Explore Workflows

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

Path: definitions/subworkflows/pindel_region.cwl

Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 093b60e546237c06cfe7820d6ac8d66467e66725

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

Path: testdata/workflow_input_sf_expr_array_v1_2.cwl

Branch/Commit ID: b76b039edb62dea76c43f173848cdc57e4b4aab7

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

Path: testdata/step_valuefrom5_wf_with_id_v1_1.cwl

Branch/Commit ID: b76b039edb62dea76c43f173848cdc57e4b4aab7

Runs RNA-Seq dUTP BioWardrobe basic analysis with strand specific single-end data file.

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

Path: workflows/trim-rnaseq-se-dutp.cwl

Branch/Commit ID: 852fa49a70fe0965de6892fa0832f30b710f0e75

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 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: 93b844a80f4008cc973ea9b5efedaff32a343895

Simulation steps pipeline

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/workflow_same_level.cwl

Branch/Commit ID: 9a0db98839bbc655e12d49f56c61deecd77ff14c

Packed ID: main_pipeline

Simulation of 2 workflows

https://github.com/ILIAD-ocean-twin/application_package.git

Path: workflow_in_workflow/workflow_same_level.cwl

Branch/Commit ID: 9a0db98839bbc655e12d49f56c61deecd77ff14c

Packed ID: second_pipeline

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

Path: definitions/subworkflows/pindel.cwl

Branch/Commit ID: 60edaf6f57eaaf02cda1a3d8cb9a825aa64a43e2

Derive mirror panel alignment parameters from measurements of the optical point-spread functions.

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

Path: workflows/SetMirrorPanelAlignment.cwl

Branch/Commit ID: 789752af87eb190387ff2acb4c95c7a5cdb961e7