Explore Workflows

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: e0a30aa1ad516dd2ec0e9ce006428964b840daf4

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

Path: v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: d9ec78aa015755d07a1c60a9d11f1f5f0a012e1f

Packed ID: wf

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: e0a30aa1ad516dd2ec0e9ce006428964b840daf4

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/fastqtosam/fastqtosam_pe.cwl

Branch/Commit ID: b110a23e2efaaadfd4feca4f9e130946d1c5418d

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

Path: definitions/subworkflows/varscan_germline.cwl

Branch/Commit ID: 35e6b3ef71b4a2a9caba1dbd5dc424a8809bcc0a

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: be465ad19b07378f3f863f2c4e0019b420c859f2

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/extract_readgroup_fastq_pe_http.cwl

Branch/Commit ID: ff015418f870bdfbd82ba675eb549fe8b4584b0c

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

Path: tools/collate_unique_SSU_headers.cwl

Branch/Commit ID: 9a72f76a5c8c874278205fcdeb93362a0d8a5152

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

Path: v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: f24c797ea017a467185b516ea4862c9c494c9d33

Packed ID: wf

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

Path: definitions/subworkflows/filter_vcf_nonhuman.cwl

Branch/Commit ID: 480c438a6a7e78c624712aec01bc4214d2bc179c