Explore Workflows

\"Perform pairwise alignment of protein sequences for pairs identified by structural similarity search. Step 1: retrieve sequence from blastdbcmd result Step 2: makeblastdb: ../Tools/14_makeblastdb.cwl Step 3: blastdbcmd: ../Tools/15_blastdbcmd.cwl Step 4: seqretsplit: ../Tools/16_seqretsplit.cwl Step 5: needle (Global alignment): ../Tools/17_needle.cwl Step 6: water (Local alignment): ../Tools/17_water.cwl\"

https://github.com/yonesora56/plant2human.git

Path: Workflow/11_retrieve_sequence_wf.cwl

Branch/Commit ID: 11b46d8d8c0db462edbd2657fc62cf31bc93ecee

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

Path: tests/count-lines14-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: da335d9963418f7bedd84cb2791a0df1b3165ffe

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-pe.cwl

Branch/Commit ID: ebbf23764ede324cabc064bd50647c1f643726fa

This is a placeholder for a missing acceptance workflow.

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

Path: workflows/ValidationWorkflowMissing.cwl

Branch/Commit ID: bf4d4a44a543bcc04f4508ce020751c71550acf5

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

Path: tests/wf/cache_test_workflow.cwl

Branch/Commit ID: dbc4c4c2ad30ed31367b4fbcc3bb4084fdcabaa2

https://github.com/common-workflow-lab/wdl-cwl-translator.git

Path: wdl2cwl/tests/cwl_files/BuildCembaReferences.cwl

Branch/Commit ID: 471bf44b717b69b108fc6d13bc71fac55827d1dd

Reverse the lines in a document, then sort those lines.

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

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: bfe56f3138e9e6fc0b9b8c06447553d4cea03d59

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: c64599f5db2437f9323d41cc3d8d9efb20a2667e

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: 69643d8c15f5357a320aa7e2f6adb2e71302fd20