Explore Workflows
View already parsed workflows here or click here to add your own
| Graph | Name | Retrieved From | View |
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kmer_seq_entry_extract_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl Branch/Commit ID: 0bc1c33a2293e054ad00974971edc79c13252cc7 |
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kmer_cache_store
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_cache_store.cwl Branch/Commit ID: 3e7a3c1cc1ed5164ae0a51a96f20d7c480d1d70b |
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kmer_ref_compare_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_ref_compare_wnode.cwl Branch/Commit ID: 3e7a3c1cc1ed5164ae0a51a96f20d7c480d1d70b |
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Unaligned bam to sorted, markduped bam
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https://github.com/genome/analysis-workflows.git
Path: definitions/subworkflows/align_sort_markdup.cwl Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5 |
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scatter-valuefrom-wf4.cwl#main
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl Branch/Commit ID: 3ed10d0ea7ac57550433a89a92bdbe756bdb0e40 Packed ID: main |
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ChIP-Seq pipeline paired-end
The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **paired-end** experiment. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. The pipeline produces a sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, coverage by estimated fragments as a BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot. Workflow starts with step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is an unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Depending on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` *samtools\_rmdup* to get rid of duplicated reads. If removing duplicates is not required the original BAM and BAI files are returned. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files without duplicates. Next `macs2 callpeak` performs peak calling *macs2\_callpeak* and the next step reports *macs2\_island\_count* the number of islands and estimated fragment size. If the latter is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). It is also possible to force MACS2 to use pre set fragment size in the first place. Next step (*macs2\_stat*) is used to define which of the islands and estimated fragment size should be used in workflow output: either from *macs2\_island\_count* step or from *macs2\_island\_count\_forced* step. If input trigger of this step is set to True it means that *macs2\_callpeak\_forced* step was run and it returned different from *macs2\_callpeak* step results, so *macs2\_stat* step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from *macs2\_island\_count* step and sufix \"new\" - from *macs2\_island\_count\_forced* step. The following two steps (*bamtools\_stats* and *bam\_to\_bigwig*) are used to calculate coverage from BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it as a BEDgraph file whichis then sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step *get\_stat* is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step *island\_intersect* assigns nearest genes and regions to the islands obtained from *macs2\_callpeak\_forced*. Step *average\_tag\_density* is used to calculate data for average tag density plot from the BAM file. |
https://github.com/datirium/workflows.git
Path: workflows/chipseq-pe.cwl Branch/Commit ID: b5e16e359007150647b14dc6e038f4eb8dccda79 |
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schemadef-wf.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/schemadef-wf.cwl Branch/Commit ID: 5ae5798f1c0c8d2178986b77cfd74edff510877a |
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PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
https://github.com/datirium/workflows.git
Path: workflows/pca.cwl Branch/Commit ID: cc6fa135d04737fdde3b4414d6e214cf8c812f6e |
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wgs alignment with qc
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https://github.com/genome/analysis-workflows.git
Path: definitions/pipelines/alignment_wgs.cwl Branch/Commit ID: 8dc462a7d9ba1479f764682af99c69d8574cb3dc |
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tt_fscr_calls_pass1
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https://github.com/ncbi/pgap.git
Path: task_types/tt_fscr_calls_pass1.cwl Branch/Commit ID: 093b60e546237c06cfe7820d6ac8d66467e66725 |
