Explore Workflows

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

Path: tests/wf/js_output_workflow.cwl

Branch/Commit ID: 49cd284a8fc7884de763573075d3e1d6a4c1ffdd

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

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

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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

Path: task_types/tt_align_merge_sas.cwl

Branch/Commit ID: c28cfb9882dedd3c522160f933cff1050ae24100

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 252e7214ac64cb1128881e76743013e61bc7ec38

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

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf6.cwl

Branch/Commit ID: 7c7615c44b80f8e76e659433f8c7875603ae0b25

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

Path: cwltool/schemas/v1.0/v1.0/count-lines3-wf.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda

A workflow for processing a multiple 16S samples from within the SciDAP platform, via a QIIME2 pipeline. ## __Outputs__ #### Output files: Primary 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 - table.qza, table containing how many sequences are associated with each sample and with each feature (OTU) Optional output files: - pcoa-unweighted-unifrac-emperor.qzv, PCoA using unweighted unifrac method - pcoa-bray-curtis-emperor.qzv, PCoA using bray curtis method - heatmap.qzv, output from gneiss differential abundance analysis using unsupervised correlation-clustering method (this will define the partitions of microbes that commonly co-occur with each other using Ward hierarchical clustering) - ancom-\$LEVEL.qzv, output from ANCOM differential abundance analysis at family, genus, and species taxonomic levels (includes volcano plot) ## __Inputs__ #### General Info - Sample short name/Alias: Used for samplename in downstream analyses. Ensure this is the same name used in the metadata samplesheet. - metadata_file: Path to the TSV file containing experiment metadata. The first column must have the header \"sample-id\" with sample names exactly as they have been input into your SciDAP project. The remaining column headers are experiment-specific. NOTE: Custom Label parameter metadata must be INT data type. - Metadata header name for PCoA axis label: Must be identical to one of the headers of the metadata file. Values under this metadata header must be INT. Required for PCoA analysis. - Rarefaction normalization sampling depth: Required for differential abundance analyses (along with group and taxonomic level). This step will subsample the counts in each sample without replacement so that each sample in the resulting table has a total count of INT. If the total count for any sample(s) are smaller than this value, those samples will be dropped from further analysis. It's recommend making your choice by reviewing the rarefaction plot. Choose a value that is as high as possible (so you retain more sequences per sample) while excluding as few samples as possible. - Metadata header name for differential abundance analyses: Required for differential abundance analyses (along with sampling depth and taxonomic level). Group/experimental condition column name from sample metadata file. Must be identical to one of the headers of the sample-metadata file. The corresponding column should only have two groups/conditions. - Taxonomic level for differential abundance analysis: Required for differential abundance analyses (along with sampling depth and group). Collapses the OTU table at the taxonomic level of interest for differential abundance analysis with ANCOM. Default: Genus - 16S samples for combined analysis: Upstream 16S samples for combined analysis. R1 and R2 fastq are used for generating the manifest file for data import to qiime2. - 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. Import all sample read data, make a qiime artifact (demux.qza), and summary visualization 2. 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. 3. Generate a phylogenetic tree for diversity analyses and rarefaction processing and plotting. 4. Taxonomy classification of amplicons. Performed using a Naive Bayes classifier trained on the Greengenes2 database \"gg_2022_10_backbone_full_length.nb.qza\". 5. If \"Metadata header name for PCoA axis label\" is provided, principle coordinates analysis (PCoA) will be performed using the unweighted unifrac and bray curtis methods. 3D plots are produced with PCo1, PCo2, and the provided axis label on the x, y, and z axes. 6. If the sampling depth and metadata header for differential analysis are provided, differential abundance analysis will be performed using Gneiss and ANCOM methods at the family, genus, and species taxonomic levels. A unsupervised hierarchical clustering heatmap (Gneiss) and volcano plot (ANCOM) are produced at the taxonomic level between the specified group. ### __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-aggregate.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

SoupX Estimate ==============

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

Path: workflows/soupx.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d