Accurate identification of DNA polymorphisms using next-generation sequencing technology is challenging because of a high rate of sequencing error and incorrect mapping of reads to reference genomes. Currently available short read aligners and DNA variant callers suffer from these problems. We developed the Coval software to improve the quality of short read alignments. Coval is designed to minimize the incidence of spurious alignment of short reads, by filtering mismatched reads that remained in alignments after local realignment and error correction of mismatched reads. The error correction is executed based on the base quality and allele frequency at the non-reference positions for an individual or pooled sample. We demonstrated the utility of Coval by applying it to simulated genomes and experimentally obtained short-read data of rice, nematode, and mouse. Moreover, we found an unexpectedly large number of incorrectly mapped reads in ‘targeted’ alignments, where the whole genome sequencing reads had been aligned to a local genomic segment, and showed that Coval effectively eliminated such spurious alignments. We conclude that Coval significantly improves the quality of short-read sequence alignments, thereby increasing the calling accuracy of currently available tools for SNP and indel identification. Coval is available at http://sourceforge.net/projects/coval105/.
Genomic read alignment involves mapping (exactly or approximately) short reads from a particular individual onto a pre-sequenced reference genome of the same species. Because all individuals of the same species share the majority of their genomes, short reads alignment provides an alternative and much more efficient way to sequence the genome of a particular individual than does direct sequencing. Among many strategies proposed for this alignment process, indexing the reference genome and short read searching over the index is a dominant technique. Our goal is to design a space-efficient indexing structure with fast searching capability to catch the massive short reads produced by the next generation high-throughput DNA sequencing technology.
We concentrate on indexing DNA sequences via sparse suffix arrays (SSAs) and propose a new short read aligner named Ψ-RA (PSI-RA: parallel sparse index read aligner). The motivation in using SSAs is the ability to trade memory against time. It is possible to fine tune the space consumption of the index based on the available memory of the machine and the minimum length of the arriving pattern queries. Although SSAs have been studied before for exact matching of short reads, an elegant way of approximate matching capability was missing. We provide this by defining the rightmost mismatch criteria that prioritize the errors towards the end of the reads, where errors are more probable. Ψ-RA supports any number of mismatches in aligning reads. We give comparisons with some of the well-known short read aligners, and show that indexing a genome with SSA is a good alternative to the Burrows-Wheeler transform or seed-based solutions.
Ψ-RA is expected to serve as a valuable tool in the alignment of short reads generated by the next generation high-throughput sequencing technology. Ψ-RA is very fast in exact matching and also supports rightmost approximate matching. The SSA structure that Ψ-RA is built on naturally incorporates the modern multicore architecture and thus further speed-up can be gained. All the information, including the source code of Ψ-RA, can be downloaded at: http://www.busillis.com/o_kulekci/PSIRA.zip.
Motivation: Several software tools specialize in the alignment of short next-generation sequencing reads to a reference sequence. Some of these tools report a mapping quality score for each alignment—in principle, this quality score tells researchers the likelihood that the alignment is correct. However, the reported mapping quality often correlates weakly with actual accuracy and the qualities of many mappings are underestimated, encouraging the researchers to discard correct mappings. Further, these low-quality mappings tend to correlate with variations in the genome (both single nucleotide and structural), and such mappings are important in accurately identifying genomic variants.
Approach: We develop a machine learning tool, LoQuM (LOgistic regression tool for calibrating the Quality of short read mappings, to assign reliable mapping quality scores to mappings of Illumina reads returned by any alignment tool. LoQuM uses statistics on the read (base quality scores reported by the sequencer) and the alignment (number of matches, mismatches and deletions, mapping quality score returned by the alignment tool, if available, and number of mappings) as features for classification and uses simulated reads to learn a logistic regression model that relates these features to actual mapping quality.
Results: We test the predictions of LoQuM on an independent dataset generated by the ART short read simulation software and observe that LoQuM can ‘resurrect’ many mappings that are assigned zero quality scores by the alignment tools and are therefore likely to be discarded by researchers. We also observe that the recalibration of mapping quality scores greatly enhances the precision of called single nucleotide polymorphisms.
Availability: LoQuM is available as open source at http://compbio.case.edu/loqum/.
A common goal in the discovery of rare functional DNA variants via medical resequencing is to incur a relatively lower proportion of false positive base-calls. We developed a novel statistical method for resequencing arrays (SRMA, sequence robust multi-array analysis) to increase the accuracy of detecting rare variants and reduce the costs in subsequent sequence verifications required in medical applications. SRMA includes single and multi-array analysis and accounts for technical variables as well as the possibility of both low- and high-frequency genomic variation. The confidence of each base-call was ranked using two quality measures. In comparison to Sanger capillary sequencing, we achieved a false discovery rate of 2% (false positive rate 1.2 × 10−5, false negative rate 5%), which is similar to automated second-generation sequencing technologies. Applied to the analysis of 39 nuclear candidate genes in disorders of mitochondrial DNA (mtDNA) maintenance, we confirmed mutations in the DNA polymerase gamma POLG in positive control cases, and identified novel rare variants in previously undiagnosed cases in the mitochondrial topoisomerase TOP1MT, the mismatch repair enzyme MUTYH, and the apurinic-apyrimidinic endonuclease APEX2. Some patients carried rare heterozygous variants in several functionally interacting genes, which could indicate synergistic genetic effects in these clinically similar disorders.
The new generation of massively parallel DNA sequencers, combined with the challenge of whole human genome resequencing, result in the need for rapid and accurate alignment of billions of short DNA sequence reads to a large reference genome. Speed is obviously of great importance, but equally important is maintaining alignment accuracy of short reads, in the 25–100 base range, in the presence of errors and true biological variation.
We introduce a new algorithm specifically optimized for this task, as well as a freely available implementation, BFAST, which can align data produced by any of current sequencing platforms, allows for user-customizable levels of speed and accuracy, supports paired end data, and provides for efficient parallel and multi-threaded computation on a computer cluster. The new method is based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. The final local alignment uses a Smith-Waterman method, with gaps to support the detection of small indels.
We compare BFAST to a selection of large-scale alignment tools - BLAT, MAQ, SHRiMP, and SOAP - in terms of both speed and accuracy, using simulated and real-world datasets. We show BFAST can achieve substantially greater sensitivity of alignment in the context of errors and true variants, especially insertions and deletions, and minimize false mappings, while maintaining adequate speed compared to other current methods. We show BFAST can align the amount of data needed to fully resequence a human genome, one billion reads, with high sensitivity and accuracy, on a modest computer cluster in less than 24 hours. BFAST is available at http://bfast.sourceforge.net.
Motivation: Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read-mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics. It is modeled after the short read-mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes.
Results: CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments. Using a larger remote compute cloud with 96 cores, CloudBurst improved performance by >100-fold, reducing the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome.
Availability: CloudBurst is available open-source as a model for parallelizing algorithms with MapReduce at http://cloudburst-bio.sourceforge.net/.
Genome resequencing with short reads generated from pyrosequencing generally relies on mapping the short reads against a single reference genome. However, mapping of reads from multiple reference genomes is not possible using a pairwise mapping algorithm. In order to align the reads w.r.t each other and the reference genomes, existing multiple sequence alignment(MSA) methods cannot be used because they do not take into account the position of these short reads with respect to the genome, and are highly inefficient for large number of sequences. In this paper, we develop a highly scalable parallel algorithm based on domain decomposition, referred to as P-Pyro-Align, to align such large number of reads from single or multiple reference genomes. The proposed alignment algorithm accurately aligns the erroneous reads, and has been implemented on a cluster of workstations using MPI library. Experimental results for different problem sizes are analyzed in terms of execution time, quality of the alignments, and the ability of the algorithm to handle reads from multiple haplotypes. We report high quality multiple alignment of up to 0.5 million reads. The algorithm is shown to be highly scalable and exhibits super-linear speedups with increasing number of processors.
Motivation: Next-generation sequencing captures sequence differences in reads relative to a reference genome or transcriptome, including splicing events and complex variants involving multiple mismatches and long indels. We present computational methods for fast detection of complex variants and splicing in short reads, based on a successively constrained search process of merging and filtering position lists from a genomic index. Our methods are implemented in GSNAP (Genomic Short-read Nucleotide Alignment Program), which can align both single- and paired-end reads as short as 14 nt and of arbitrarily long length. It can detect short- and long-distance splicing, including interchromosomal splicing, in individual reads, using probabilistic models or a database of known splice sites. Our program also permits SNP-tolerant alignment to a reference space of all possible combinations of major and minor alleles, and can align reads from bisulfite-treated DNA for the study of methylation state.
Results: In comparison testing, GSNAP has speeds comparable to existing programs, especially in reads of ≥70 nt and is fastest in detecting complex variants with four or more mismatches or insertions of 1–9 nt and deletions of 1–30 nt. Although SNP tolerance does not increase alignment yield substantially, it affects alignment results in 7–8% of transcriptional reads, typically by revealing alternate genomic mappings for a read. Simulations of bisulfite-converted DNA show a decrease in identifying genomic positions uniquely in 6% of 36 nt reads and 3% of 70 nt reads.
Availability: Source code in C and utility programs in Perl are freely available for download as part of the GMAP package at http://share.gene.com/gmap.
Techniques enabling targeted re-sequencing of the protein coding sequences of the human genome on next generation sequencing instruments are of great interest. We conducted a systematic comparison of the solution-based exome capture kits provided by Agilent and Roche NimbleGen. A control DNA sample was captured with all four capture methods and prepared for Illumina GAII sequencing. Sequence data from additional samples prepared with the same protocols were also used in the comparison.
We developed a bioinformatics pipeline for quality control, short read alignment, variant identification and annotation of the sequence data. In our analysis, a larger percentage of the high quality reads from the NimbleGen captures than from the Agilent captures aligned to the capture target regions. High GC content of the target sequence was associated with poor capture success in all exome enrichment methods. Comparison of mean allele balances for heterozygous variants indicated a tendency to have more reference bases than variant bases in the heterozygous variant positions within the target regions in all methods. There was virtually no difference in the genotype concordance compared to genotypes derived from SNP arrays. A minimum of 11× coverage was required to make a heterozygote genotype call with 99% accuracy when compared to common SNPs on genome-wide association arrays.
Libraries captured with NimbleGen kits aligned more accurately to the target regions. The updated NimbleGen kit most efficiently covered the exome with a minimum coverage of 20×, yet none of the kits captured all the Consensus Coding Sequence annotated exons.
The development of Next Generation Sequencing technologies, capable of sequencing hundreds of millions of short reads (25–70 bp each) in a single run, is opening the door to population genomic studies of non-model species. In this paper we present SHRiMP - the SHort Read Mapping Package: a set of algorithms and methods to map short reads to a genome, even in the presence of a large amount of polymorphism. Our method is based upon a fast read mapping technique, separate thorough alignment methods for regular letter-space as well as AB SOLiD (color-space) reads, and a statistical model for false positive hits. We use SHRiMP to map reads from a newly sequenced Ciona savignyi individual to the reference genome. We demonstrate that SHRiMP can accurately map reads to this highly polymorphic genome, while confirming high heterozygosity of C. savignyi in this second individual. SHRiMP is freely available at http://compbio.cs.toronto.edu/shrimp.
Next Generation Sequencing (NGS) technologies are revolutionizing the way biologists acquire and analyze genomic data. NGS machines, such as Illumina/Solexa and AB SOLiD, are able to sequence genomes more cheaply by 200-fold than previous methods. One of the main application areas of NGS technologies is the discovery of genomic variation within a given species. The first step in discovering this variation is the mapping of reads sequenced from a donor individual to a known (“reference”) genome. Differences between the reference and the reads are indicative either of polymorphisms, or of sequencing errors. Since the introduction of NGS technologies, many methods have been devised for mapping reads to reference genomes. However, these algorithms often sacrifice sensitivity for fast running time. While they are successful at mapping reads from organisms that exhibit low polymorphism rates, they do not perform well at mapping reads from highly polymorphic organisms. We present a novel read mapping method, SHRiMP, that can handle much greater amounts of polymorphism. Using Ciona savignyi as our target organism, we demonstrate that our method discovers significantly more variation than other methods. Additionally, we develop color-space extensions to classical alignment algorithms, allowing us to map color-space, or “dibase”, reads generated by AB SOLiD sequencers.
Many plants have large and complex genomes with an abundance of repeated sequences. Many plants are also polyploid. Both of these attributes typify the genome architecture in the tribe Triticeae, whose members include economically important wheat, rye and barley. Large genome sizes, an abundance of repeated sequences, and polyploidy present challenges to genome-wide SNP discovery using next-generation sequencing (NGS) of total genomic DNA by making alignment and clustering of short reads generated by the NGS platforms difficult, particularly in the absence of a reference genome sequence.
An annotation-based, genome-wide SNP discovery pipeline is reported using NGS data for large and complex genomes without a reference genome sequence. Roche 454 shotgun reads with low genome coverage of one genotype are annotated in order to distinguish single-copy sequences and repeat junctions from repetitive sequences and sequences shared by paralogous genes. Multiple genome equivalents of shotgun reads of another genotype generated with SOLiD or Solexa are then mapped to the annotated Roche 454 reads to identify putative SNPs. A pipeline program package, AGSNP, was developed and used for genome-wide SNP discovery in Aegilops tauschii-the diploid source of the wheat D genome, and with a genome size of 4.02 Gb, of which 90% is repetitive sequences. Genomic DNA of Ae. tauschii accession AL8/78 was sequenced with the Roche 454 NGS platform. Genomic DNA and cDNA of Ae. tauschii accession AS75 was sequenced primarily with SOLiD, although some Solexa and Roche 454 genomic sequences were also generated. A total of 195,631 putative SNPs were discovered in gene sequences, 155,580 putative SNPs were discovered in uncharacterized single-copy regions, and another 145,907 putative SNPs were discovered in repeat junctions. These SNPs were dispersed across the entire Ae. tauschii genome. To assess the false positive SNP discovery rate, DNA containing putative SNPs was amplified by PCR from AL8/78 and AS75 and resequenced with the ABI 3730 xl. In a sample of 302 randomly selected putative SNPs, 84.0% in gene regions, 88.0% in repeat junctions, and 81.3% in uncharacterized regions were validated.
An annotation-based genome-wide SNP discovery pipeline for NGS platforms was developed. The pipeline is suitable for SNP discovery in genomic libraries of complex genomes and does not require a reference genome sequence. The pipeline is applicable to all current NGS platforms, provided that at least one such platform generates relatively long reads. The pipeline package, AGSNP, and the discovered 497,118 Ae. tauschii SNPs can be accessed at (http://avena.pw.usda.gov/wheatD/agsnp.shtml).
The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies.
We describe a computational pipeline, called DIAL (De novo Identification of Alleles), for identifying single-base substitutions between two closely related genomes without the help of a reference genome. The method works even when the depth of coverage is insufficient for de novo assembly, and it can be extended to determine small insertions/deletions. We evaluate the software's effectiveness using published Roche/454 sequence data from the genome of Dr. James Watson (to detect heterozygous positions) and recent Illumina data from orangutan, in each case comparing our results to those from computational analysis that uses a reference genome assembly. We also illustrate the use of DIAL to identify nucleotide differences among transcriptome sequences.
DIAL can be used for identification of nucleotide differences in species for which no reference sequence is available. Our main motivation is to use this tool to survey the genetic diversity of endangered species as the identified sequence differences can be used to design genotyping arrays to assist in the species' management. The DIAL source code is freely available at http://www.bx.psu.edu/miller_lab/.
Next-generation sequencing technologies provide exciting avenues for studies of transcriptomics and population genomics. There is an increasing need to conduct spliced and unspliced alignments of short transcript reads onto a reference genome and estimate minor allele frequency from sequences of population samples.
We have designed and implemented MapNext, a software tool for both spliced and unspliced alignments of short sequence reads onto reference sequences, and automated SNP detection using neighbourhood quality standards. MapNext provides four main analyses: (i) unspliced alignment and clustering of reads, (ii) spliced alignment of transcript reads over intron boundaries, (iii) SNP detection and estimation of minor allele frequency from population sequences, and (iv) storage of result data in a database to make it available for more flexible queries and for further analyses. The software tool has been tested using both simulated and real data.
MapNext is a comprehensive and powerful tool for both spliced and unspliced alignments of short reads and automated SNP detection from population sequences. The simplicity, flexibility and efficiency of MapNext makes it a valuable tool for transcriptomic and population genomic research.
As next-generation sequence (NGS) production continues to increase, analysis is becoming a significant bottleneck. However, in situations where information is required only for specific sequence variants, it is not necessary to assemble or align whole genome data sets in their entirety. Rather, NGS data sets can be mined for the presence of sequence variants of interest by localized assembly, which is a faster, easier, and more accurate approach. We present TASR, a streamlined assembler that interrogates very large NGS data sets for the presence of specific variants by only considering reads within the sequence space of input target sequences provided by the user. The NGS data set is searched for reads with an exact match to all possible short words within the target sequence, and these reads are then assembled stringently to generate a consensus of the target and flanking sequence. Typically, variants of a particular locus are provided as different target sequences, and the presence of the variant in the data set being interrogated is revealed by a successful assembly outcome. However, TASR can also be used to find unknown sequences that flank a given target. We demonstrate that TASR has utility in finding or confirming genomic mutations, polymorphisms, fusions and integration events. Targeted assembly is a powerful method for interrogating large data sets for the presence of sequence variants of interest. TASR is a fast, flexible and easy to use tool for targeted assembly.
Pipelines for the analysis of Next-Generation Sequencing (NGS) data are generally composed of a set of different publicly available software, configured together in order to map short reads of a genome and call variants. The fidelity of pipelines is variable. We have developed ArtificialFastqGenerator, which takes a reference genome sequence as input and outputs artificial paired-end FASTQ files containing Phred quality scores. Since these artificial FASTQs are derived from the reference genome, it provides a gold-standard for read-alignment and variant-calling, thereby enabling the performance of any NGS pipeline to be evaluated. The user can customise DNA template/read length, the modelling of coverage based on GC content, whether to use real Phred base quality scores taken from existing FASTQ files, and whether to simulate sequencing errors. Detailed coverage and error summary statistics are outputted. Here we describe ArtificialFastqGenerator and illustrate its implementation in evaluating a typical bespoke NGS analysis pipeline under different experimental conditions. ArtificialFastqGenerator was released in January 2012. Source code, example files and binaries are freely available under the terms of the GNU General Public License v3.0. from https://sourceforge.net/projects/artfastqgen/.
Next-generation DNA sequencing technologies generate tens of millions of sequencing reads in one run. These technologies are now widely used in biology research such as in genome-wide identification of polymorphisms, transcription factor binding sites, methylation states, and transcript expression profiles. Mapping the sequencing reads to reference genomes efficiently and effectively is one of the most critical analysis tasks. Although several tools have been developed, their performance suffers when both multiple substitutions and insertions/deletions (indels) occur together.
We report a new algorithm, Basic Oligonucleotide Alignment Tool (BOAT) that can accurately and efficiently map sequencing reads back to the reference genome. BOAT can handle several substitutions and indels simultaneously, a useful feature for identifying SNPs and other genomic structural variations in functional genomic studies. For better handling of low-quality reads, BOAT supports a "3'-end Trimming Mode" to build local optimized alignment for sequencing reads, further improving sensitivity. BOAT calculates an E-value for each hit as a quality assessment and provides customizable post-mapping filters for further mapping quality control.
Evaluations on both real and simulation datasets suggest that BOAT is capable of mapping large volumes of short reads to reference sequences with better sensitivity and lower memory requirement than other currently existing algorithms. The source code and pre-compiled binary packages of BOAT are publicly available for download at http://boat.cbi.pku.edu.cn under GNU Public License (GPL). BOAT can be a useful new tool for functional genomics studies.
Motivation: Novel high-throughput sequencing technologies pose new algorithmic challenges in handling massive amounts of short-read, high-coverage data. A robust and versatile consensus tool is of particular interest for such data since a sound multi-read alignment is a prerequisite for variation analyses, accurate genome assemblies and insert sequencing.
Results: A multi-read alignment algorithm for de novo or reference-guided genome assembly is presented. The program identifies segments shared by multiple reads and then aligns these segments using a consistency-enhanced alignment graph. On real de novo sequencing data obtained from the newly established NCBI Short Read Archive, the program performs similarly in quality to other comparable programs. On more challenging simulated datasets for insert sequencing and variation analyses, our program outperforms the other tools.
Availability: The consensus program can be downloaded from http://www.seqan.de/projects/consensus.html. It can be used stand-alone or in conjunction with the Celera Assembler. Both application scenarios as well as the usage of the tool are described in the documentation.
Motivation: Discovering variation among high-throughput sequenced genomes relies on efficient and effective mapping of sequence reads. The speed, sensitivity and accuracy of read mapping are crucial to determining the full spectrum of single nucleotide variants (SNVs) as well as structural variants (SVs) in the donor genomes analyzed.
Results: We present drFAST, a read mapper designed for di-base encoded ‘color-space’ sequences generated with the AB SOLiD platform. drFAST is specially designed for better delineation of structural variants, including segmental duplications, and is able to return all possible map locations and underlying sequence variation of short reads within a user-specified distance threshold. We show that drFAST is more sensitive in comparison to all commonly used aligners such as Bowtie, BFAST and SHRiMP. drFAST is also faster than both BFAST and SHRiMP and achieves a mapping speed comparable to Bowtie.
Availability: The source code for drFAST is available at http://drfast.sourceforge.net
Motivation: High-throughput sequencing technologies have made population-scale studies of human genetic variation possible. Accurate and comprehensive detection of DNA sequence variants is crucial for the success of these studies. Small insertions and deletions represent the second most frequent class of variation in the human genome after single nucleotide polymorphisms (SNPs). Although several alignment tools for the gapped alignment of sequence reads to a reference genome are available, computational methods for discriminating indels from sequencing errors and genotyping indels directly from sequence reads are needed.
Results: We describe a probabilistic method for the accurate detection and genotyping of short indels from population-scale sequence data. In this approach, aligned sequence reads from a population of individuals are used to automatically account for context-specific sequencing errors associated with indels. We applied this approach to population sequence datasets from the 1000 Genomes exon pilot project generated using the Roche 454 and Illumina sequencing platforms, and were able to detect a significantly greater number of indels than reported previously. Comparison to indels identified in the 1000 Genomes pilot project demonstrated the sensitivity of our method. The consistency in the number of indels and the fraction of indels whose length is a multiple of three across different human populations and two different sequencing platforms indicated that our method has a low false discovery rate. Finally, the method represents a general approach for the detection and genotyping of small-scale DNA sequence variants for population-scale sequencing projects.
Availability: A program implementing this method is available at http://polymorphism.scripps.edu/~vbansal/software/piCALL/
Supplementary information: Supplementary data are available at Bioinformatics online.
New software for the alignment of short-read sequence data to multiple genomes allows identification of polymorphisms that cannot be identified by alignment to a single reference genome.
Genome resequencing with short reads generally relies on alignments against a single reference. GenomeMapper supports simultaneous mapping of short reads against multiple genomes by integrating related genomes (e.g., individuals of the same species) into a single graph structure. It constitutes the first approach for handling multiple references and introduces representations for alignments against complex structures. Demonstrated benefits include access to polymorphisms that cannot be identified by alignments against the reference alone. Download GenomeMapper at .
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.
Next Generation Sequencing (NGS) technology generates tens of millions of short reads for each DNA/RNA sample. A key step in NGS data analysis is the short read alignment of the generated sequences to a reference genome. Although storing alignment information in the Sequence Alignment/Map (SAM) or Binary SAM (BAM) format is now standard, biomedical researchers still have difficulty accessing this information.
We have developed a Graphical User Interface (GUI) software tool named SAMMate. SAMMate allows biomedical researchers to quickly process SAM/BAM files and is compatible with both single-end and paired-end sequencing technologies. SAMMate also automates some standard procedures in DNA-seq and RNA-seq data analysis. Using either standard or customized annotation files, SAMMate allows users to accurately calculate the short read coverage of genomic intervals. In particular, for RNA-seq data SAMMate can accurately calculate the gene expression abundance scores for customized genomic intervals using short reads originating from both exons and exon-exon junctions. Furthermore, SAMMate can quickly calculate a whole-genome signal map at base-wise resolution allowing researchers to solve an array of bioinformatics problems. Finally, SAMMate can export both a wiggle file for alignment visualization in the UCSC genome browser and an alignment statistics report. The biological impact of these features is demonstrated via several case studies that predict miRNA targets using short read alignment information files.
With just a few mouse clicks, SAMMate will provide biomedical researchers easy access to important alignment information stored in SAM/BAM files. Our software is constantly updated and will greatly facilitate the downstream analysis of NGS data. Both the source code and the GUI executable are freely available under the GNU General Public License at http://sammate.sourceforge.net.
Motivation: The explosive growth of next-generation sequencing datasets poses a challenge to the mapping of reads to reference genomes in terms of alignment quality and execution speed. With the continuing progress of high-throughput sequencing technologies, read length is constantly increasing and many existing aligners are becoming inefficient as generated reads grow larger.
Results: We present CUSHAW2, a parallelized, accurate, and memory-efficient long read aligner. Our aligner is based on the seed-and-extend approach and uses maximal exact matches as seeds to find gapped alignments. We have evaluated and compared CUSHAW2 to the three other long read aligners BWA-SW, Bowtie2 and GASSST, by aligning simulated and real datasets to the human genome. The performance evaluation shows that CUSHAW2 is consistently among the highest-ranked aligners in terms of alignment quality for both single-end and paired-end alignment, while demonstrating highly competitive speed. Furthermore, our aligner shows good parallel scalability with respect to the number of CPU threads.
Availability: CUSHAW2, written in C++, and all simulated datasets are available at http://cushaw2.sourceforge.net
Supplementary data are available at Bioinformatics online.
Background: Deep RNA sequencing, the application of Next Generation sequencing technology to generate a comprehensive profile of the message RNA present in a set of biological samples, provides unprecedented resolution into the molecular foundations of biological processes. By aligning short read RNA sequence data to a set of gene models, expression patterns for all of the genes and gene variants in a biological sample can be calculated. However, accurate determination of gene model expression from deep RNA sequencing is hindered by the presence of ambiguously aligning short read sequences.
BowStrap, a program for implementing the sequence alignment tool ‘Bowtie’ in a bootstrap-style approach, accommodates multiply-aligning short read sequences and reports gene model expression as an averaged aligned reads per Kb of gene model sequence per million aligned deep RNA sequence reads with a confidence interval, suitable for calculating statistical significance of presence/absence of detected gene model expression. BowStrap v1.0 was validated against a simulated metatranscriptome. Results were compared with two alternate ‘Bowtie’-based calculations of gene model expression. BowStrap is better at accurately identifying expressed gene models in a dataset and provides a more accurate estimate of gene model expression level than methods that do not incorporate a boot-strap style approach.
BowStrap v1.0 is superior in ability to detect significant gene model expression and calculate accurate determination of gene model expression levels compared to other alignment-based methods of determining patterns of gene expression. BowStrap v1.0 also can utilize multiple processors as has decreased run time compared to the previous version, BowStrap 0.5. We anticipate that BowStrap will be a highly useful addition to the available set of Next Generation RNA sequence analysis tools.
Transcriptomics; Next Generation Sequencing; Gene expression; Metatranscriptome
Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.