2012 · Bioinformatics
Alexander Dobin, Carrie Davis, Felix Schlesinger, Jörg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, T …
MOTIVATION: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. RESULTS: To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. AVAILABILITY AND IMPLEMENTATION: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
57,443 citations12 viewsFull text
DOI: 10.1093/bioinformatics/bts6352010 · Bioinformatics
Aaron R. Quinlan, Ira M. Hall
MOTIVATION: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. RESULTS: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. AVAILABILITY AND IMPLEMENTATION: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools CONTACT: aaronquinlan@gmail.com; imh4y@virginia.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
31,025 citations6 viewsFull text
DOI: 10.1093/bioinformatics/btq0332009 · Bioinformatics
Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gábor Marth, Gonçalo R. Abecasis, Richard Durbin, 1…
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. AVAILABILITY: http://samtools.sourceforge.net.
68,524 citations9 viewsFull text
DOI: 10.1093/bioinformatics/btp3522009 · Bioinformatics
Peter Cock, Tiago Antão, Jeffrey T. Chang, Brad Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank…
Abstract Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_listspeter.cock@scri.ac.uk.
6,696 citations14 viewsFull text
DOI: 10.1093/bioinformatics/btp1632003 · Bioinformatics
Fredrik Ronquist, John P. Huelsenbeck
Abstract Summary: MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types—e.g. morphological, nucleotide, and protein—and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters. Availability: http://morphbank.ebc.uu.se/mrbayes Contact: fredrik.ronquist@ebc.uu.se * To whom correspondence should be addressed.
29,493 citations13 viewsFull text
DOI: 10.1093/bioinformatics/btg1802003 · Bioinformatics
Michael Hucka, Andrew Finney, Herbert M. Sauro, Hamid Bolouri, John C. Doyle, Hiroaki Kitano, Adam P. Arkin, B. Bornstein, D…
MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/
3,170 citations13 viewsFull text
DOI: 10.1093/bioinformatics/btg0151998 · Bioinformatics
Sean R. Eddy
The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
5,837 citations18 viewsFull text
DOI: 10.1093/bioinformatics/14.9.7551998 · Bioinformatics
David Posada, Keith A. Crandall
SUMMARY: The program MODELTEST uses log likelihood scores to establish the model of DNA evolution that best fits the data. AVAILABILITY: The MODELTEST package, including the source code and some documentation is available at http://bioag.byu. edu/zoology/crandall_lab/modeltest.html.
20,127 citations13 views
DOI: 10.1093/bioinformatics/14.9.817