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

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          Abstract

          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. 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. 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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          Most cited references 12

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          NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

          The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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            Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

            A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
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              From RNA-seq reads to differential expression results

              Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.
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                Author and article information

                Journal
                2013-05-14
                2013-11-14
                Article
                10.1093/bioinformatics/btt656
                1305.3347

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                This manuscript has now been published on Bioinformatics Yang Liao, Gordon K Smyth and Wei Shi. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics 2013
                q-bio.GN q-bio.QM

                Quantitative & Systems biology, Genetics

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