Explore Workflows

Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs.

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

Path: tools/soupx-subworkflow.cwl

Branch/Commit ID: 8bf36bfad5624fbc8fc315e82783a44e9e5e4470

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 5. Generate BigWig file on the base of sorted BAM file 6. Map input FASTQ file to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 7. Calculate isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; export results to file

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

Path: workflows/trim-rnaseq-se-dutp.cwl

Branch/Commit ID: c602e3cdd72ff904dd54d46ba2b5146eb1c57022

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

Path: workflows/emg-assembly.cwl

Branch/Commit ID: f993cada89d2c6f7480a0d56baa7836a361b1f3a

Generate ATDP heatmap centered on TSS from an array of input BAM files and genelist TSV file. Returns array of heatmap JSON files with the names that have the same basenames as input BAM files, but with .json extension

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

Path: workflows/heatmap.cwl

Branch/Commit ID: 91bb63948c0a264334b9007ef85f936768d90d11

This workflow is intended to be used to test the QC module, without having to run the long waltz step

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/QC/qc_workflow_wo_waltz.cwl

Branch/Commit ID: f09502839916ff50bdd7e1b69f2fc4f17c8416b4

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

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

Path: subworkflows/group-isoforms-batch.cwl

Branch/Commit ID: d6ec0dee61ef65a110e10141bde1a79332a64ab0

Some workflow engines won't stage files in our nested structure, so parse it out here

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

Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05

This is a workflow to go from UMI-tagged fastqs to standard bams. It does not include collapsing, or QC It does include modules 1 and 2

https://github.com/mskcc/ACCESS-Pipeline.git

Path: workflows/standard_pipeline.cwl

Branch/Commit ID: 476f3dcda929ee9eb67391bbc819573d75751b7c

1. Convert input SRA file into pair of upsrtream and downstream FASTQ files (run fastq-dump) 2. Analyze quality of FASTQ files (run fastqc with each of the FASTQ files) 3. If any of the following fields in fastqc generated report is marked as failed for at least one of input FASTQ files: \"Per base sequence quality\", \"Per sequence quality scores\", \"Overrepresented sequences\", \"Adapter Content\", - trim adapters (run trimmomatic) 4. Align original or trimmed FASTQ files to reference genome (run Bowtie2) 5. Sort and index generated by Bowtie2 BAM file (run samtools sort, samtools index) 6. Remove duplicates in sorted BAM file (run picard) 7. Sort and index BAM file after duplicates removing (run samtools sort, samtools index) 8. Count mapped reads number in sorted BAM file (run bamtools stats) 9. Generate genome coverage BED file (run bedtools genomecov) 10. Sort genearted BED file (run sort) 11. Generate genome coverage bigWig file from BED file (run bedGraphToBigWig)

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

Path: workflows/xenbase-chipseq-pe.cwl

Branch/Commit ID: c602e3cdd72ff904dd54d46ba2b5146eb1c57022

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: 91bb63948c0a264334b9007ef85f936768d90d11