reads has already proven valuable (Li et al. 2008a,b; Li and Durbin 2009)....
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TL;DR: The command-line tool cutadapt is developed, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features.
Abstract: When small RNA is sequenced on current sequencing machines, the resulting reads are usually longer than the RNA and therefore contain parts of the 3' adapter. That adapter must be found and removed error-tolerantly from each read before read mapping. Previous solutions are either hard to use or do not offer required features, in particular support for color space data. As an easy to use alternative, we developed the command-line tool cutadapt, which supports 454, Illumina and SOLiD (color space) data, offers two adapter trimming algorithms, and has other useful features. Cutadapt, including its MIT-licensed source code, is available for download at http://code.google.com/p/cutadapt/
20,255 citations
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
14,103 citations
Cites background from "Fast and accurate short read alignm..."
...The computational problem of mapping short sequence reads to a reference genome has received enormous attention in the past few years (Li and Durbin, 2009; Langmead et al., 2009; Fonseca et al., 2012; Marco-Sola et al., 2012; Liao et al., 2013), and the rapid development of fast and reliable…...
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References
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09 Sep 2003
TL;DR: It may now be practically feasible to query large biological sequence data sets using an accurate local-alignment search algorithm, called OASIS, which employs a dynamic programming A*-search driven by a suffix-tree index that is built on the input data set.
Abstract: A common query against large protein and gene sequence data sets is to locate targets that are similar to an input query sequence. The current set of popular search tools, such as BLAST, employ heuristics to improve the speed of such searches. However, such heuristics can sometimes miss targets, which in many cases is undesirable. The alternative to BLAST is to use an accurate algorithm, such as the Smith-Waterman (S-W) algorithm. However, these accurate algorithms are computationally very expensive, which limits their use in practice. This paper takes on the challenge of designing an accurate and efficient algorithm for evaluating local-alignment searches.
To meet this goal, we propose a novel search algorithm, called OASIS. This algorithm employs a dynamic programming A*-search driven by a suffix-tree index that is built on the input data set. We experimentally evaluate OASIS and demonstrate that for an important class of searches, in which the query sequence lengths are small, OASIS is more than an order of magnitude faster than S-W. In addition, the speed of OASIS is comparable to BLAST. Furthermore, OASIS returns results in decreasing order of the matching score, making it possible to use OASIS in an online setting. Consequently, we believe that it may now be practically feasible to query large biological sequence data sets using an accurate local-alignment search algorithm.
99 citations
TL;DR: This paper initiates the study of constructing compressed suffix arrays directly from the text with a construction algorithm that uses only O(n) bits of working memory, and the time complexity is O( n log n).
Abstract: With the first human DNA being decoded into a sequence of about 28 billion characters, much biological research has been centered on analyzing this sequence Theoretically speaking, it is now feasible to accommodate an index for human DNA in the main memory so that any pattern can be located efficiently This is due to the recent breakthrough on compressed suffix arrays, which reduces the space requirement from O(n log n) bits to O(n) bits However, constructing compressed suffix arrays is still not an easy task because we still have to compute suffix arrays first and need a working memory of O(n log n) bits (ie, more than 13 gigabytes for human DNA) This paper initiates the study of constructing compressed suffix arrays directly from the text The main contribution is a construction algorithm that uses only O(n) bits of working memory, and the time complexity is O(n log n) Our construction algorithm is also time and space efficient for texts with large alphabets such as Chinese or Japanese Precisely, when the alphabet size is |Σ|, the working space is O(n log |Σ|) bits, and the time complexity remains O(n log n), which is independent of |Σ|
84 citations
"Fast and accurate short read alignm..." refers background in this paper
...Recently, Hon et al. (2007) gave a new algorithm that uses n bits of working space and only requires <1 GB memory at peak time for constructing the BWT of human genome ....
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15 Aug 2002
TL;DR: This paper initiates the study of constructing compressed suffix arrays directly from text with the main contribution is a new construction algorithm that uses only O(n) bits of working memory, and more importantly, the time complexity remains the same as before.
Abstract: With the first Human DNA being decoded into a sequence of about 2.8 billion base pairs, many biological research has been centered on analyzing this sequence. Theoretically speaking, it is now feasible to accommodate an index for human DNA in main memory so that any pattern can be located efficiently. This is due to the recent breakthrough on compressed suffix arrays, which reduces the space requirement from O(n log n) bits to O(n) bits. However, constructing compressed suffix arrays is still not an easy task because we still have to compute suffix arrays first and need a working memory of O(n log n) bits (i.e., more than 13 Gigabytes for human DNA). This paper initiates the study of constructing compressed suffix arrays directly from text. The main contribution is a new construction algorithm that uses only O(n) bits of working memory, and more importantly, the time complexity remains the same as before, i.e., O(n log n).
77 citations
TL;DR: A new alignment approach (Slider) that reduces the alignment problem space by utilizing each read base's probabilities given in the prb files, and its SNP predictions are more accurate than other SNP prediction approaches used today that start from the most probable sequence.
Abstract: Motivation: A plethora of alignment tools have been created that are designed to best fit different types of alignment conditions. While some of these are made for aligning Illumina Sequence Analyzer reads, none of these are fully utilizing its probability (prb) output. In this article, we will introduce a new alignment approach (Slider) that reduces the alignment problem space by utilizing each read base's probabilities given in the prb files.
Results: Compared with other aligners, Slider has higher alignment accuracy and efficiency. In addition, given that Slider matches bases with probabilities other than the most probable, it significantly reduces the percentage of base mismatches. The result is that its SNP predictions are more accurate than other SNP prediction approaches used today that start from the most probable sequence, including those using base quality.
Contact: [email protected]
Supplementary information and availability: http://www.bcgsc.ca/platform/bioinfo/software/slider
68 citations
TL;DR: A description of the compressed suffix array (CSA) and how it is used to obtain matches is presented, and an implementation is demonstrated by aligning two mammalian genomes on a modest workstation equipped with under 2 GB of free RAM in time superior to that of the implementations of other data structures.
Abstract: The starting point for any alignment of mammalian genomes is the computation of exact matches satisfying various criteria. Time-efficient, O(n), data structures for this computation, such as the suffix tree, require O(n log(n)) space, several times the space of the genomes themselves. Thus, any reasonable whole-genome comparative project finds itself requiring tens of Gigabytes of RAM to maintain time-efficiency. This is beyond most modern workstations. With a new data structure, the compressed suffix array (CSA) implemented via the Burrows–Wheeler transform, we can trade time-efficiency for space-efficiency, taking O(n log(n)) time, but running in O(n) space, typically in total space less than or equal to that of the genomes themselves. If space is more expensive than time, this is an appropriate approach to consider. The most space-efficient implementation of this data structure requires 5 bits per nucleotide character to build on-line, in the worst case, and 2.5 bits per character to store once built. ...
54 citations
"Fast and accurate short read alignm..." refers methods in this paper
...Essentially, using backward search (Ferragina and Manzini, 2000; Lippert, 2005) with BWT, we are able to effectively mimic the topdown traversal on the prefix trie of the genome with relatively small memory footprint (Lam et al., 2008) and to count the number of exact hits of a string of length m…...
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