Explore Workflows

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

Path: definitions/pipelines/rnaseq_star_fusion.cwl

Branch/Commit ID: 97572e3a088d79f6a4166385f79e79ea77b11470

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 3. 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 file 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/rnaseq-se-dutp.cwl

Branch/Commit ID: 12e5256de1b680c551c87fd5db6f3bc65428af67

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/fail-unconnected.cwl

Branch/Commit ID: 57baec040c99d7edef8242ef51b5470b1c82d733

GATK4.2.4.1 Mutect2 workflow

https://github.com/nci-gdc/gatk4_mutect2_cwl.git

Path: subworkflows/gatk4.2.4.1_mutect2_workflow.cwl

Branch/Commit ID: 138d484362084dfc97d9fb7d839855b4bc2c5599

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines11-null-step-wf.cwl

Branch/Commit ID: b1d4a69df86350059bd49aa127c02be0c349f7de

Workflow to find hotspot VAFs from duplex (for Tumor sample) and unfiltered (for Normal sample) pileups. These inputs are all required to be sorted in the same order: sample_ids patient_ids sample_classes unfiltered_pileups duplex_pileups

https://github.com/andurill/access-pipeline.git

Path: workflows/subworkflows/find_hotspots_in_normals.cwl

Branch/Commit ID: 3441040dfaecba58150c13a95a6a93657b00778a

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

Path: v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 22490926651174c6cbe01c76c2ded3c9e8d0ee6f

Packed ID: collision

https://github.com/rosafilgueira/dare_seismo.git

Path: specfem3d/specfem3d_test_input_cwl/run_test.cwl

Branch/Commit ID: 1bec173f62a5ba91c938a6f00f49c07af94288c1

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

Path: testdata/stdout-wf_v1_2.cwl

Branch/Commit ID: 124a08ce3389eb49066c34a4163cbbed210a0355

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: 12e5256de1b680c551c87fd5db6f3bc65428af67