{"@id":"https://w3id.org/cwl/view/git/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","retrievedFrom":{"repoUrl":"https://github.com/alexbarrera/GGR-cwl.git","branch":"81555a89cc2d16bd3ab131a6685a44b44f08f927","path":"v1.0/ATAC-seq_pipeline/05-quantification.cwl","packedId":null,"type":"GITHUB","rawUrl":"https://raw.githubusercontent.com/alexbarrera/GGR-cwl/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","url":"https://github.com/alexbarrera/GGR-cwl/blob/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl"},"retrievedOn":"2026-09-22T15:31:14.090+00:00","lastCommit":"81555a89cc2d16bd3ab131a6685a44b44f08f927","label":"05-quantification.cwl","doc":"ATAC-seq - Quantification","inputs":{"nthreads":{"type":"Integer"},"input_bam_files":{"type":"File[]"},"input_genome_sizes":{"type":"File"}},"outputs":{"bigwig_raw_files":{"doc":"Raw reads bigWig (signal) files","type":"File[]"},"bigwig_norm_files":{"doc":"signal files of pileup reads in RPKM","type":"File[]"}},"steps":{"bdg2bw-raw":{"doc":"Tool:   bedGraphToBigWig v 4 - Convert a bedGraph file to bigWig format.","run":"../quant/bedGraphToBigWig.cwl","runType":"COMMANDLINETOOL","sources":{"bdg2bw-raw":{"sourceIDs":["input_genome_sizes"]},"https://w3id.org/cwl/view/git/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl#bdg2bw-raw/bed_graph":{"sourceIDs":["bedsort_genomecov"]}}},"bamcoverage":{"doc":"usage: An example usage is:$ bamCoverage -b reads.bam -o coverage.bw\n\nThis tool takes an alignment of reads or fragments as input (BAM file) and\ngenerates a coverage track (bigWig or bedGraph) as output. The coverage is\ncalculated as the number of reads per bin, where bins are short consecutive\ncounting windows of a defined size. It is possible to extended the length of\nthe reads to better reflect the actual fragment length. *bamCoverage* offers\nnormalization by scaling factor, Reads Per Kilobase per Million mapped reads\n(RPKM), and 1x depth (reads per genome coverage, RPGC).\nRequired arguments:\n  --bam BAM file, -b BAM file\n                        BAM file to process (default: None)\nOutput:\n  --outFileName FILENAME, -o FILENAME\n                        Output file name. (default: None)\n  --outFileFormat {bigwig,bedgraph}, -of {bigwig,bedgraph}\n                        Output file type. Either \\\"bigwig\\\" or \\\"bedgraph\\\".\n                        (default: bigwig)\nOptional arguments:\n  --help, -h            show this help message and exit\n  --scaleFactor SCALEFACTOR\n                        The smooth length defines a window, larger than the\n                        binSize, to average the number of reads. For example,\n                        if the –binSize is set to 20 and the –smoothLength is\n                        set to 60, then, for each bin, the average of the bin\n                        and its left and right neighbors is considered.\n                        Any value smaller than –binSize will be ignored and\n                        no smoothing will be applied. (default: 1.0)\n  --MNase               Determine nucleosome positions from MNase-seq data.\n                        Only 3 nucleotides at the center of each fragment are\n                        counted. The fragment ends are defined by the two mate\n                        reads. Only fragment lengthsbetween 130 - 200 bp are\n                        considered to avoid dinucleosomes or other\n                        artifacts.*NOTE*: Requires paired-end data. A bin size\n                        of 1 is recommended. (default: False)\n  --filterRNAstrand {forward,reverse}\n                        Selects RNA-seq reads (single-end or paired-end) in\n                        the given strand. (default: None)\n  --version             show program's version number and exit\n  --binSize INT bp, -bs INT bp\n                        Size of the bins, in bases, for the output of the\n                        bigwig/bedgraph file. (default: 50)\n  --region CHR:START:END, -r CHR:START:END\n                        Region of the genome to limit the operation to - this\n                        is useful when testing parameters to reduce the\n                        computing time. The format is chr:start:end, for\n                        example --region chr10 or --region\n                        chr10:456700:891000. (default: None)\n  --blackListFileName BED file, -bl BED file\n                        A BED file containing regions that should be excluded\n                        from all analyses. Currently this works by rejecting\n                        genomic chunks that happen to overlap an entry.\n                        Consequently, for BAM files, if a read partially\n                        overlaps a blacklisted region or a fragment spans over\n                        it, then the read/fragment might still be considered.\n                        (default: None)\n  --numberOfProcessors INT, -p INT\n                        Number of processors to use. Type \\\"max/2\\\" to use half\n                        the maximum number of processors or \\\"max\\\" to use all\n                        available processors. (default: max/2)\n  --verbose, -v         Set to see processing messages. (default: False)\nRead coverage normalization options:\n  --normalizeTo1x EFFECTIVE GENOME SIZE LENGTH\n                        Report read coverage normalized to 1x sequencing depth\n                        (also known as Reads Per Genomic Content (RPGC)).\n                        Sequencing depth is defined as: (total number of\n                        mapped reads * fragment length) / effective genome\n                        size. The scaling factor used is the inverse of the\n                        sequencing depth computed for the sample to match the\n                        1x coverage. To use this option, the effective genome\n                        size has to be indicated after the option. The\n                        effective genome size is the portion of the genome\n                        that is mappable. Large fractions of the genome are\n                        stretches of NNNN that should be discarded. Also, if\n                        repetitive regions were not included in the mapping of\n                        reads, the effective genome size needs to be adjusted\n                        accordingly. Common values are: mm9: 2,150,570,000;\n                        hg19:2,451,960,000; dm3:121,400,000 and\n                        ce10:93,260,000. See Table 2 of http://www.plosone.org\n                        /article/info:doi/10.1371/journal.pone.0030377 or http\n                        ://www.nature.com/nbt/journal/v27/n1/fig_tab/nbt.1518_\n                        T1.html for several effective genome sizes. (default:\n                        None)\n--ignoreForNormalization IGNOREFORNORMALIZATION [IGNOREFORNORMALIZATION ...]\n                        A list of space-delimited chromosome names containing\n                        those chromosomes that should be excluded for\n                        computing the normalization. This is useful when\n                        considering samples with unequal coverage across\n                        chromosomes, like male samples. An usage examples is\n                        --ignoreForNormalization chrX chrM. (default: None)\n  --skipNonCoveredRegions, --skipNAs\n                        This parameter determines if non-covered regions\n                        (regions without overlapping reads) in a BAM file\n                        should be skipped. The default is to treat those\n                        regions as having a value of zero. The decision to\n                        skip non-covered regions depends on the interpretation\n                        of the data. Non-covered regions may represent, for\n                        example, repetitive regions that should be skipped.\n                        (default: False)\n  --smoothLength INT bp\n                        The smooth length defines a window, larger than the\n                        binSize, to average the number of reads. For example,\n                        if the --binSize is set to 20 and the --smoothLength\n                        is set to 60, then, for each bin, the average of the\n                        bin and its left and right neighbors is considered.\n                        Any value smaller than --binSize will be ignored and\n                        no smoothing will be applied. (default: None)\nRead processing options:\n  --extendReads [INT bp], -e [INT bp]\n                        This parameter allows the extension of reads to\n                        fragment size. If set, each read is extended, without\n                        exception. *NOTE*: This feature is generally NOT\n                        recommended for spliced-read data, such as RNA-seq, as\n                        it would extend reads over skipped regions. *Single-\n                        end*: Requires a user specified value for the final\n                        fragment length. Reads that already exceed this\n                        fragment length will not be extended. *Paired-end*:\n                        Reads with mates are always extended to match the\n                        fragment size defined by the two read mates. Unmated\n                        reads, mate reads that map too far apart (>4x fragment\n                        length) or even map to different chromosomes are\n                        treated like single-end reads. The input of a fragment\n                        length value is optional. If no value is specified, it\n                        is estimated from the data (mean of the fragment size\n                        of all mate reads). (default: False)\n  --ignoreDuplicates    If set, reads that have the same orientation and start\n                        position will be considered only once. If reads are\n                        paired, the mate's position also has to coincide to\n                        ignore a read. (default: False)\n  --minMappingQuality INT\n                        If set, only reads that have a mapping quality score\n                        of at least this are considered. (default: None)\n  --centerReads         By adding this option, reads are centered with respect\n                        to the fragment length. For paired-end data, the read\n                        is centered at the fragment length defined by the two\n                        ends of the fragment. For single-end data, the given\n                        fragment length is used. This option is useful to get\n                        a sharper signal around enriched regions. (default:\n                        False)\n  --samFlagInclude INT  Include reads based on the SAM flag. For example, to\n                        get only reads that are the first mate, use a flag of\n                        64. This is useful to count properly paired reads only\n                        once, as otherwise the second mate will be also\n                        considered for the coverage. (default: None)\n  --samFlagExclude INT  Exclude reads based on the SAM flag. For example, to\n                        get only reads that map to the forward strand, use\n                        --samFlagExclude 16, where 16 is the SAM flag for\n                        reads that map to the reverse strand. (default: None)\n","run":"../quant/deeptools-bamcoverage.cwl","runType":"COMMANDLINETOOL","sources":{"bamcoverage":{"sourceIDs":["nthreads"]},"https://w3id.org/cwl/view/git/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl#bamcoverage/bam":{"sourceIDs":["input_bam_files"]}}},"bedsort_genomecov":{"doc":"bedSort - Sort a .bed file by chrom,chromStart\nusage:\n   bedSort in.bed out.bed\nin.bed and out.bed may be the same.\n","run":"../quant/bedSort.cwl","runType":"COMMANDLINETOOL","sources":{"bedsort_genomecov":{"sourceIDs":["bedtools_genomecov"]}}},"bedtools_genomecov":{"doc":"Tool:    bedtools genomecov (aka genomeCoverageBed)\nVersion: v2.25.0\nSummary: Compute the coverage of a feature file among a genome.\n\nUsage: bedtools genomecov [OPTIONS] -i <bed/gff/vcf> -g <genome>\n\nOptions: \n\t-ibam\t\tThe input file is in BAM format.\n\t\t\tNote: BAM _must_ be sorted by position\n\n\t-d\t\tReport the depth at each genome position (with one-based coordinates).\n\t\t\tDefault behavior is to report a histogram.\n\n\t-dz\t\tReport the depth at each genome position (with zero-based coordinates).\n\t\t\tReports only non-zero positions.\n\t\t\tDefault behavior is to report a histogram.\n\n\t-bg\t\tReport depth in BedGraph format. For details, see:\n\t\t\tgenome.ucsc.edu/goldenPath/help/bedgraph.html\n\n\t-bga\t\tReport depth in BedGraph format, as above (-bg).\n\t\t\tHowever with this option, regions with zero \n\t\t\tcoverage are also reported. This allows one to\n\t\t\tquickly extract all regions of a genome with 0 \n\t\t\tcoverage by applying: \\\"grep -w 0$\\\" to the output.\n\n\t-split\t\tTreat \\\"split\\\" BAM or BED12 entries as distinct BED intervals.\n\t\t\twhen computing coverage.\n\t\t\tFor BAM files, this uses the CIGAR \\\"N\\\" and \\\"D\\\" operations \n\t\t\tto infer the blocks for computing coverage.\n\t\t\tFor BED12 files, this uses the BlockCount, BlockStarts, and BlockEnds\n\t\t\tfields (i.e., columns 10,11,12).\n\n\t-strand\t\tCalculate coverage of intervals from a specific strand.\n\t\t\tWith BED files, requires at least 6 columns (strand is column 6). \n\t\t\t- (STRING): can be + or -\n\n\t-5\t\tCalculate coverage of 5\\\" positions (instead of entire interval).\n\n\t-3\t\tCalculate coverage of 3\\\" positions (instead of entire interval).\n\n\t-max\t\tCombine all positions with a depth >= max into\n\t\t\ta single bin in the histogram. Irrelevant\n\t\t\tfor -d and -bedGraph\n\t\t\t- (INTEGER)\n\n\t-scale\t\tScale the coverage by a constant factor.\n\t\t\tEach coverage value is multiplied by this factor before being reported.\n\t\t\tUseful for normalizing coverage by, e.g., reads per million (RPM).\n\t\t\t- Default is 1.0; i.e., unscaled.\n\t\t\t- (FLOAT)\n\n\t-trackline\tAdds a UCSC/Genome-Browser track line definition in the first line of the output.\n\t\t\t- See here for more details about track line definition:\n\t\t\t      http://genome.ucsc.edu/goldenPath/help/bedgraph.html\n\t\t\t- NOTE: When adding a trackline definition, the output BedGraph can be easily\n\t\t\t      uploaded to the Genome Browser as a custom track,\n\t\t\t      BUT CAN NOT be converted into a BigWig file (w/o removing the first line).\n\n\t-trackopts\tWrites additional track line definition parameters in the first line.\n\t\t\t- Example:\n\t\t\t   -trackopts 'name=\\\"My Track\\\" visibility=2 color=255,30,30'\n\t\t\t   Note the use of single-quotes if you have spaces in your parameters.\n\t\t\t- (TEXT)\n\nNotes: \n\t(1) The genome file should tab delimited and structured as follows:\n\t <chromName><TAB><chromSize>\n\n\tFor example, Human (hg19):\n\tchr1\t249250621\n\tchr2\t243199373\n\t...\n\tchr18_gl000207_random\t4262\n\n\t(2) The input BED (-i) file must be grouped by chromosome.\n\t A simple \\\"sort -k 1,1 <BED> > <BED>.sorted\\\" will suffice.\n\n\t(3) The input BAM (-ibam) file must be sorted by position.\n\t A \\\"samtools sort <BAM>\\\" should suffice.\n\nTips: \n\tOne can use the UCSC Genome Browser's MySQL database to extract\n\tchromosome sizes. For example, H. sapiens:\n\n\tmysql --user=genome --host=genome-mysql.cse.ucsc.edu -A -e \\\n\t\\\"select chrom, size from hg19.chromInfo\\\" > hg19.genome","run":"../map/bedtools-genomecov.cwl","runType":"COMMANDLINETOOL","sources":{"bedtools_genomecov":{"sourceIDs":["input_bam_files"]},"https://w3id.org/cwl/view/git/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl#bedtools_genomecov/g":{"sourceIDs":["input_genome_sizes"]}}}},"cwltoolVersion":"3.1.20230201224320","visualisationDot":"digraph workflow {\n  graph [\n    bgcolor = \"#eeeeee\"\n    color = \"black\"\n    fontsize = \"10\"\n    labeljust = \"left\"\n    clusterrank = \"local\"\n    ranksep = \"0.22\"\n    nodesep = \"0.05\"\n  ]\n  node [\n    fontname = \"Helvetica\"\n    fontsize = \"10\"\n    fontcolor = \"black\"\n    shape = \"record\"\n    height = \"0\"\n    width = \"0\"\n    color = \"black\"\n    fillcolor = \"lightgoldenrodyellow\"\n    style = \"filled\"\n  ];\n  edge [\n    fontname=\"Helvetica\"\n    fontsize=\"8\"\n    fontcolor=\"black\"\n    color=\"black\"\n    arrowsize=\"0.7\"\n  ];\n  subgraph cluster_inputs {\n    rank = \"same\";\n    style = \"dashed\";\n    label = \"Workflow Inputs\";\n    \"input_bam_files\" [fillcolor=\"#94DDF4\",label=\"input_bam_files\"];\n    \"input_genome_sizes\" [fillcolor=\"#94DDF4\",label=\"input_genome_sizes\"];\n    \"nthreads\" [fillcolor=\"#94DDF4\",label=\"nthreads\"];\n  }\n  subgraph cluster_outputs {\n    rank = \"same\";\n    style = \"dashed\";\n    labelloc = \"b\";\n    label = \"Workflow Outputs\";\n    \"bigwig_raw_files\" [fillcolor=\"#94DDF4\",label=\"bigwig_raw_files\"];\n    \"bigwig_norm_files\" [fillcolor=\"#94DDF4\",label=\"bigwig_norm_files\"];\n  }\n  \"bamcoverage\" [label=\"bamcoverage\"];\n  \"bdg2bw-raw\" [label=\"bdg2bw-raw\"];\n  \"bedtools_genomecov\" [label=\"bedtools_genomecov\"];\n  \"bedsort_genomecov\" [label=\"bedsort_genomecov\"];\n  \"nthreads\" -> \"bamcoverage\" [label=\"numberOfProcessors\"];\n  \"input_bam_files\" -> \"bamcoverage\" [label=\"bam\"];\n  \"input_genome_sizes\" -> \"bdg2bw-raw\" [label=\"genome_sizes\"];\n  \"bedsort_genomecov\" -> \"bdg2bw-raw\" [label=\"bed_graph\"];\n  \"input_bam_files\" -> \"bedtools_genomecov\" [label=\"ibam\"];\n  \"input_genome_sizes\" -> \"bedtools_genomecov\" [label=\"g\"];\n  \"bedtools_genomecov\" -> \"bedsort_genomecov\" [label=\"bed_file\"];\n  \"bdg2bw-raw\" -> \"bigwig_raw_files\";\n  \"bamcoverage\" -> \"bigwig_norm_files\";\n}\n","licenseLink":"https://spdx.org/licenses/MIT","permalink":"https://w3id.org/cwl/view/git/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","packed":false,"visualisationXdot":"/graph/xdot/github.com/alexbarrera/GGR-cwl/blob/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","visualisationPng":"/graph/png/github.com/alexbarrera/GGR-cwl/blob/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","visualisationSvg":"/graph/svg/github.com/alexbarrera/GGR-cwl/blob/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","roBundle":"/robundle/github.com/alexbarrera/GGR-cwl/blob/81555a89cc2d16bd3ab131a6685a44b44f08f927/v1.0/ATAC-seq_pipeline/05-quantification.cwl","licenseName":"MIT License"}