Explore Workflows

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: 3b6d0475c80f5e452793a46a38ee188742b86595

https://github.com/sfu-ireceptor/AIRR-seqAA.git

Path: cwl/id_to_json_workflow.cwl

Branch/Commit ID: d02d2e42ee53055708b6ea0a921a34a2ca77cbf6

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 2d54b11cc9891c9aa52515fe4f8cd9cba12c6629

https://github.com/common-workflow-language/common-workflow-language.git

Path: v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 4fe434e969c93c94b690ba72db295d9d52a6f576

Packed ID: main

https://github.com/Barski-lab/workflows.git

Path: subworkflows/xenbase-sra-to-fastq-se.cwl

Branch/Commit ID: 378f693ebfb3edf9f589007e366fec1195ec1464

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

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: 4aba7c6591c2f1ebd827a36d325a58738c429bea

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: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df

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

Path: definitions/subworkflows/sequence_to_bqsr_mouse.cwl

Branch/Commit ID: adcae308fdccaa1190083616118dfadb4df65dca

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: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df

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

Path: bacterial_noncoding/wf_gcmsearch.cwl

Branch/Commit ID: 7cee09fb3e33c851e4e1dfc965c558b82290a785