Explore Workflows
View already parsed workflows here or click here to add your own
Graph | Name | Retrieved From | View |
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tt_blastn_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_blastn_wnode.cwl Branch/Commit ID: af78bfbc7625a817a2875e87c8ee267cf46b8c57 |
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Build STAR indices
Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome. |
https://github.com/datirium/workflows.git
Path: workflows/star-index.cwl Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd |
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paramref_arguments_self.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: tests/wf/paramref_arguments_self.cwl Branch/Commit ID: aec33fcfa3459a90cbba8c88ebb991be94d21429 |
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heatmap-prepare.cwl
Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order. |
https://github.com/datirium/workflows.git
Path: tools/heatmap-prepare.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f |
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directory.cwl
Inspect provided directory and return filenames. Generate a new directory and return it (including content). |
https://github.com/common-workflow-language/cwltool.git
Path: tests/wf/directory.cwl Branch/Commit ID: 2710cfe731374cf7244116dd7186fc2b6e4af344 |
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count-lines9-wf.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/count-lines9-wf.cwl Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9 |
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scatter-valuefrom-wf1.cwl
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https://github.com/common-workflow-language/cwltool.git
Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf1.cwl Branch/Commit ID: e835bc0487fe42fb330b6222c9be65d18dd81ec9 |
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Trim Galore ChIP-Seq pipeline single-read
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 **single-read** experiment with Trim Galore. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, reads coverage in a form of 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 (on the base of BAM file). 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 unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Based 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 input BAM and BAI files return. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files. Right after that `macs2 callpeak` performs peak calling *macs2\_callpeak*. On the base of returned outputs the next step *macs2\_island\_count* calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimates fragment size (*macs2\_island\_count\_forced*) for the data obtained from *macs2\_callpeak\_forced* step. If the last one was skipped the results from *macs2\_island\_count\_forced* step are equal to the ones obtained from *macs2\_island\_count* step. 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 on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being 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 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 on the base of BAM file. |
https://github.com/datirium/workflows.git
Path: workflows/trim-chipseq-se.cwl Branch/Commit ID: 9bf0aa495735f8081bb5870cb32fc898b9e6eb22 |
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GSEApy - Gene Set Enrichment Analysis in Python
GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA. |
https://github.com/datirium/workflows.git
Path: workflows/gseapy.cwl Branch/Commit ID: 8a92669a566589d80fde9d151054ffc220ed4ddd |
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Functional analyis of sequences that match the 16S SSU
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https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git
Path: workflows/16S_taxonomic_analysis.cwl Branch/Commit ID: 3f85843d4a6debdabe96bc800bf2a4efdcda1ef3 |