| Title: | Coverage visualization package for R |
|---|---|
| Description: | This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome |
| Authors: | Ernesto Lowy |
| Maintainer: | Ernesto Lowy <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 1.45.0 |
| Built: | 2024-10-31 05:55:33 UTC |
| Source: | https://github.com/bioc/CoverageView |
Package for the generation of coverage profiles using data originated in different types of NGS experiments
This package provides a framework for the visualization of genome coverage profiles.
It can be used for ChIP-seq experiments, but it can be also used for nucleosome or RNA-seq experiments or whatever type of experiment where one can produce an aligment in the BAM or WIG/BigWIG formats
Ernesto Lowy
Maintainer: Ernesto Lowy <[email protected]>
BAM format specification: http://samtools.sourceforge.net/SAMv1.pdf
WIG format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
This method computes a numeric vector with the result of a certain arithmetic operation with the coverage for two particular BAM or BigWIG files within a specific genomic interval defined by the user
## S4 method for signature 'CoverageBamFile,CoverageBamFile' cov.interval(tr,ctl,normalization,chr,start,end,bin_width,do) ## S4 method for signature 'CoverageBigWigFile,CoverageBigWigFile' cov.interval(tr,ctl,normalization,chr,start,end,bin_width,do)## S4 method for signature 'CoverageBamFile,CoverageBamFile' cov.interval(tr,ctl,normalization,chr,start,end,bin_width,do) ## S4 method for signature 'CoverageBigWigFile,CoverageBigWigFile' cov.interval(tr,ctl,normalization,chr,start,end,bin_width,do)
tr |
An instance of a |
ctl |
An instance of a |
normalization |
Normalize the coverage values in the resulting numeric vector. Possible normalization types are |
chr |
chromosome of the genomic interval |
start |
start coordinate of the genomic interval. If the |
end |
end coordinate of the genomic interval |
bin_width |
calculate the average coverage within bins of size defined by this argument. Default is 1 nucleotide |
do |
specify the arithmetic operation to perform on the genomic interval. Possible values are 'log2ratio','ratio' or 'substraction'.Default= |
The cov.interval function receives 2 CoverageBamFiles or CoverageBigWigFiles objects and returns a numeric vector with the result of doing a certain arithmetic operation with the two files using the coverage values for a certain position or genomic bin that is included in the genomic interval defined by the user using the chr, start and end arguments. The size of the bins is controlled by the bin_width argument and for each bin the average coverage value is computed and used for the different arithmetic operations.The arithmetic operation to perform is set with the do argument.
If the normalization parameter is set to 'rpm' then the number of reads aligned into the reference is required to perform the normalization. This number can be provided using the reads_mapped argument of the CoverageBamFile or CoverageBigWigFile objects, or it is automatically calculated if the function is used with a CoverageBamFile object. NOTE: If the reads_mapped argument is set then the automatic calculation will NOT be performed and the provided number will be taken as correct for all downstream calculations.
Ernesto Lowy <[email protected]>
BAM format specification: http://samtools.sourceforge.net/SAMv1.pdf
WIG format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
##BAM files #get treatment and control test files treat1file<-system.file("extdata","treat.bam",package="CoverageView") control1file<-system.file("extdata","ctrl.bam",package="CoverageView") #create two CoverageBamFile objects representing single-end alignments trm1<-CoverageBamFile(treat1file,run_type="single") ctl1<-CoverageBamFile(control1file,run_type="single") #calculate the ratio of the coverages for the defined genomic interval using a bin_width equal to 10 nts cov1=cov.interval(trm1,ctl1,chr="chrI",start=1,end=100,bin_width=10,do="ratio") #create a WIG file with the obtained vector with the ratios outfolder=system.file("extdata",package="CoverageView") an_outfile1=paste(outfolder,"out.wig",sep="/") export.wig(cov1,outfile=an_outfile1) ##BigWIG files #get a treatment and control test files treat2file<-system.file("extdata","treat.bw",package="CoverageView") control2file<-system.file("extdata","ctrl.bw",package="CoverageView") #create the 'treatment' and 'control' CoverageBigWigFile objects trm2<-CoverageBigWigFile(path=treat2file,reads_mapped=28564510) ctl2<-CoverageBigWigFile(path=control2file,reads_mapped=26713667) #calculate the ratio of the coverages for the defined genomic interval cov2=cov.interval(trm2,ctl2,bin_width=1,chr="chrI",start=1,end=1000,do='ratio')##BAM files #get treatment and control test files treat1file<-system.file("extdata","treat.bam",package="CoverageView") control1file<-system.file("extdata","ctrl.bam",package="CoverageView") #create two CoverageBamFile objects representing single-end alignments trm1<-CoverageBamFile(treat1file,run_type="single") ctl1<-CoverageBamFile(control1file,run_type="single") #calculate the ratio of the coverages for the defined genomic interval using a bin_width equal to 10 nts cov1=cov.interval(trm1,ctl1,chr="chrI",start=1,end=100,bin_width=10,do="ratio") #create a WIG file with the obtained vector with the ratios outfolder=system.file("extdata",package="CoverageView") an_outfile1=paste(outfolder,"out.wig",sep="/") export.wig(cov1,outfile=an_outfile1) ##BigWIG files #get a treatment and control test files treat2file<-system.file("extdata","treat.bw",package="CoverageView") control2file<-system.file("extdata","ctrl.bw",package="CoverageView") #create the 'treatment' and 'control' CoverageBigWigFile objects trm2<-CoverageBigWigFile(path=treat2file,reads_mapped=28564510) ctl2<-CoverageBigWigFile(path=control2file,reads_mapped=26713667) #calculate the ratio of the coverages for the defined genomic interval cov2=cov.interval(trm2,ctl2,bin_width=1,chr="chrI",start=1,end=1000,do='ratio')
This method generates a coverage matrix for which each column is a different genomic feature (i.e. the coordinate of the transcription start site) or genomic interval (defined by start/end coordinates) and each row can be either a position or a bin related to this feature or interval. Each element in the matrix is the result of computing the coverage value for this position/bin that is at a certain distance upstream or dowstream the genomic feature (i.e. the TSS) or it is in the genomic interval
## S4 method for signature 'ANY,missing' cov.matrix(tr,coordfile,normalization,bin_width,extend,no_windows,offset,num_cores) ## S4 method for signature 'ANY,ANY' cov.matrix(tr,ctl,coordfile,normalization,bin_width,extend,no_windows,offset,do,num_cores)## S4 method for signature 'ANY,missing' cov.matrix(tr,coordfile,normalization,bin_width,extend,no_windows,offset,num_cores) ## S4 method for signature 'ANY,ANY' cov.matrix(tr,ctl,coordfile,normalization,bin_width,extend,no_windows,offset,do,num_cores)
tr |
An instance of a |
ctl |
An instance of a |
coordfile |
File in the |
normalization |
Normalize the coverage values in the matrix. Possible normalization types are |
bin_width |
Calculate the average coverage for bins of length defined by this argument. Default is 1 nucleotide |
extend |
Controls the number of nucleotides to extend relative to the anchor coordinates (i.e. TSS) as specified in the |
no_windows |
Number of windows used to divide the interval defined by the start and end coordinates as specified in the |
offset |
If the |
do |
Used to set the arithmetic operation to perform in a certain position or bin. Possible values are: |
num_cores |
Number of cores to use. Default= The |
The cov.matrix function receives one or two CoverageBamFile or CoverageBigWigFile objects and returns a coverage matrix in which every column can be either a single genomic feature (i.e. the coordinates of the transcription start site for a gene) or a certain genomic interval defined by the start and end coordinates specified in the file used with the coordfile argument, and each row represents a different position/bin relative to the feature or a certain window within a genomic interval. This behaviour is controlled by the extend and no_windows parameters. For example, if one set the extend argument, then each row represents a single genomic position (or bin with a a size defined by the bin_width argument) relative to the anchor coordinate. Each matrix element will be the result of calculating the depth of coverage for that particular position/bin relative to the anchor coordinate defined in coordfile. If the bin_width parameter is >1 nt, then the average coverate for each bin is used for all downstream calculations. On the other hand, if the no_windows parameter is set, then each row in the matrix will represent a certain genomic window within an interval defined by the start and end coordinates as specified in the coordfile file. In this case, each element in the matrix will be the average coverage value for that particular genomic window. The number of windows in which the genomic interval is divided is controlled by the no_windows argument. Finally, if only one CoverageBamFile or CoverageBigWigFile is provided using the tr argument, then the matrix elements are the result of computing the coverage value or the average coverage value for a certain position or a certain genomic bin/window respectively. If, on the other hand, two files are provided using the tr and the ctl arguments, then the matrix elements will be the result of performing a certain arithmetic operation (specified with the do argument) with the coverages.
If the normalization parameter is set to 'rpm' then the number of reads aligned into the reference is required to perform the normalization. This number can be provided using the reads_mapped argument of the CoverageBamFile or CoverageBigWigFile objects, or it is automatically calculated if the function is used with a CoverageBamFile object. NOTE: If the reads_mapped argument is set then the automatic calculation will NOT be performed and the provided number will be taken as correct for all downstream calculations
Returns a matrix
Ernesto Lowy <[email protected]>
BAM format specification: http://samtools.sourceforge.net/SAMv1.pdf
WIG format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
BED format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
########### ##BAM files ########### ## Generating a coverage matrix with a single BAM file #get a BAM test file aBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(aBAMfile,reads_mapped=28564510) #generate the coverage matrix extending 100 nts on each side of the provided TSS #in the bedfile DF1=cov.matrix(tr=trm,coordfile=bedfile,extend=100,num_cores=2) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages DF2=cov.matrix(tr=trm,coordfile=bedfile,extend=100,normalization="rpm",num_cores=2) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages. This time we calculate the average #coverage in bins of 2 nucleotides DF3=cov.matrix(tr=trm,coordfile=bedfile,extend=100,normalization="rpm",bin_width=2,num_cores=2) ## Generating a coverage matrix with 2 BAM files #get 2 BAM test files treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create 2 CoverageBamFile objects trm<-CoverageBamFile(treatBAMfile,reads_mapped=28564510) ctl<-CoverageBamFile(ctrlBAMfile,reads_mapped=26713667) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages.The matrix elements are obtained #by computing the ratio of the coverages of the trm against the ctl objects and then #calculating the log2 of the ratios DF4=cov.matrix(tr=trm,ctl=ctl,coordfile=bedfile,extend=100,normalization="rpm",do="log2ratio",num_cores=2) ##################### #get a treatment BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a GFF file with the chr,start and end coordinates of different genomic #features (i.e. CDS) gffile<-system.file("extdata","test.gff",package="CoverageView") #create the 'treatment' CoverageBamFile object trm<-CoverageBamFile(treatBAMfile,reads_mapped=28564510) #generate the coverage matrix dividing each genomic interval defined by the start and #end coordinates in the gff file into 10 windows and calculating the average coverage #for each window DF1=cov.matrix(trm,coordfile=gffile,no_windows=10,num_cores=2) #generate the coverage matrix dividing each genomic interval defined by the start and #end coordinates in the gff file into 10 windows and calculating the average coverage #for each window, this time we extend the genomic interval by 1 window before the start #coordinate and 1 window after the end coordinate (offset argument is set to 1) DF1=cov.matrix(trm,coordfile=gffile,no_windows=10,offset=1) ########### ##BigWIG files ########### ## Generating a coverage matrix with a single WIG file #get a bigWIG test file abigWIGfile<-system.file("extdata","treat.bw",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBigWigFile object trm<-CoverageBigWigFile(abigWIGfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided DF1=cov.matrix(trm,coordfile=bedfile,extend=100,bin_width=10,num_cores=2) ## Generating a coverage matrix with 2 BigWIG files #get 2 BigWIG test files treatBigWIGfile<-system.file("extdata","treat.bw",package="CoverageView") ctrlBigWIGfile<-system.file("extdata","ctrl.bw",package="CoverageView") #create 2 CoverageBigWigFile objects trm<-CoverageBigWigFile(treatBigWIGfile) ctl<-CoverageBigWigFile(ctrlBigWIGfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile .The matrix elements are obtained by computing the ratio of the #coverages of the trm against the ctl objects and then calculating the log2 of the ratios DF2=cov.matrix(tr=trm,ctl=ctl,coordfile=bedfile,extend=100,do="log2ratio",num_cores=2)########### ##BAM files ########### ## Generating a coverage matrix with a single BAM file #get a BAM test file aBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(aBAMfile,reads_mapped=28564510) #generate the coverage matrix extending 100 nts on each side of the provided TSS #in the bedfile DF1=cov.matrix(tr=trm,coordfile=bedfile,extend=100,num_cores=2) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages DF2=cov.matrix(tr=trm,coordfile=bedfile,extend=100,normalization="rpm",num_cores=2) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages. This time we calculate the average #coverage in bins of 2 nucleotides DF3=cov.matrix(tr=trm,coordfile=bedfile,extend=100,normalization="rpm",bin_width=2,num_cores=2) ## Generating a coverage matrix with 2 BAM files #get 2 BAM test files treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create 2 CoverageBamFile objects trm<-CoverageBamFile(treatBAMfile,reads_mapped=28564510) ctl<-CoverageBamFile(ctrlBAMfile,reads_mapped=26713667) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile and normalize the resulting coverages.The matrix elements are obtained #by computing the ratio of the coverages of the trm against the ctl objects and then #calculating the log2 of the ratios DF4=cov.matrix(tr=trm,ctl=ctl,coordfile=bedfile,extend=100,normalization="rpm",do="log2ratio",num_cores=2) ##################### #get a treatment BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a GFF file with the chr,start and end coordinates of different genomic #features (i.e. CDS) gffile<-system.file("extdata","test.gff",package="CoverageView") #create the 'treatment' CoverageBamFile object trm<-CoverageBamFile(treatBAMfile,reads_mapped=28564510) #generate the coverage matrix dividing each genomic interval defined by the start and #end coordinates in the gff file into 10 windows and calculating the average coverage #for each window DF1=cov.matrix(trm,coordfile=gffile,no_windows=10,num_cores=2) #generate the coverage matrix dividing each genomic interval defined by the start and #end coordinates in the gff file into 10 windows and calculating the average coverage #for each window, this time we extend the genomic interval by 1 window before the start #coordinate and 1 window after the end coordinate (offset argument is set to 1) DF1=cov.matrix(trm,coordfile=gffile,no_windows=10,offset=1) ########### ##BigWIG files ########### ## Generating a coverage matrix with a single WIG file #get a bigWIG test file abigWIGfile<-system.file("extdata","treat.bw",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBigWigFile object trm<-CoverageBigWigFile(abigWIGfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided DF1=cov.matrix(trm,coordfile=bedfile,extend=100,bin_width=10,num_cores=2) ## Generating a coverage matrix with 2 BigWIG files #get 2 BigWIG test files treatBigWIGfile<-system.file("extdata","treat.bw",package="CoverageView") ctrlBigWIGfile<-system.file("extdata","ctrl.bw",package="CoverageView") #create 2 CoverageBigWigFile objects trm<-CoverageBigWigFile(treatBigWIGfile) ctl<-CoverageBigWigFile(ctrlBigWIGfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the bedfile .The matrix elements are obtained by computing the ratio of the #coverages of the trm against the ctl objects and then calculating the log2 of the ratios DF2=cov.matrix(tr=trm,ctl=ctl,coordfile=bedfile,extend=100,do="log2ratio",num_cores=2)
"CoverageBamFile"
The CoverageBamFile class contains information on a BAM file and inherits fields from the BamFile class in the Rsamtools package.
path |
A character string that details the path to the BAM file |
reads_mapped |
A number representing the number of reads having an alignment. Default = 0 |
run_type |
A character string describing the type of sequencing run. Possible values are 'single' and 'paired'. Default='single' |
... |
Additional arguments |
Use CoverageBamFile() to create a reference to a BAM file.
This class represents an alignment in the BAM format that can be processed by the different methods of the CoverageView package.
The reads_mapped argument is optional and will be automatically calculated (if it is not provided) when the normalization argument is set in the cov.matrix and cov.interval functions
Ernesto Lowy <[email protected]>
BAM format specification: http://samtools.sourceforge.net/SAMv1.pdf
#get a Bam test file inputfile<-system.file("extdata","treat.bam",package="CoverageView") #create a CoverageBamFile object trm<-CoverageBamFile(inputfile) #create a CoverageBamFile object including the information of the number #of reads aligned and the sequencing run type trm1<-CoverageBamFile(inputfile,reads_mapped=28654321,run_type="single")#get a Bam test file inputfile<-system.file("extdata","treat.bam",package="CoverageView") #create a CoverageBamFile object trm<-CoverageBamFile(inputfile) #create a CoverageBamFile object including the information of the number #of reads aligned and the sequencing run type trm1<-CoverageBamFile(inputfile,reads_mapped=28654321,run_type="single")
"CoverageBigWigFile"
The CoverageBigWigFile class contains information on a BigWIG file and inherits fields from the BigWigFile class in the rtracklayer package.
Objects can be created by calls of the form CoverageBigWigFile(). This class represents an alignment in the BigWIG format for which each genomic position has a depth of coverage value associated.
The reads_mapped argument is optional but it is required if the normalization parameter is set in the cov.matrix and cov.interval functions
resourceA character string that details the path to the BigWIG file
reads_mappedA number representing the number of reads aligned in the file. Default = 0
Additional arguments
Ernesto Lowy <[email protected]>
WIG format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
##get BigWIG test file inputfile<-system.file("extdata","treat.bw",package="CoverageView") #create a CoverageBigWigFile object trm<-CoverageBigWigFile(inputfile) #create a CoverageBigWigFile object including the information of the number of reads aligned trm1<-CoverageBigWigFile(inputfile,reads_mapped=28654321) ##get BigWIG test file inputfile<-system.file("extdata","treat.bw",package="CoverageView") #create a CoverageBigWigFile object trm<-CoverageBigWigFile(inputfile) #create a CoverageBigWigFile object including the information of the number of reads aligned trm1<-CoverageBigWigFile(inputfile,reads_mapped=28654321)##get BigWIG test file inputfile<-system.file("extdata","treat.bw",package="CoverageView") #create a CoverageBigWigFile object trm<-CoverageBigWigFile(inputfile) #create a CoverageBigWigFile object including the information of the number of reads aligned trm1<-CoverageBigWigFile(inputfile,reads_mapped=28654321) ##get BigWIG test file inputfile<-system.file("extdata","treat.bw",package="CoverageView") #create a CoverageBigWigFile object trm<-CoverageBigWigFile(inputfile) #create a CoverageBigWigFile object including the information of the number of reads aligned trm1<-CoverageBigWigFile(inputfile,reads_mapped=28654321)
A coverage matrix calculated using the H3K36me3 ChIP-seq data from the GM12878 cell line. This matrix was computed using only the reads aligned into the Human chromosome 19
data(DF_H3K36me3)data(DF_H3K36me3)
matrix with the normalized (read per million) coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix and setting the no_windows parameter to 100, the normalization parameter to "rpm" and the offset paremeter to 10. The BED format coordinate file was passed to the function through the coordfile argument and contained the genomic coordinates of a set of genes for which the region starting 2500 nucleotides before the transcription start site (TSS) and ending in the transcription end site (TES) contains at least one enrichment peak identified by MACS using the ChIP-seq data. All peaks, regardless its false discovery rate, were considered. For illustrative purposes only the genes located in the chr19 were analyzed.
H3K36me3 ChIP-seq data set for the GM12878 cell line from the Broad Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
A coverage matrix calculated using the control replicate for the H3K36me3 ChIP-seq data from the GM12878 cell line. This matrix was computed using only the reads aligned into the Human chromosome 19
data(DF_H3K36me3_control)data(DF_H3K36me3_control)
matrix with the normalized (read per million) coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix and setting the no_windows parameter to 100, the normalization parameter to "rpm" and the offset paremeter to 10. The BED format coordinate file was passed to the function through the coordfile argument and contained the genomic coordinates of a set of genes for which the region starting 2500 nucleotides before the transcription start site (TSS) and ending in the transcription end site (TES) contains at least one enrichment peak identified by MACS using the ChIP-seq data that was used for generating the other coverage matrix example used in CoverageView (see DF_H3K36me3). For illustrative purposes only the genes located in the chr19 were analyzed.
Control replicate for the H3K36me3 ChIP-seq data set for the GM12878 cell line from the Broad Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
A coverage matrix calculated using the H3K4me3 ChIP-seq data from the K562 cell line. This matrix was computed using only the reads aligned into the Human chromosome 19
data(DF_H3K4me3)data(DF_H3K4me3)
matrix with the normalized (read per million) coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix and setting the no_windows parameter to 100, the normalization parameter to "rpm" and the offset parameter to 10. The BED format coordinate file was passed to the function through the coordfile argument and contained the genomic coordinates of a set of genes for which the region starting 2500 nucleotides before the transcription start site (TSS) and ending in the transcription end site (TES) contains at least one enrichment peak identified by MACS using the ChIP-seq data. All peaks, regardless its false discovery rate, were considered. For illustrative purposes only the genes located in the chr19 were analyzed.
H3K4me3 ChIP-seq data set for the K562 cell line from the HudsonAlpha Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
A coverage matrix calculated using the control replicate for the H3K4me3 ChIP-seq data from the K562 cell line. This matrix was computed using only the reads aligned into the Human chromosome 19
data(DF_H3K4me3_ctl)data(DF_H3K4me3_ctl)
matrix with the normalized (read per million) coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix and setting the no_windows parameter to 100, the normalization parameter to "rpm" and the offset parameter to 10. The BED format coordinate file was passed to the function through the coordfile argument and contained the genomic coordinates of a set of genes for which the region starting 2500 nucleotides before the transcription start site (TSS) and ending in the transcription end site (TES) contains at least one enrichment peak identified by MACS using the ChIP-seq data that was used for generating the other coverage matrix example used in CoverageView (see DF_H3K4me3). For illustrative purposes only the genes located in the chr19 were analyzed.
Control replicate for the H3K4me3 ChIP-seq data set for the cell line from the HudsonAlpha Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
A matrix with the ratio of the coverages calculated using the H3K4me3 ChIP-seq data from the K562 cell line and its respective control replicate. This matrix was computed using only the reads aligned into the Human chromosome 19
data(DF_H3K4me3_nopeaks_ratios)data(DF_H3K4me3_nopeaks_ratios)
matrix with the normalized (read per million) coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix with two CoverageBamFile objects (one sample and one control respectively) and setting the no_windows parameter to 100, the normalization parameter to "rpm" and the offset parameter to 10. The do argument was set to ratio, so each element in the matrix will be the result of calculating the ratio of the coverages between the sample and the control.
The BED format coordinate file was passed to the function through the coordfile argument and contained the genomic coordinates of a set of genes for which the region starting 2500 nucleotides before the transcription start site (TSS) and ending in the transcription end site (TES) did not contain any enrichment peak identified by MACS using the ChIP-seq data. For illustrative purposes only the genes located in the chr19 were analyzed.
H3K4me3 ChIP-seq data set for the K562 cell line from the HudsonAlpha Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
This method draws a heatmap of the coverage values using the matrix that is passed as an argument to the method
## S4 method for signature 'matrix' draw.heatmap(data,outfile, color,...) ## S4 method for signature 'list' draw.heatmap(data,outfile, color,...)## S4 method for signature 'matrix' draw.heatmap(data,outfile, color,...) ## S4 method for signature 'list' draw.heatmap(data,outfile, color,...)
data |
Matrix or list of matrices generated by the function |
outfile |
URL of the |
color |
Graphical argument to set the color of the plot |
... |
Additional parameters |
This function is used to create a coverage heatmap of the matrix/list of matrices generated using the cov.matrix function
Ernesto Lowy <[email protected]>
## draw the heatmap for a coverate matrix that was previously calculated # using the cov.matrix function for a BAM file containing ChIP-seq data # from a H3K36me3 histone modification experiment data(DF_H3K36me3) draw.heatmap(DF_H3K36me3,outfile="testHeatmap.png") ## Now, draw two heatmaps for 2 different coverage matrices previously # obtained for the same H3K36me3 histone modification experiment and its # respective 'control' file data(DF_H3K36me3_control) # create a list with the two matrices input_l=list(DF_H3K36me3,DF_H3K36me3_control) draw.heatmap(input_l,outfile="testHeatmap.png")## draw the heatmap for a coverate matrix that was previously calculated # using the cov.matrix function for a BAM file containing ChIP-seq data # from a H3K36me3 histone modification experiment data(DF_H3K36me3) draw.heatmap(DF_H3K36me3,outfile="testHeatmap.png") ## Now, draw two heatmaps for 2 different coverage matrices previously # obtained for the same H3K36me3 histone modification experiment and its # respective 'control' file data(DF_H3K36me3_control) # create a list with the two matrices input_l=list(DF_H3K36me3,DF_H3K36me3_control) draw.heatmap(input_l,outfile="testHeatmap.png")
This method draws a coverage profile for the coverage matrix that is passed as an argument to the method
## S4 method for signature 'matrix' draw.profile(data,outfile=NULL,...) ## S4 method for signature 'list' draw.profile(data,outfile=NULL,...)## S4 method for signature 'matrix' draw.profile(data,outfile=NULL,...) ## S4 method for signature 'list' draw.profile(data,outfile=NULL,...)
data |
Matrix or list of matrices generated by the function |
outfile |
URL of the |
... |
Additional parameters |
Function to generate a plot in the .png format displaying the profile resulting from calculating the average of each row in the matrix generated by using the cov.matrix function. See the cov.matrix documentation for more details on the matrix used to draw the profile
Ernesto Lowy <[email protected]>
## draw the coverage profile for a coverate matrix that was previously calculated # using the cov.matrix function for a BAM file containing ChIP-seq data # from a H3K36me3 histone modification experiment data(DF_H3K36me3) draw.profile(DF_H3K36me3,ylab="coverage",outfile="testProfile.png",main="testProfile",col="red") ## Now, draw two profiles for 2 different coverage matrices previously # obtained for the same H3K36me3 histone modification experiment and its # respective 'control' file data(DF_H3K36me3_control) #create a list with the two matrices input_l=list(DF_H3K36me3,DF_H3K36me3_control) draw.profile(input_l,ylab="coverage",outfile="testProfile.png",main="testProfile")## draw the coverage profile for a coverate matrix that was previously calculated # using the cov.matrix function for a BAM file containing ChIP-seq data # from a H3K36me3 histone modification experiment data(DF_H3K36me3) draw.profile(DF_H3K36me3,ylab="coverage",outfile="testProfile.png",main="testProfile",col="red") ## Now, draw two profiles for 2 different coverage matrices previously # obtained for the same H3K36me3 histone modification experiment and its # respective 'control' file data(DF_H3K36me3_control) #create a list with the two matrices input_l=list(DF_H3K36me3,DF_H3K36me3_control) draw.profile(input_l,ylab="coverage",outfile="testProfile.png",main="testProfile")
Write the coverage values calculated using the cov.interval function into a WIG format file
## S4 method for signature 'numeric' export.wig(cov,outfile=NULL)## S4 method for signature 'numeric' export.wig(cov,outfile=NULL)
cov |
Numeric vector containing the coverage values generated by the function |
outfile |
Where to write the |
This method is used to write the coverage values calculated by the cov.interval function into a WIG format file.
Ernesto Lowy <[email protected]>
WIG format specification: https://genome.ucsc.edu/FAQ/FAQformat.html
#get treatment and control test files treat1file<-system.file("extdata","treat.bam",package="CoverageView") control1file<-system.file("extdata","ctrl.bam",package="CoverageView") #create two CoverageBamFile objects representing single-end alignments trm1<-CoverageBamFile(treat1file,run_type="single") ctl1<-CoverageBamFile(control1file,run_type="single") #calculate the ratio of the coverages for the defined genomic interval using a bin_width equal to 10 nts cov1=cov.interval(trm1,ctl1,chr="chrI",start=1,end=100,bin_width=10,do="ratio") #create a WIG file with the obtained vector with the ratios outfolder=system.file("extdata",package="CoverageView") an_outfile1=paste(outfolder,"out.wig",sep="/") export.wig(cov1,outfile=an_outfile1)#get treatment and control test files treat1file<-system.file("extdata","treat.bam",package="CoverageView") control1file<-system.file("extdata","ctrl.bam",package="CoverageView") #create two CoverageBamFile objects representing single-end alignments trm1<-CoverageBamFile(treat1file,run_type="single") ctl1<-CoverageBamFile(control1file,run_type="single") #calculate the ratio of the coverages for the defined genomic interval using a bin_width equal to 10 nts cov1=cov.interval(trm1,ctl1,chr="chrI",start=1,end=100,bin_width=10,do="ratio") #create a WIG file with the obtained vector with the ratios outfolder=system.file("extdata",package="CoverageView") an_outfile1=paste(outfolder,"out.wig",sep="/") export.wig(cov1,outfile=an_outfile1)
A coverage matrix calculated using the FoxA1 ChIP-seq data from the T-47D cell line. This matrix was computed using only the reads aligned into the Human chromosome 19
data(FoxA1_chr19)data(FoxA1_chr19)
matrix with the coverages for the genomic interval corresponding to the gene body of a certain set of genes
This matrix was obtained by running the CoverageView function named cov.matrix and setting the extend parameter to 1000 and the bin\_width paremeter to 10. The BED format coordinate file was passed to the function through the coordfile argument and contained the transcription start sites (TSS) of the genes that had a valid MACS peak in the region spanning 2500 nucleotides on each side of the TSS. All peaks, regardless its false discovery rate, were considered. For illustrative purposes only the genes located in the chr19 were analyzed.
FoxA1 ChIP-seq data set for the T-47D cell line from the HudsonAlpha Institute that was used as a test case in the Nature protocol named: Identifying ChIP-seq enrichment using MACS. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Nat Protoc. 2012 Sep;7(9):1728-40.
Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137".
This method generates a plot showing the percentage of the genome covered at different read depths
## S4 method for signature 'CoverageBamFile' genome.covplot.cumdepth(data,outfile,max_depth) ## S4 method for signature 'list' genome.covplot.cumdepth(data,outfile,max_depth)## S4 method for signature 'CoverageBamFile' genome.covplot.cumdepth(data,outfile,max_depth) ## S4 method for signature 'list' genome.covplot.cumdepth(data,outfile,max_depth)
data |
Either an instance of |
outfile |
URL of the |
max_depth |
Maximum read depth to be displayed in the X-axis |
This method receives either a single CoverageBamFile object or a list of CoverageBamFile objects and generates a plot for which the X-axis is a range of cumulative read depths and the Y-axis is the percentage of the genome covered at a certain read depth. If a list of CoverageBamFile objects is passed to the function then it will generate a different coloured line for each of the passed objects
Ernesto Lowy <[email protected]>
##draw a cumulative coverage plot for a test case BAM file #get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create a CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #draw the plot genome.covplot.cumdepth(trm,outfile="test.png") #draw the plot setting the max_depth parameter (30X in this case) genome.covplot.cumdepth(trm,outfile="test.png",max_depth=30) ##draw two overlapping cumulative coverage plots for two different BAM files #get the first BAM file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #get the second BAM test file ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #create the CoverageBamFile object ctl<-CoverageBamFile(ctrlBAMfile) #create a list with the two files input_d=list(trm,ctl) #draw the plot genome.covplot.cumdepth(input_d,outfile="test.png")##draw a cumulative coverage plot for a test case BAM file #get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create a CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #draw the plot genome.covplot.cumdepth(trm,outfile="test.png") #draw the plot setting the max_depth parameter (30X in this case) genome.covplot.cumdepth(trm,outfile="test.png",max_depth=30) ##draw two overlapping cumulative coverage plots for two different BAM files #get the first BAM file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #get the second BAM test file ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #create the CoverageBamFile object ctl<-CoverageBamFile(ctrlBAMfile) #create a list with the two files input_d=list(trm,ctl) #draw the plot genome.covplot.cumdepth(input_d,outfile="test.png")
This method generates a plot showing the number of genomic positions reaching a certain read depth
## S4 method for signature 'CoverageBamFile' genome.covplot.depth(data,outfile,max_depth) ## S4 method for signature 'list' genome.covplot.depth(data,outfile,max_depth)## S4 method for signature 'CoverageBamFile' genome.covplot.depth(data,outfile,max_depth) ## S4 method for signature 'list' genome.covplot.depth(data,outfile,max_depth)
data |
Either an instance of a |
outfile |
URL of the |
max_depth |
Maximum read depth to be displayed in the X-axis |
This method receives either a single CoverageBamFile object or a list of CoverageBamFile objects and generates a plot for which the X-axis represents a range of coverage read depths and the Y-axis corresponds to the number of megabases having a specific read coverage value. If a list of CoverageBamFile objects is passed to the function then it will generate a different coloured line for each of the objects
Ernesto Lowy <[email protected]>
##draw a coverage plot for a test case BAM file #get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #draw the plot genome.covplot.depth(trm,outfile="test.png") #draw the plot setting the max_depth parameter (70X in this case) genome.covplot.depth(trm,outfile="test.png",max_depth=70) ##draw two overlapping coverage plots for two different test BAM files #get a first BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #get a second BAM test file ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #create the CoverageBamFile object ctl<-CoverageBamFile(ctrlBAMfile) #create a list with the two files input_d=list(trm,ctl) #draw the plot genome.covplot.depth(input_d,outfile="test.png")##draw a coverage plot for a test case BAM file #get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #draw the plot genome.covplot.depth(trm,outfile="test.png") #draw the plot setting the max_depth parameter (70X in this case) genome.covplot.depth(trm,outfile="test.png",max_depth=70) ##draw two overlapping coverage plots for two different test BAM files #get a first BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #get a second BAM test file ctrlBAMfile<-system.file("extdata","ctrl.bam",package="CoverageView") #create the CoverageBamFile object ctl<-CoverageBamFile(ctrlBAMfile) #create a list with the two files input_d=list(trm,ctl) #draw the plot genome.covplot.depth(input_d,outfile="test.png")
Write the coverage values calculated using the cov.matrix function into a file
## S4 method for signature 'matrix' write.profile(DF,outfile=NULL)## S4 method for signature 'matrix' write.profile(DF,outfile=NULL)
DF |
Coverage matrix generated by the function |
outfile |
Where to write the |
This method is used to write the matrix containing the coverage values calculated by cov.matrix into a file.
Ernesto Lowy <[email protected]>
#get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the BED file DF1=cov.matrix(trm,coordfile=bedfile,extend=100) #write the coverage matrix into the provided file write.profile(DF1,outfile="DF1.txt")#get a BAM test file treatBAMfile<-system.file("extdata","treat.bam",package="CoverageView") #get a BED file with the TSS (transcription start site) coordinates of some genes bedfile<-system.file("extdata","testTSS.bed",package="CoverageView") #create the CoverageBamFile object trm<-CoverageBamFile(treatBAMfile) #generate the coverage matrix extending 100 nts on each side of the TSS provided #in the BED file DF1=cov.matrix(trm,coordfile=bedfile,extend=100) #write the coverage matrix into the provided file write.profile(DF1,outfile="DF1.txt")