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workflow graph rmats_wf.cwl

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

Path: workflow/rmats_wf.cwl

Branch/Commit ID: 65161d6565c436a7b1e0b3be56efb433a994ed9d
workflow graph delay-calibration.cwl

https://git.astron.nl/RD/VLBI-cwl.git

Path: workflows/delay-calibration.cwl

Branch/Commit ID: f0006a95724104665eac9a2d6505bf505835dd28
workflow graph BWA index pipeline

This workflow indexes the input reference FASTA with bwa, and generates faidx and dict file using samtools. This index sample can then be used as input into the germline variant calling workflow, or others that may include this workflow as an upstream source. ### __Inputs__ - FASTA file of the reference genome that will be indexed. ### __Outputs__ - Directory containing the original FASTA, faidx, dict, and bwa index files. - stdout log file (output in Overview tab as well) - stderr log file ### __Data Analysis Steps__ 1. cwl calls dockercontainer robertplayer/scidap-gatk4 to index reference FASTA with bwa, and generates faidx and dict files using samtools ### __References__ - Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics, 25(14), 1754–1760.

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

Path: workflows/bwa-index.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e
workflow graph MAnorm SE - quantitative comparison of ChIP-Seq single-read data

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-se.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e
workflow graph FastQC - a quality control tool for high throughput sequence data

FastQC - a quality control tool for high throughput sequence data ===================================== FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are: - Import of data from FastQ files (any variant) - Providing a quick overview to tell you in which areas there may be problems - Summary graphs and tables to quickly assess your data - Export of results to an HTML based permanent report - Offline operation to allow automated generation of reports without running the interactive application

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

Path: workflows/fastqc.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e
workflow graph chipseq-se.cwl

Runs ChIP-Seq BioWardrobe basic analysis with single-end data file.

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

Path: workflows/chipseq-se.cwl

Branch/Commit ID: 7bda675d999a449a911df3a6973c60a39565bc24
workflow graph copy_outputs.cwl

https://github.com/mskcc/argos-cwl.git

Path: workflows/copy_outputs.cwl

Branch/Commit ID: baf246fab1e6d2e6c628377d9c443edbbe4f7838
workflow graph Variant calling germline paired-end

A workflow for the Broad Institute's best practices gatk4 germline variant calling pipeline. ## __Outputs__ #### Primary Output files: - bqsr2_indels.vcf, filtered and recalibrated indels (IGV browser) - bqsr2_snps.vcf, filtered and recalibrated snps (IGV browser) - bqsr2_snps.ann.vcf, filtered and recalibrated snps with effect annotations #### Secondary Output files: - sorted_dedup_reads.bam, sorted deduplicated alignments (IGV browser) - raw_indels.vcf, first pass indel calls - raw_snps.vcf, first pass snp calls #### Reports: - overview.md (input list, alignment metrics, variant counts) - insert_size_histogram.pdf - recalibration_plots.pdf - snpEff_summary.html ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - BWA index: BWA index sample that contains reference genome FASTA with associated indices. - SNPEFF database: Name of SNPEFF database to use for SNP effect annotation. - Read 1 file: First FASTQ file (generally contains \"R1\" in the filename) - Read 2 file: Paired FASTQ file (generally contains \"R2\" in the filename) #### Advanced - Ploidy: number of copies per chromosome (default should be 2) - SNP filters: see Step 6 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ - Indel filters: see Step 7 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ #### SNPEFF notes: Get snpeff databases using `docker run --rm -ti gatk4-dev /bin/bash` then running `java -jar $SNPEFF_JAR databases`. Then, use the first column as SNPEFF input (e.g. \"hg38\"). - hg38, Homo_sapiens (USCS), http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg38.zip - mm10, Mus_musculus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_mm10.zip - dm6.03, Drosophila_melanogaster, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_dm6.03.zip - Rnor_6.0.86, Rattus_norvegicus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_Rnor_6.0.86.zip - R64-1-1.86, Saccharomyces_cerevisiae, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_R64-1-1.86.zip ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. Run germline variant calling pipeline, custom wrapper script implementing Steps 1 - 17 of the Broad Institute's best practices gatk4 germline variant calling pipeline (https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/) ### __References__ 1. https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ 2. https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels- 3. https://software.broadinstitute.org/software/igv/VCF

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

Path: workflows/vc-germline-pe.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e
workflow graph Kallisto index pipeline

This workflow indexes the input reference FASTA with kallisto, and generates a kallisto index file (.kdx). This index sample can then be used as input into the kallisto transcript-level quantification workflow (kallisto-quant-pe.cwl), or others that may include this workflow as an upstream source. ### __Inputs__ - FASTA file of the reference genome that will be indexed - number of threads to use for multithreading processes ### __Outputs__ - kallisto index file (.kdx). - stdout log file (output in Overview tab as well) - stderr log file ### __Data Analysis Steps__ 1. cwl calls dockercontainer robertplayer/scidap-kallisto to index reference FASTA with `kallisto index`, generating a kallisto index file. ### __References__ - Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525-527(2016), doi:10.1038/nbt.3519

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

Path: workflows/kallisto-index.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e
workflow graph Trim Galore RNA-Seq pipeline paired-end

The 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 must be used with paired-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 2 (after running STAR) 5. Generate BigWig file using 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.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e