Explore Workflows

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Graph Name Retrieved From View
workflow graph umi duplex alignment fastq workflow

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

Path: definitions/pipelines/umi_duplex_alignment.cwl

Branch/Commit ID: d2c2f2eb846ae2e9cdcab46e3bb88e42126cb3f5
workflow graph Bismark Methylation SE

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: 675a3ff982091faf304931e9261aacdbabf51702
workflow graph echo-wf-default.cwl

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

Path: cwltool/schemas/v1.0/v1.0/echo-wf-default.cwl

Branch/Commit ID: b82ce7ae901a54c7a062fd5eefd8d5ceb5a4d684
workflow graph 816_wf.cwl

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

Path: tests/wf/816_wf.cwl

Branch/Commit ID: 55ccde7c2fe3e7899136ce8606a341e292d7050a
workflow graph Motif Finding with HOMER with target and background regions from peaks

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: ebbf23764ede324cabc064bd50647c1f643726fa
workflow graph PGAP Pipeline, simple user input, PGAPX-134

PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)

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

Path: pgap.cwl

Branch/Commit ID: 5461e63dc4714bb81e1c9f58e436c8465107a199
workflow graph kmer_build_tree

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: a7fced3ed8c839272c8f3a8db9da7bc8cd50271f
workflow graph Single-Cell RNA-Seq Trajectory Analysis

Single-Cell RNA-Seq Trajectory Analysis Infers developmental trajectories and pseudotime from cells clustered by similarity of gene expression data.

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

Path: workflows/sc-rna-trajectory.cwl

Branch/Commit ID: 675a3ff982091faf304931e9261aacdbabf51702
workflow graph io-any-wf-1.cwl

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

Path: tests/io-any-wf-1.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2
workflow graph kmer_compare_wnode

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

Path: task_types/tt_kmer_compare_wnode.cwl

Branch/Commit ID: e6fd7898b71a89b667d2eb38f412999920be5902