Explore Workflows

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 5b498b4c4f17bb8f17e6886aa4c5661d7aba34fc

https://github.com/EBI-Metagenomics/emg-viral-pipeline.git

Path: cwl/src/Tools/Krona/krona_swf.cwl

Branch/Commit ID: b0ed3f07c8faced85609287759596ad83e154977

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/scatter-wf2_v1_2.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

This workflow downloads the user-selected pre-built kraken2 database from: https://benlangmead.github.io/aws-indexes/k2 ### __Inputs__ Select a pre-built Kraken2 database to download and use for metagenomic classification: - Available options comprised of various combinations of RefSeq reference genome sets: - [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) ### __Outputs__ - k2db, an upstream database used by kraken2 classification tool ### __Data Analysis Steps__ 1. download selected pre-built kraken2 database. 2. make available as upstream source for kraken2 metagenomic taxonomic classification. ### __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-databases.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Input is a fasta file with n>1 samples, with sample id as sequence identifier prefix, and a sample id file. The workflow calls open otus and assigns taxa using greengenes. The output are taxa plots.

https://github.com/MG-RAST/qiime-pipeline.git

Path: CWL/Workflows/qiime/cluster2plot.cwl

Branch/Commit ID: 3565f6f4d26f5709aff56e6a266dbb7c7d6129d8

Cell Ranger Aggregate (RNA, RNA+VDJ) Combines outputs from multiple runs of either “Cell Ranger Count (RNA)” or “Cell Ranger Count (RNA+VDJ)” pipelines. The results of this workflow are primarily used in “Single-Cell RNA-Seq Filtering Analysis” and “Single-Cell Immune Profiling Analysis” pipelines.

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

Path: workflows/cellranger-aggr.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

https://chipster.csc.fi/manual/library-type-summary.html 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-smarter-dutp.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

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

Path: definitions/pipelines/aml_trio_cle.cwl

Branch/Commit ID: 889a077a20c0fdb01f4ed97aa4bc40f920c37a1a

Motif Finding with HOMER with target and background regions from peaks --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-peak.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/step-valuefrom2-wf_v1_1.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9