Explore Workflows

https://github.com/NCI-GDC/gdc_tosvc_workflow.git

Path: gdc-purecn-tumor-only-filtration-cwl/gpas_purecn_tumor_only_filtration.cwl

Branch/Commit ID: 282546012e0de7a9dda41cf08c1a94f357c1bc2a

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

Path: testdata/cond-single-source-wf-005.1.cwl

Branch/Commit ID: 77669d4dd1d1ebd2bdd9810f911608146d9b8e51

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-pe-dutp-mitochondrial.cwl

Branch/Commit ID: cbefc215d8286447620664fb47076ba5d81aa47f

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-pe-dutp-mitochondrial.cwl

Branch/Commit ID: aebf2355539fdf81fd9082616f8b21440d2691c6

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

Path: definitions/subworkflows/filter_vcf_mouse.cwl

Branch/Commit ID: d2c2f2eb846ae2e9cdcab46e3bb88e42126cb3f5

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **paired-end** experiment with Trim Galore. _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics for both the input FASTQ files, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with running fastx_quality_stats (steps fastx_quality_stats_upstream and fastx_quality_stats_downstream) from FASTX-Toolkit to calculate quality statistics for both upstream and downstream input FASTQ files. At the same time Bowtie is used to align reads from input FASTQ files to reference genome (Step bowtie_aligner). The output of this step is unsorted SAM file which is being sorted and indexed by samtools sort and samtools index (Step samtools_sort_index). Depending on workflow’s input parameters indexed and sorted BAM file could be processed by samtools rmdup (Step samtools_rmdup) to remove all possible read duplicates. In a case when removing duplicates is not necessary the step returns original input BAM and BAI files without any processing. If the duplicates were removed the following step (Step samtools_sort_index_after_rmdup) reruns samtools sort and samtools index with BAM and BAI files, if not - the step returns original unchanged input files. Right after that macs2 callpeak performs peak calling (Step macs2_callpeak). On the base of returned outputs the next step (Step macs2_island_count) calculates the number of islands and estimated fragment size. If the last one is less that 80 (hardcoded in a workflow) macs2 callpeak is rerun again with forced fixed fragment size value (Step macs2_callpeak_forced). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimated fragment size (Step macs2_island_count_forced) for the data obtained from macs2_callpeak_forced step. If the last one was skipped the results from macs2_island_count_forced step are equal to the ones obtained from macs2_island_count step. Next step (Step macs2_stat) is used to define which of the islands and estimated fragment size should be used in workflow output: either from macs2_island_count step or from macs2_island_count_forced step. If input trigger of this step is set to True it means that macs2_callpeak_forced step was run and it returned different from macs2_callpeak step results, so macs2_stat step should return [fragments_new, fragments_old, islands_new], if trigger is False the step returns [fragments_old, fragments_old, islands_old], where sufix \"old\" defines results obtained from macs2_island_count step and sufix \"new\" - from macs2_island_count_forced step. The following two steps (Step bamtools_stats and bam_to_bigwig) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step get_stat is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step island_intersect assigns genes and regions to the islands obtained from macs2_callpeak_forced. Step average_tag_density is used to calculate data for average tag density plot on the base of BAM file.

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

Path: workflows/trim-chipseq-pe.cwl

Branch/Commit ID: d1bef74924efcb8bfaa00987b3f148d5a192b7a9

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

Path: vecscreen/foreign_screening.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77

Single-Cell RNA-Seq Differential Expression Analysis Identifies differentially expressed genes between any two groups of cells, optionally aggregating gene expression data from single-cell to pseudobulk form.

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

Path: workflows/sc-rna-de-pseudobulk.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

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

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

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

A workflow for processing a single 16S sample via a QIIME2 pipeline. ## __Outputs__ #### Output files: - overview.md, list of inputs - demux.qzv, summary visualizations of imported data - alpha-rarefaction.qzv, plot of OTU rarefaction - taxa-bar-plots.qzv, relative frequency of taxomonies barplot ## __Inputs__ #### General Info - Sample short name/Alias: Used for samplename in downstream analyses. Ensure this is the same name used in the metadata samplesheet. - Environment: where the sample was collected - Catalog No.: catalog number if available (optional) - Read 1 FASTQ file: Read 1 FASTQ file from a paired-end sequencing run. - Read 2 FASTQ file: Read 2 FASTQ file that pairs with the input R1 file. - Trim 5' of R1: Recommended if adapters are still on the input sequences. Trims the first J bases from the 5' end of each forward read. - Trim 5' of R2: Recommended if adapters are still on the input sequences. Trims the first K bases from the 5' end of each reverse read. - Truncate 3' of R1: Recommended if quality drops off along the length of the read. Clips the forward read starting M bases from the 5' end (before trimming). - Truncate 3' of R2: Recommended if quality drops off along the length of the read. Clips the reverse read starting N bases from the 5' end (before trimming). - Threads: Number of threads to use for steps that support multithreading. ### __Data Analysis Steps__ 1. Generate FASTX quality statistics for visualization of unmapped, raw FASTQ reads. 2. Import the data, make a qiime artifact (demux.qza), and summary visualization 3. Denoising will detect and correct (where possible) Illumina amplicon sequence data. This process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences. 4. Generate a phylogenetic tree for diversity analyses and rarefaction processing and plotting. 5. Taxonomy classification of amplicons. Performed using a Naive Bayes classifier trained on the Greengenes2 database \"gg_2022_10_backbone_full_length.nb.qza\". ### __References__ 1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, and Caporaso JG. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37: 852–857. https://doi.org/10.1038/s41587-019-0209-9

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

Path: workflows/qiime2-sample-pe.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869