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Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTT656

featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

01 Apr 2014-Bioinformatics (Oxford University Press)-Vol. 30, Iss: 7, pp 923-930
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.

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Citations
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Open accessJournal ArticleDOI: 10.1186/S13059-014-0550-8
05 Dec 2014-Genome Biology
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

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Topics: MRNA Sequencing (54%), Integrator complex (51%), Count data (50%) ...read more

29,675 Citations


Open accessJournal ArticleDOI: 10.1093/NAR/GKV007
Matthew E. Ritchie1, Belinda Phipson2, Di Wu3, Yifang Hu1  +4 moreInstitutions (5)
Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Topics: Microarray databases (61%), Bioconductor (51%)

13,819 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTU638
15 Jan 2015-Bioinformatics
Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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11,833 Citations


Open accessJournal ArticleDOI: 10.1186/GB-2014-15-2-R29
Charity W. Law1, Charity W. Law2, Yunshun Chen2, Yunshun Chen1  +4 moreInstitutions (2)
03 Feb 2014-Genome Biology
Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

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3,339 Citations


Open accessPosted ContentDOI: 10.1101/002832
17 Nov 2014-bioRxiv
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-Seq data, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data. DESeq2 uses shrinkage estimation for dispersions and fold changes to improve stability and interpretability of the estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression and facilitates downstream tasks such as gene ranking and visualization. DESeq2 is available as an R/Bioconductor package.

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  • Figure 9 Precision estimated from experimental reproducibility. Each algorithm’s precision in the evaluation set (box plots) is evaluated using the calls of each other algorithm in the verification set (panels with grey label).
    Figure 9 Precision estimated from experimental reproducibility. Each algorithm’s precision in the evaluation set (box plots) is evaluated using the calls of each other algorithm in the verification set (panels with grey label).
  • Figure 2 Effect of shrinkage on logarithmic fold change estimates. Plots of the (A)MLE (i.e., no shrinkage) and (B)MAP estimate (i.e., with shrinkage) for the LFCs attributable to mouse strain, over the average expression strength for a ten vs eleven sample comparison of the Bottomly et al. [16] dataset. Small triangles at the top and bottom of the plots indicate points that would fall outside of the plotting window. Two genes with similar mean count and MLE logarithmic fold change are highlighted with green and purple circles. (C) The counts (normalized by size factors sj) for these genes reveal low dispersion for the gene in green and high dispersion for the gene in purple. (D) Density plots of the likelihoods (solid lines, scaled to integrate to 1) and the posteriors (dashed lines) for the green and purple genes and of the prior (solid black line): due to the higher
    Figure 2 Effect of shrinkage on logarithmic fold change estimates. Plots of the (A)MLE (i.e., no shrinkage) and (B)MAP estimate (i.e., with shrinkage) for the LFCs attributable to mouse strain, over the average expression strength for a ten vs eleven sample comparison of the Bottomly et al. [16] dataset. Small triangles at the top and bottom of the plots indicate points that would fall outside of the plotting window. Two genes with similar mean count and MLE logarithmic fold change are highlighted with green and purple circles. (C) The counts (normalized by size factors sj) for these genes reveal low dispersion for the gene in green and high dispersion for the gene in purple. (D) Density plots of the likelihoods (solid lines, scaled to integrate to 1) and the posteriors (dashed lines) for the green and purple genes and of the prior (solid black line): due to the higher
  • Figure 8 Sensitivity estimated from experimental reproducibility. Each algorithm’s sensitivity in the evaluation set (box plots) is evaluated using the calls of each other algorithm in the verification set (panels with grey label).
    Figure 8 Sensitivity estimated from experimental reproducibility. Each algorithm’s sensitivity in the evaluation set (box plots) is evaluated using the calls of each other algorithm in the verification set (panels with grey label).
  • Figure 3 Stability of logarithmic fold changes. DESeq2 is run on equally split halves of the data of Bottomly et al. [16], and the LFCs from the halves are plotted against each other. (A)MLEs, i.e., without LFC shrinkage. (B)MAP estimates, i.e., with shrinkage. Points in the top left and bottom right quadrants indicate genes with a change of sign of LFC. Red points indicate genes with adjusted P value < 0.1. The legend displays the root-mean-square error of the estimates in group I compared to those in group II. LFC, logarithmic fold change; MAP, maximum a posteriori; MLE,
    Figure 3 Stability of logarithmic fold changes. DESeq2 is run on equally split halves of the data of Bottomly et al. [16], and the LFCs from the halves are plotted against each other. (A)MLEs, i.e., without LFC shrinkage. (B)MAP estimates, i.e., with shrinkage. Points in the top left and bottom right quadrants indicate genes with a change of sign of LFC. Red points indicate genes with adjusted P value < 0.1. The legend displays the root-mean-square error of the estimates in group I compared to those in group II. LFC, logarithmic fold change; MAP, maximum a posteriori; MLE,
Topics: Count data (53%), Bioconductor (53%), Fold change (51%)

2,229 Citations


References
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Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP352
Heng Li1, Bob Handsaker2, Alec Wysoker2, T. J. Fennell2  +5 moreInstitutions (4)
01 Aug 2009-Bioinformatics
Abstract: 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 Contact: [email protected]

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Topics: Variant Call Format (62%), Stockholm format (61%), FASTQ format (56%) ...read more

35,747 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP324
Heng Li1, Richard Durbin1Institutions (1)
01 Jul 2009-Bioinformatics
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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Topics: Hybrid genome assembly (54%), Sequence assembly (53%), 2 base encoding (52%) ...read more

35,234 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTP616
01 Jan 2010-Bioinformatics
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).

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Topics: Bioconductor (64%)

21,575 Citations


Open accessJournal ArticleDOI: 10.1186/GB-2009-10-3-R25
04 Mar 2009-Genome Biology
Abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.

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Topics: Hybrid genome assembly (51%)

18,079 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTQ033
Aaron R. Quinlan1, Ira M. Hall1Institutions (1)
15 Mar 2010-Bioinformatics
Abstract: 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 webbased 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

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Topics: Software suite (52%), Source code (50%)

14,088 Citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202211
20212,710
20202,192
20191,484
20181,003
2017669
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