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Graph Name Retrieved From View
workflow graph chipseq-gen-bigwig.cwl

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

Path: subworkflows/chipseq-gen-bigwig.cwl

Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b
workflow graph stdout-wf_v1_1.cwl

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

Path: testdata/stdout-wf_v1_1.cwl

Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507
workflow graph io-int-wf.cwl

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

Path: tests/io-int-wf.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b
workflow graph RNA-Seq alignment and transcript/gene abundance workflow

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

Path: definitions/pipelines/rnaseq.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b
workflow graph exome alignment and somatic variant detection

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b
workflow graph phase VCF

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

Path: definitions/subworkflows/phase_vcf.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b
workflow graph Workflow to run pVACseq from detect_variants and rnaseq pipeline outputs

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

Path: definitions/subworkflows/pvacseq.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b
workflow graph exome alignment and germline variant detection, with optitype for HLA typing

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

Path: definitions/pipelines/germline_exome_hla_typing.cwl

Branch/Commit ID: f9600f9959acdc30259ba7e64de61104c9b01f0b
workflow graph 16S metagenomic paired-end QIIME2 Sample (preprocessing)

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: 3042f91bf93bd91c0f420c53aaf10874967e8ddc
workflow graph count-lines11-wf-noET.cwl

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

Path: tests/count-lines11-wf-noET.cwl

Branch/Commit ID: ea9f8634e41824ac3f81c3dde698d5f0eef54f1b