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workflow graph assm_assm_blastn_wnode

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

Path: task_types/tt_assm_assm_blastn_wnode.cwl

Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84
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: d76110e0bfc40c874f82e37cef6451d74df4f908
workflow graph Trim Galore RNA-Seq pipeline single-read

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-end** experiment. A corresponded input [FASTQ](http://maq.sourceforge.net/fastq.shtml) file has to be provided. Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Trim adapters from input FASTQ file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 4. Use 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/trim-rnaseq-se.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
workflow graph workflow.cwl

https://github.com/nal-i5k/organism_onboarding.git

Path: flow_dispatch/2working_files/workflow.cwl

Branch/Commit ID: 556884e8e205295e561b1d9140d836cdd6c1d8c9
workflow graph Cell Ranger Count (RNA+ATAC)

Cell Ranger Count (RNA+ATAC) Quantifies single-cell gene expression and chromatin accessibility of the sequencing data from a single 10x Genomics library in a combined manner. The results of this workflow are primarily used in either “Single-Cell Multiome ATAC and RNA-Seq Filtering Analysis” or “Cell Ranger Aggregate (RNA+ATAC)” pipelines.

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

Path: workflows/cellranger-arc-count.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
workflow graph step_valuefrom5_wf_with_id_v1_2.cwl

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

Path: testdata/step_valuefrom5_wf_with_id_v1_2.cwl

Branch/Commit ID: c46a3ad4e488e75b7a58032c129f0605f5e84f40
workflow graph Sounder SIPS L1B PGE

Processes Sounder SIPS L1A products into L1B Products

 https://github.com/unity-sds/unity-sps-workflows.git

Path: sounder_sips/l1b_package.cwl

Branch/Commit ID: d08e59ccfedba6cf4606a9317900754790f1447e

Packed ID: main
workflow graph SPRM pipeline

https://github.com/hubmapconsortium/sprm.git

Path: pipeline.cwl

Branch/Commit ID: cba3af50fde60128bd5e64c562860c8426a192b7
workflow graph genomics-workspace-cds.cwl

https://github.com/NAL-i5K/Organism_Onboarding.git

Path: flow_genomicsWorkspace/genomics-workspace-cds.cwl

Branch/Commit ID: 556884e8e205295e561b1d9140d836cdd6c1d8c9
workflow graph cluster_blastp_wnode and gpx_qdump combined

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84