Explore Workflows

View already parsed workflows here or click here to add your own

Graph Name Retrieved From View
workflow graph adapter for sequence_align_and_tag

Some workflow engines won't stage files in our nested structure, so parse it out here

 https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e
workflow graph assm_assm_blastn_wnode

https://github.com/ncbi/pgap.git

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: f5c11df465aaadf712c38ba4933679fe1cbe03ca
workflow graph kmer_seq_entry_extract_wnode

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph 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: 10ce6e113f749c7bd725e426445220c3bdc5ddf1
workflow graph Conversion and compression of RDF files

Workflow to convert a RDF file to the HDT format and GZIP compress it for long term storage

 https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_toHDT_compression.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0
workflow graph running cellranger mkfastq and count

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/cellranger_mkfastq_and_count.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
workflow graph RNA-Seq pipeline single-read

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-read** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

 https://github.com/datirium/workflows.git

Path: workflows/rnaseq-se.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca
workflow graph tt_hmmsearch_wnode.cwl

https://github.com/ncbi/pgap.git

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: 146df33e2e44afa2a608ac72c036e6b6b871af93
workflow graph Bismark Methylation - pipeline for BS-Seq data analysis

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

 https://github.com/datirium/workflows.git

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: 1131f82a53315cca217a6c84b3bd272aa62e4bca
workflow graph host.sort.workflow.cwl

https://github.com/azzaea/cwl-scalability-vis.git

Path: host.sort.workflow.cwl

Branch/Commit ID: d79d09f92c64e25d1a68af7e8aed5e48e73e6537