Explore Workflows

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/scatter-wf3.cwl

Branch/Commit ID: 0e37d46e793e72b7c16b5ec03e22cb3ce1f55ba3

Packed ID: main

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: a409db2289b86779897ff19003bd351701a81c50

https://github.com/CRI-iAtlas/iatlas-workflows.git

Path: Kallisto/workflow/kallisto_scatter_synapse_paired_end_workflow2.cwl

Branch/Commit ID: 3acab4d22ff0f9657dc8c5685799898a2fc2fd25

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: a409db2289b86779897ff19003bd351701a81c50

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-end** 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-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. 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/trim-rnaseq-se.cwl

Branch/Commit ID: 17a4a68b20e0af656e09714c1f39fe761b518686

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v4-single.cwl

Branch/Commit ID: ecf044f3a5a7589cb2238487a19f22863c2bcdb1

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

Path: workflows/hello/exome_alignment_packed.cwl

Branch/Commit ID: 0ae2468ab2ba0b9a196c2aa89b580555750bf0f6

Packed ID: workflow.cwl_2

Pairwise genomic regions intersection ============================================= Overlaps peaks from two ChIP/ATAC experiments

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

Path: workflows/peak-intersect.cwl

Branch/Commit ID: b1a5dabeeeb9079b30b2871edd9c9034a1e00c1c

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3-paired.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 09267e79fd867aa68a219c69e6db7d8e2e877be2