Explore Workflows

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: 5e7385b8cfa4ddae822fff37b6bd22eb0370b389

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: 5e7385b8cfa4ddae822fff37b6bd22eb0370b389

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

Path: definitions/subworkflows/strelka_process_vcf.cwl

Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a

https://github.com/NCI-GDC/gdc-dnaseq-cwl.git

Path: workflows/mirnaseq/metrics.cwl

Branch/Commit ID: 1046947f8d2923e6563b3aceac9e435554c5bea1

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_dispatch/2working_files/workflow.cwl

Branch/Commit ID: 0ae9c1bbf6c3f3ff6286e45c5652853bdbab93fa

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

Path: definitions/subworkflows/qc_exome.cwl

Branch/Commit ID: 18d8efdc4c97c1c9222f603f529b909b36fa42e7

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

Path: definitions/subworkflows/docm_cle.cwl

Branch/Commit ID: 18d8efdc4c97c1c9222f603f529b909b36fa42e7

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

Path: definitions/subworkflows/cnvkit_single_sample.cwl

Branch/Commit ID: 04d21c33a5f2950e86db285fa0a32a6659198d8a

https://github.com/alexbarrera/GGR-cwl.git

Path: workflows/workflows/kallisto-demo.cwl

Branch/Commit ID: 4e568335133405d28f4b73ae11e7f51f2900dfa3

https://github.com/reanahub/reana-demo-worldpopulation.git

Path: workflow/cwl/worldpopulation.cwl

Branch/Commit ID: fbc50661307003a2330434d9a6b1450a0297006c