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workflow graph chipseq-pe.cwl

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/chipseq-pe.cwl

Branch/Commit ID: 568da91bb1c6182ba4f146e2a729cac1c3d8783c
workflow graph Trim Galore RNA-Seq pipeline paired-end strand specific

Modified original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe-dutp.cwl

Branch/Commit ID: 29bf638904709cfbf10908adcd51ba4886ace94a
workflow graph kfdrc_alignment_wf.cwl

https://github.com/kids-first/kf-alignment-workflow.git

Path: workflows/kfdrc_alignment_wf.cwl

Branch/Commit ID: 0e35fb09e15a7a6af52d9906111136a961b2c488
workflow graph Run genomic CMsearch (Rfam rRNA)

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

Path: bacterial_ncrna/wf_gcmsearch.cwl

Branch/Commit ID: 424a01693259a75641dc249d553235aa38a6ce23
workflow graph Trim Galore SMARTer RNA-Seq pipeline paired-end strand specific

https://chipster.csc.fi/manual/library-type-summary.html Modified original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe-smarter-dutp.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph trim-rnaseq-pe-dutp.cwl

Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file.

https://github.com/Barski-lab/workflows.git

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

Branch/Commit ID: 94f3486d3867b9ece47c07bc8fe47f57569786f4
workflow graph bam-bedgraph-bigwig.cwl

Workflow converts input BAM file into bigWig and bedGraph files. Input BAM file should be sorted by coordinates (required by `bam_to_bedgraph` step). If `split` input is not provided use true by default. Default logic is implemented in `valueFrom` field of `split` input inside `bam_to_bedgraph` step to avoid possible bug in cwltool with setting default values for workflow inputs. `scale` has higher priority over the `mapped_reads_number`. The last one is used to calculate `-scale` parameter for `bedtools genomecov` (step `bam_to_bedgraph`) only in a case when input `scale` is not provided. All logic is implemented inside `bedtools-genomecov.cwl`. `bigwig_filename` defines the output name only for generated bigWig file. `bedgraph_filename` defines the output name for generated bedGraph file and can influence on generated bigWig filename in case when `bigwig_filename` is not provided. All workflow inputs and outputs don't have `format` field to avoid format incompatibility errors when workflow is used as subworkflow.

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

Path: tools/bam-bedgraph-bigwig.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph Cell Ranger Reference (RNA, ATAC, RNA+ATAC)

Cell Ranger Reference (RNA, ATAC, RNA+ATAC) Builds a reference genome of a selected species for quantifying gene expression and chromatin accessibility. The results of this workflow are used in all “Cell Ranger Count” and “Cell Ranger Aggregate” pipelines.

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

Path: workflows/cellranger-mkref.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph Motif Finding with HOMER with random background regions

Motif Finding with HOMER with random background regions --------------------------------------------------- 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. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background 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.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895
workflow graph Single-Cell RNA-Seq Differential Expression Analysis

Single-Cell RNA-Seq Differential Expression Analysis Identifies differentially expressed genes between any two groups of cells, optionally aggregating gene expression data from single-cell to pseudobulk form.

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

Path: workflows/sc-rna-de-pseudobulk.cwl

Branch/Commit ID: 93b844a80f4008cc973ea9b5efedaff32a343895