Explore Workflows

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/samtools_mpileup_subpipeline.cwl

Branch/Commit ID: d919f2dd335da64a4fa352df9ea1b27ba13edad8

Workflow to run the MSI analysis on a batch of samples and merge the results back into a single data clinical file

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

Path: cwl/msi_workflow.cwl

Branch/Commit ID: 5cad957fec135aa55ca8d588372db0557ca1cad5

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

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada

Single-Cell ATAC-Seq Genome Coverage Generates genome coverage tracks from chromatin accessibility data of selected cells

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

Path: workflows/sc-atac-coverage.cwl

Branch/Commit ID: 3a311af320e65271f3efb4f27a6ed10aa5d50a0e

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

Path: workflow/rmats_wf.cwl

Branch/Commit ID: 65161d6565c436a7b1e0b3be56efb433a994ed9d

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

Path: workflows/delay-calibration.cwl

Branch/Commit ID: f0006a95724104665eac9a2d6505bf505835dd28

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

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

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