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Graph Name Retrieved From View
workflow graph Genelists heatmap - RNA-seq expression data visualized

# Genelists heatmap - RNA-seq expression data visualized This visualization workflow takes as input 1 or more genelists derived from the DESeq and/or diffbind workflows along with user-selected samples and visualizes RNA-Seq expression data in a single morpheus heatmap. ### __References__ - Morpheus, https://software.broadinstitute.org/morpheus

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

Path: workflows/genelists-deseq-only.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
workflow graph gp_makeblastdb

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

Path: progs/gp_makeblastdb.cwl

Branch/Commit ID: e668f9c4047f1971ae53040a5af3eccc4bfc3c53
workflow graph Kraken2 Metagenomic pipeline paired-end

This workflow taxonomically classifies paired-end sequencing reads in FASTQ format, that have been optionally adapter trimmed with trimgalore, using Kraken2 and a user-selected pre-built database from a list of [genomic index files](https://benlangmead.github.io/aws-indexes/k2). ### __Inputs__ Kraken2 database for taxonomic classification: - [Viral (0.5 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_viral_20221209.tar.gz), all refseq viral genomes - [MinusB (8.7 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_minusb_20221209.tar.gz), standard minus bacteria (archaea, viral, plasmid, human1, UniVec_Core) - [PlusPFP-16 (15.0 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_pluspfp_16gb_20221209.tar.gz), standard (archaea, bacteria, viral, plasmid, human1, UniVec_Core) + (protozoa, fungi & plant) capped at 16 GB (shrunk via random kmer downselect) - [EuPathDB46 (34.1 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_eupathdb48_20201113.tar.gz), eukaryotic pathogen genomes with contaminants removed (https://veupathdb.org/veupathdb/app) - [16S_gg_13_5 (73 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Greengenes13.5_20200326.tgz), Greengenes 16S rRNA database ([release 13.5](https://greengenes.secondgenome.com/?prefix=downloads/greengenes_database/gg_13_5/), 20200326)\n - [16S_silva_138 (112 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Silva138_20200326.tgz), SILVA 16S rRNA database ([release 138.1](https://www.arb-silva.de/documentation/release-1381/), 20200827) Read 1 file: - FASTA/Q input R1 from a paired end library Read 2 file: - FASTA/Q input R2 from a paired end library Number of threads for steps that support multithreading: - Number of threads for steps that support multithreading - default set to `4` Advanced Inputs Tab (Optional): - Number of bases to clip from the 3p end - Number of bases to clip from the 5p end ### __Outputs__ - k2db, an upstream database used by kraken2 classifier ### __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. (Optional) Clipping of 5' and/or 3' end by the specified number of bases. 4. Mapping reads to primary genome index with Bowtie. ### __References__ - Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0

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

Path: workflows/kraken2-classify-pe.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f
workflow graph extract_gencoll_ids

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 0d0ba0c3410e8aee55c82f077cee31d8ee929b5a
workflow graph mini-ST610106.cwl

https://github.com/Marco-Salvi/dtc61.git

Path: mini-ST610106.cwl

Branch/Commit ID: 11824ff538109de2f553f836903b32c975873adb
workflow graph Salmon quantification, FASTQ -> H5AD count matrix

https://github.com/hubmapconsortium/multiome-rna-atac-pipeline.git

Path: salmon-rnaseq/steps/salmon-quantification.cwl

Branch/Commit ID: 68e0cc1be35751f5ef5958050742ddfffd564d3c
workflow graph PGAP Pipeline, simple user input, PGAPX-134

PGAP pipeline for external usage, powered via containers, simple user input: (FASTA + yaml only, no template)

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

Path: pgap.cwl

Branch/Commit ID: 61eaea2f746c8a1fc2a2b731056b068e28ca4e20
workflow graph Raw sequence data to BQSR

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

Path: definitions/subworkflows/sequence_to_bqsr.cwl

Branch/Commit ID: 2f65fc96207a71b1cda4e246f808bed056608cd0
workflow graph WGS QC workflow mouse

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

Path: definitions/subworkflows/qc_wgs_mouse.cwl

Branch/Commit ID: c6bbd4cdd612b3b5cc6e9000df4800c21e192bf5
workflow graph THOR - differential peak calling of ChIP-seq signals with replicates

What is THOR? -------------- THOR is an HMM-based approach to detect and analyze differential peaks in two sets of ChIP-seq data from distinct biological conditions with replicates. THOR performs genomic signal processing, peak calling and p-value calculation in an integrated framework. For more information please refer to: ------------------------------------- Allhoff, M., Sere K., Freitas, J., Zenke, M., Costa, I.G. (2016), Differential Peak Calling of ChIP-seq Signals with Replicates with THOR, Nucleic Acids Research, epub gkw680.

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

Path: workflows/rgt-thor.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16