Explore Workflows

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: d1bef74924efcb8bfaa00987b3f148d5a192b7a9

https://github.com/MaastrichtU-IDS/bio2rdf.git

Path: support/aynec-fb13-a/virtuoso-workflow/workflow.cwl

Branch/Commit ID: ffc33a6db55c50d1dd7b766b687e448249820df9

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

Path: v1.0/v1.0/output-arrays-file-wf.cwl

Branch/Commit ID: 40fcfc01812046f012acf5153cc955ee848e69e3

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

Path: definitions/subworkflows/varscan.cwl

Branch/Commit ID: ffd73951157c61c1581d346628d75b61cdd04141

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

Path: task_types/tt_blastp_wnode_naming.cwl

Branch/Commit ID: dcbbce152fbff2637f102471fb80318244492853

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

Path: v1.0/v1.0/count-lines2-wf.cwl

Branch/Commit ID: 148f11b11d31c098196e649f680797f0b4680114

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: d1bef74924efcb8bfaa00987b3f148d5a192b7a9

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

Path: tools/5S-from-tablehits.cwl

Branch/Commit ID: cac44f2cf14110fde9951161c663c4525772f616

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

Path: definitions/subworkflows/vcf_readcount_annotator.cwl

Branch/Commit ID: 038cb3617a1966a1057386adcde97ce55d9e1139

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

Path: definitions/subworkflows/bam_readcount.cwl

Branch/Commit ID: eb0092603bf57acb7bda08a06e4f2f1e2a8c9b6d