featureCounts

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featureCounts: a general-purpose read summarization function

This function assigns mapped sequencing reads to genomic features

Usage
featureCounts(files,
# annotation annot.inbuilt="mm10", annot.ext=NULL, isGTFAnnotationFile=FALSE, GTF.featureType="exon", GTF.attrType="gene_id", chrAliases=NULL, # level of summarization useMetaFeatures=TRUE, # overlap between reads and features allowMultiOverlap=FALSE, minOverlap=1, largestOverlap=FALSE, readExtension5=0, readExtension3=0, read2pos=NULL, # multi-mapping reads countMultiMappingReads=FALSE, fraction=FALSE,
# read filtering minMQS=0, splitOnly=FALSE, nonSplitOnly=FALSE, primaryOnly=FALSE, ignoreDup=FALSE, # strandness strandSpecific=0, # exon-exon junctions juncCounts=FALSE, genome=NULL, # parameters specific to paired end reads isPairedEnd=FALSE, requireBothEndsMapped=FALSE, checkFragLength=FALSE, minFragLength=50, maxFragLength=600, countChimericFragments=TRUE, autosort=TRUE, # miscellaneous nthreads=1, maxMOp=10, reportReads=FALSE)
Arguments
files
a character vector giving names of input files containing read mapping results. The files can be in either SAM format or BAM format. The file format is automatically detected by the function.
annot.inbuilt
a character string specifying an in-built annotation used for read summarization. It has four possible values including mm10, mm9, hg38 and hg19, corresponding to the NCBI RefSeq annotations for genomes `mm10', `mm9', `hg38' and `hg19', respectively. mm10 by default. The in-built annotation has a SAF format (see below).
annot.ext
A character string giving name of a user-provided annotation file or a data frame including user-provided annotation data. If the annotation is in GTF format, it can only be provided as a file. If it is in SAF format, it can be provided as a file or a data frame. See below for more details about SAF format annotation. annot.ext will override annot.inbuilt if they are both provided.
isGTFAnnotationFile
logical indicating whether the annotation provided via the annot.ext argument is in GTF format or not. FALSE by default. This option is only applicable when annot.ext is not NULL.
GTF.featureType
a character string giving the feature type used to select rows in the GTF annotation which will be used for read summarization. exon by default. This argument is only applicable when isGTFAnnotationFile is TRUE.
GTF.attrType
a character string giving the attribute type in the GTF annotation which will be used to group features (eg. exons) into meta-features (eg. genes). gene_id by default. This argument is only applicable when isGTFAnnotationFile is TRUE.
chrAliases
a character string giving the name of a file that contains aliases of chromosome names. The file should be a comma delimited text file that includes two columns. The first column gives the chromosome names used in the annotation and the second column gives the chromosome names used by reads. This file should not contain header lines. Names included in this file are case sensitive.
useMetaFeatures
logical indicating whether the read summarization should be performed at the feature level (eg. exons) or meta-feature level (eg genes). If TRUE, features in the annotation (each row is a feature) will be grouped into meta-features using their values in the ``GeneID" column in the SAF-format annotation file or using the ``gene_id" attribute in the GTF-format annotation file, and reads will assiged to the meta-features instead of the features. See below for more details.
allowMultiOverlap
logical indicating if a read is allowed to be assigned to more than one feature (or meta-feature) if it is found to overlap with more than one feature (or meta-feature). FALSE by default.
minOverlap
integer giving the minimum number of overlapped bases required for assigning a read to a feature (or a meta-feature). For assignment of read pairs (fragments), numbers of overlapping bases from each read in the same pair will be summed. If a negative value is provided, the read will be extended from both ends. 1 by default.
largestOverlap
If TRUE, a read (or read pair) will be assigned to the feature (or meta-feature) that has the largest number of overlapping bases, if the read (or read pair) overlaps with multiple features (or meta-features).
readExtension5
integer giving the number of bases extended upstream from 5' end of each read. 0 by default.
readExtension3
integer giving the number of bases extended downstream from 3' end of each read. 0 by default.
read2pos
Specifying whether each read should be reduced to its 5' most base or 3' most base. It has three possible values: NULL, 5 (denoting 5' most base) and 3 (denoting 3' most base). The default value is NULL. With the default value, the whole read is used for summarization. When read2pos is set to 5 (or 3), read summarization will be performed based on the 5' (or 3') most base position. read2pos can be used together with readExtension5 and readExtension3 parameters to set any desired length for reads.
countMultiMappingReads
logical indicating if multi-mapping reads/fragments should be counted, FALSE by default. If TRUE, a multi-mapping read will be counted up to N times if it has N reported mapping locations. This function uses the `NH' tag to find multi-mapping reads.
fraction
logical indicating if fractional counts will be produced for multi-mapping reads. If TRUE, a fractional count, 1/n, will be generated for each reported alignment of a multi-mapping read, where n is the total number of alignments reported for that read. countMultiMappingReads must be set to TRUE when fraction is TRUE.
minMQS
integer giving the minimum mapping quality score a read must satisfy in order to be counted. For paired-end reads, at least one end should satisfy this criteria. 0 by default.
splitOnly
logical indicating whether only split alignments (their CIGAR strings contain letter 'N') should be included for summarization. FALSE by default. Example split alignments are exon-spanning reads from RNA-seq data. useMetaFeatures should be set to FALSE and allowMultiOverlap should be set to TRUE, if the purpose of summarization is to assign exon-spanning reads to all their overlapping exons.
nonSplitOnly
logical indicating whether only non-split alignments (their CIGAR strings do not contain letter 'N') should be included for summarization. FALSE by default.
primaryOnly
logical indicating if only primary alignments should be counted. Primary and secondary alignments are identified using bit 0x100 in the Flag field of SAM/BAM files. If TRUE, all primary alignments in a dataset will be counted no matter they are from multi-mapping reads or not (ie. countMultiMappingReads is ignored).
ignoreDup
logical indicating whether reads marked as duplicates should be ignored. FALSE by default. Read duplicates are identified using bit Ox400 in the FLAG field in SAM/BAM files. The whole fragment (read pair) will be ignored if paired end.
strandSpecific
integer indicating if strand-specific read counting should be performed. It has three possible values: 0 (unstranded), 1 (stranded) and 2 (reversely stranded). 0 by default.
juncCounts
logical indicating if number of reads supporting each exon-exon junction will be reported. Junctions are identified from those exon-spanning reads in input data. FALSE by default.
genome
a character string giving the name of a FASTA-format file that includes the reference genome sequences. The reference genome provided here should be the same as the one used in read mapping. NULL by default.
isPairedEnd
logical indicating if paired-end reads are used. If TRUE, fragments (templates or read pairs) will be counted instead of individual reads. FALSE by default.
requireBothEndsMapped
logical indicating if both ends from the same fragment are required to be successfully aligned before the fragment can be assigned to a feature or meta-feature. This parameter is only appliable when isPairedEnd is TRUE.
checkFragLength
logical indicating if the two ends from the same fragment are required to satisify the fragment length criteria before the fragment can be assigned to a feature or meta-feature. This parameter is only appliable when isPairedEnd is TRUE. The fragment length criteria are specified via minFragLength and maxFragLength.
minFragLength
integer giving the minimum fragment length for paired-end reads. 50 by default.
maxFragLength
integer giving the maximum fragment length for paired-end reads. 600 by default. minFragLength and maxFragLength are only applicable when isPairedEnd is TRUE. Note that when a fragment spans two or more exons, the observed fragment length might be much bigger than the nominal fragment length.
countChimericFragments
logical indicating whether a chimeric fragment, which has its two reads mapped to different chromosomes, should be counted or not. TRUE by default.
autosort
logical specifying if the automatic read sorting is enabled. This option is only applicable for paired-end reads. If TRUE, reads will be automatically sorted by their names if reads from the same pair are found not to be located next to each other in the input. No read sorting will be performed if there are no such reads found.
nthreads
integer giving the number of threads used for running this function. 1 by default.
maxMOp
integer giving the maximum number of `M' operations (matches or mis-matches) allowed in a CIGAR string. 10 by default. Both `X' and `=' operations are treated as `M' and adjacent `M' operations are merged in the CIGAR string.
reportReads
logical indicating if read counting result for each read/fragment is saved to a file. If TRUE, read counting results for reads/fragments will be saved to a tab-delimited file that contains four columns including name of read/fragment, status(assigned or the reason if not assigned), name of target feature/meta-feature and number of hits if the read/fragment is counted multiple times. Name of the file is the same as name of the input read file except a suffix `.featureCounts' is added. Multiple files will be generated if there is more than one input read file.
Details

featureCounts is a general-purpose read summarization function, which assigns to the genomic features (or meta-features) the mapped reads that were generated from genomic DNA and RNA sequencing.

This function takes as input a set of files containing read mapping results output from a read aligner (e.g. align), and then assigns mapped reads to genomic features. Both SAM and BAM format input files are accepted.

The argument useMetaFeatures specifies the read summarization should be performed at the feature level or at the meta-feature level. Each entry in the annotation data is a feature, which for example could be an exon. When useMetaFeatures is TRUE, the featureCounts function creates meta-features by grouping features using the gene identifiers included in the ``GeneID" column in the annotation data (or in the ``gene_id" attribute in the GTF format annotation file) and then assigns reads to meta-features instead of features. The useMetaFeatures is particularly useful for gene-level expression analysis, because it instructs this function to count reads for genes (meta-features) instead of exons (features). Note that when meta-features are used in the read summarization, if a read is found to overlap two or more features belong to the same meta-feature it will be only counted once for that meta-feature.

The argument allowMultiOverlap specifies how those reads, which are found to overlap with more than one feature (or meta-feature), should be assigned. When allowMultiOverlap is FALSE, a read overlapping multiple features (or meta-features) will not be assigned to any of them (not counted). Otherwise, it will be assigned to all of them. A read overlaps a meta-feature if it overlaps at least one of the features belonging to this meta-feature.

gene and exon are typically used when summarizing RNA-seq read data, which will yield read counts for genes and exons, respectively.

The in-built annotations for human and mouse genomes (hg38, hg19, mm10 and mm9) provided in this function can be conveniently used for read summarization. These annotations were downloaded from the NCBI ftp server (ftp://ftp.ncbi.nlm.nih.gov/genomes/) and were then postprocessed by removing redundant chromosomal regions within each gene and combining adjacent CDS and UTR sequences. The in-built annotations use the SAF annotation format (see below) and their content can be retrieved using the getInBuiltAnnotation function.

Users may also choose to provide their own annotation for summarization. If users provide a SAF (Simplified Annotation Format) annotation, the annotation should have the following format:

GeneID		Chr	Start	End	Strand
497097		chr1	3204563	3207049	-
497097		chr1	3411783	3411982	-
497097		chr1	3660633	3661579	-
100503874	chr1	3637390	3640590	-
100503874	chr1	3648928	3648985	-
100038431	chr1	3670236	3671869	-
...

The SAF annotation format has five required columns, including GeneID, Chr, Start, End and Strand. These columns can be in any order. More columns can be included in the annotation. Columns are tab-delimited. Column names are case insensitive. GeneID column may contain integers or character strings. Chromosomal names included in the Chr column must match those used inclued in the mapping results, otherwise reads will fail to be assigned. Users may provide a SAF annotation in the form of a data frame or a file using the annot.ext argument.

Users may also provide a GTF/GFF format annotation via annot.ext argument. But GTF/GFF annotation should only be provided as a file, and isGTFAnnotationFile should be set to TRUE when such a annotation is provided. featureCounts function uses the `gene_id' attribute in a GTF/GFF annotation to group features to form meta-features when performing read summarization at meta-feature level. When isPairedEnd is TRUE, fragments (pairs of reads) instead of reads will be counted. featureCounts function checks if reads from the same pair are adjacent to each other (this could happen when reads were for example sorted by their mapping locations), and it automatically reorders those reads that belong to the same pair but are not adjacent to each other in the input read file.

Value

A list with the following components:
counts
a data matrix containing read counts for each feature or meta-feature for each library.
counts_junction (optional)
a data frame including the number of supporting reads for each exon-exon junction, genes that junctions belong to, chromosomal coordinates of splice sites, etc. This component is present only when juncCounts is set to TRUE.
annotation
a data frame with six columns including GeneID, Chr, Start, End and Length. When read summarization was performed at feature level, each row in the data frame is a feature and columns in the data frame give the annotation information for the features. When read summarization was performed at meta-feature level, each row in the data frame is a meta-feature and columns in the data frame give the annotation information for the features included in each meta feature except the Length column. For each meta-feature, the Length column gives the total length of genomic regions covered by features included in that meta-feature. Note that this length will be less than the sum of lengths of features included in the meta-feature when there are features overlapping with each other. Also note the GeneID column gives Entrez gene identifiers when the in-built annotations are used.
targets
a character vector giving sample information.
stat
a data frame giving numbers of unassigned reads and the reasons why they are not assigned (eg. ambiguity, multi-mapping, secondary alignment, mapping quality, fragment length, chimera, read duplicate, non-junction and so on), in addition to the number of successfully assigned reads for each library.

References

Yang Liao, Gordon K Smyth and Wei Shi. featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7):923-30, 2014.

See Also

getInBuiltAnnotation

Aliases
  • featureCounts
Documentation reproduced from package Rsubread, version 1.22.2, License: GPL-3

Community examples

ajr7533@rit.edu at Nov 21, 2019 Rsubread v1.22.2

``` filenames <- c("sample1.bed", "sample2.bed", "sample3.bed", "sample4.bed") #run featureCounts fc <- featureCounts(filenames, annot.ext = gtffile.gtf, isGTFAnnotationFile = TRUE) #view counts table from output head(fc$counts) ```