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Graph Name Retrieved From View
workflow graph DiffBind spike-in - Differential Binding Analysis of ChIP-Seq or CUTß&RUN/Tag Peak Data with spike-in

Differential Binding Analysis of ChIP-Seq, ATAC-Seq, or CUT&RUN/Tag Peak Data with spike-in --------------------------------------------------- DiffBind processes ChIP-Seq, ATAC-Seq, or CUT&RUN/Tag data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by peak caller tools and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP, ATAC, or CUT&RUN/Tag experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. This specific workflow is designed for experiments that use a spike-in control for each sample. These spike-in reads are used to normalize the datasets during differential analysis using the RLE method (for either edgeR or DESeq) while accounting for background (spike-in). For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind-for-spikein.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16
workflow graph tt_fscr_calls_pass1

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

Path: task_types/tt_fscr_calls_pass1.cwl

Branch/Commit ID: f390475a4e0898d4933f0a28dae278aa35803eb1
workflow graph rnaseq-pe.cwl

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

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

Path: workflows/rnaseq-pe.cwl

Branch/Commit ID: e89b2c17aa5efccef6ca424dec5a0a021bd8d20c
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
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: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
workflow graph RNASelector as a CWL workflow

https://doi.org/10.1007/s12275-011-1213-z

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

Path: workflows/rna-selector.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824
workflow graph extract_gencoll_ids

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 8fb4ac7f5a66897206c7469101a471108b06eada
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 72804b6506c9f54ec75627f82aafe6a28d7a49fa
workflow graph scatter-valuefrom-wf4.cwl#main

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: e8b3565a008d95859fc44227987a54e6a53a8c29

Packed ID: main
workflow graph PCA - Principal Component Analysis

Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f