Explore Workflows

Generates a FASTA file with the DNA sequences for all transcripts in a GFF file and builds kallisto index

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

Path: tools/genome-kallisto-index.cwl

Branch/Commit ID: cf678db8304ffaa20c1d6c854364db5ed41803c2

Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored.

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

Path: tools/group-isoforms-batch.cwl

Branch/Commit ID: dc4ee45ed2c5c30e9a1a173c9ea4445f27d3788a

protein - qc, preprocess, annotation, index, abundance

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/metabarcode-fastq.workflow.cwl

Branch/Commit ID: d9cf22cd615542c94f7974e8bce4cf29b24d985f

What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.

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

Path: workflows/rgt-thor.cwl

Branch/Commit ID: aebf2355539fdf81fd9082616f8b21440d2691c6

Generates indices for bowtie v1.2.0 (12/30/2016).

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: a9551ece898f619167db58e4b74a6cae2d7f7d13

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: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

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: cbefc215d8286447620664fb47076ba5d81aa47f

https://git.astron.nl/RD/LINC.git

Path: workflows/linc_target/prep.cwl

Branch/Commit ID: ee2e8e751a5202b670d6543d932757c00fb3bb03

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/io-union-input-default-wf.cwl

Branch/Commit ID: a5073143db4155e05df8d2e7eb59d9e62acd65a5

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: b174aec5dba5524367061a2c60472c318430f4f5