Explore Workflows

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

Path: subworkflows/allele-process-strain.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c

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

Path: task_types/tt_blastn_wnode.cwl

Branch/Commit ID: 2801ce53744a085580a8de91cd007c45146b51e8

GSEAPY: Gene Set Enrichment Analysis in Python ============================================== Gene Set Enrichment Analysis is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes). GSEA requires as input an expression dataset, which contains expression profiles for multiple samples. While the software supports multiple input file formats for these datasets, the tab-delimited GCT format is the most common. The first column of the GCT file contains feature identifiers (gene ids or symbols in the case of data derived from RNA-Seq experiments). The second column contains a description of the feature; this column is ignored by GSEA and may be filled with “NA”s. Subsequent columns contain the expression values for each feature, with one sample's expression value per column. It is important to note that there are no hard and fast rules regarding how a GCT file's expression values are derived. The important point is that they are comparable to one another across features within a sample and comparable to one another across samples. Tools such as DESeq2 can be made to produce properly normalized data (normalized counts) which are compatible with GSEA. Documents ============================================== - GSEA Home Page: https://www.gsea-msigdb.org/gsea/index.jsp - Results Interpretation: https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Interpreting_GSEA_Results - GSEA User Guide: https://gseapy.readthedocs.io/en/latest/faq.html - GSEAPY Docs: https://gseapy.readthedocs.io/en/latest/introduction.html References ============================================== - Subramanian, Tamayo, et al. (2005, PNAS), https://www.pnas.org/content/102/43/15545 - Mootha, Lindgren, et al. (2003, Nature Genetics), http://www.nature.com/ng/journal/v34/n3/abs/ng1180.html

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

Path: workflows/gseapy.cwl

Branch/Commit ID: 27bee2c853c98af5ce8ace0585b74658adc2e955

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

Path: definitions/pipelines/detect_variants_mouse.cwl

Branch/Commit ID: 449bc7e45bb02316d040f73838ef18359e770268

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

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

Path: subworkflows/heatmap-prepare.cwl

Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b

Workflow to build different indices for different tools from a genome and transcriptome. This workflow expects an (annotated) genome in GBOL ttl format. Steps: - SAPP: rdf2gtf (genome fasta) - SAPP: rdf2fasta (transcripts fasta) - STAR index (Optional for Eukaryotic origin) - bowtie2 index - kallisto index

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_indexbuilder.cwl

Branch/Commit ID: d6893a25b58b9b25fb76c5e060974b54d9eabc41

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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/rnaseq-se-dutp-mitochondrial.cwl

Branch/Commit ID: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d

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

Path: subworkflows/xenbase-sra-to-fastq-se.cwl

Branch/Commit ID: e238d1756f1db35571e84d72e1699e5d1540f10c

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

Path: definitions/subworkflows/umi_alignment.cwl

Branch/Commit ID: 641bdeffd942f5121e19626a094c8633386ad546

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **paired-end** experiment with Trim Galore. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics for both the input FASTQ files, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with running fastx_quality_stats (steps fastx_quality_stats_upstream and fastx_quality_stats_downstream) from FASTX-Toolkit to calculate quality statistics for both upstream and downstream input FASTQ files. At the same time Bowtie is used to align reads from input FASTQ files to reference genome (Step bowtie_aligner). The output of this step is unsorted SAM file which is being sorted and indexed by samtools sort and samtools index (Step samtools_sort_index). Depending on workflow’s input parameters indexed and sorted BAM file could be processed by samtools rmdup (Step samtools_rmdup) to remove all possible read duplicates. In a case when removing duplicates is not necessary the step returns original input BAM and BAI files without any processing. If the duplicates were removed the following step (Step samtools_sort_index_after_rmdup) reruns samtools sort and samtools index with BAM and BAI files, if not - the step returns original unchanged input files. Right after that macs2 callpeak performs peak calling (Step macs2_callpeak). On the base of returned outputs the next step (Step macs2_island_count) calculates the number of islands and estimated fragment size. If the last one is less that 80 (hardcoded in a workflow) macs2 callpeak is rerun again with forced fixed fragment size value (Step macs2_callpeak_forced). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimated fragment size (Step macs2_island_count_forced) for the data obtained from macs2_callpeak_forced step. If the last one was skipped the results from macs2_island_count_forced step are equal to the ones obtained from macs2_island_count step. Next step (Step macs2_stat) is used to define which of the islands and estimated fragment size should be used in workflow output: either from macs2_island_count step or from macs2_island_count_forced step. If input trigger of this step is set to True it means that macs2_callpeak_forced step was run and it returned different from macs2_callpeak step results, so macs2_stat step should return [fragments_new, fragments_old, islands_new], if trigger is False the step returns [fragments_old, fragments_old, islands_old], where sufix \"old\" defines results obtained from macs2_island_count step and sufix \"new\" - from macs2_island_count_forced step. The following two steps (Step bamtools_stats and bam_to_bigwig) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step get_stat is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step island_intersect assigns genes and regions to the islands obtained from macs2_callpeak_forced. Step average_tag_density is used to calculate data for average tag density plot on the base of BAM file.

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

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: 4106b7dc96e968db291b7a61ecd1641aa3b3dd6d