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workflow graph msi.cwl

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/subworkflows/msi.cwl

Branch/Commit ID: master
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: master
workflow graph chksum_seqval_wf_interleaved_fq.cwl

https://github.com/cancerit/workflow-seq-import.git

Path: cwls/chksum_seqval_wf_interleaved_fq.cwl

Branch/Commit ID: 0.2.3
workflow graph collate_unique_SSU_headers.cwl

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: tools/collate_unique_SSU_headers.cwl

Branch/Commit ID: 6c856cd
workflow graph Subworkflow that runs cnvkit in single sample mode and returns a vcf file

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

Path: definitions/subworkflows/cnvkit_single_sample.cwl

Branch/Commit ID: e7e888df9e7d44f036c4c7985e474016ee9e6525
workflow graph wf_blastit.cwl

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

Path: contam_filter/wf_blastit.cwl

Branch/Commit ID: master
workflow graph rRNA_selection.cwl

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: tools/rRNA_selection.cwl

Branch/Commit ID: 1b0851e
workflow graph Bacterial Annotation, pass 4, blastp-based functional annotation (second pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass4.cwl

Branch/Commit ID: test
workflow graph functional analysis prediction with InterProScan

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/functional_analysis.cwl

Branch/Commit ID: 3f85843
workflow graph cnv.cwl

Copynumber variation workflow, runs ADTEx and Varscan

 https://github.com/BD2KGenomics/dockstore_workflow_cnv.git

Path: cnv.cwl

Branch/Commit ID: v1.0.0