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Graph Name Retrieved From View
workflow graph format_rrnas_from_seq_entry

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

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 122aba2dafbb63241413c82b725b877c04523aaf
workflow graph Unaligned BAM to BQSR

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

Path: definitions/subworkflows/bam_to_bqsr.cwl

Branch/Commit ID: 049f4aeff4c4a1b8421cac9b1c1c1f0da5848315
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: 5e8217435bcdd597b2ad236f3e847d13d4c21824
workflow graph revsort.cwl

Reverse the lines in a document, then sort those lines.

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

Path: cwltool/schemas/v1.0/v1.0/revsort.cwl

Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c
workflow graph Bacterial Annotation, pass 1, genemark training, by HMMs (first pass)

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

Path: bacterial_annot/wf_bacterial_annot_pass1.cwl

Branch/Commit ID: 17bae57a1f00f5c6db8f3a82d86262f12b8153cf
workflow graph CLIP-Seq pipeline for single-read experiment NNNNG

Cross-Linking ImmunoPrecipitation ================================= `CLIP` (`cross-linking immunoprecipitation`) is a method used in molecular biology that combines UV cross-linking with immunoprecipitation in order to analyse protein interactions with RNA or to precisely locate RNA modifications (e.g. m6A). (Uhl|Houwaart|Corrado|Wright|Backofen|2017)(Ule|Jensen|Ruggiu|Mele|2003)(Sugimoto|König|Hussain|Zupan|2012)(Zhang|Darnell|2011) (Ke| Alemu| Mertens| Gantman|2015) CLIP-based techniques can be used to map RNA binding protein binding sites or RNA modification sites (Ke| Alemu| Mertens| Gantman|2015)(Ke| Pandya-Jones| Saito| Fak|2017) of interest on a genome-wide scale, thereby increasing the understanding of post-transcriptional regulatory networks. The identification of sites where RNA-binding proteins (RNABPs) interact with target RNAs opens the door to understanding the vast complexity of RNA regulation. UV cross-linking and immunoprecipitation (CLIP) is a transformative technology in which RNAs purified from _in vivo_ cross-linked RNA-protein complexes are sequenced to reveal footprints of RNABP:RNA contacts. CLIP combined with high-throughput sequencing (HITS-CLIP) is a generalizable strategy to produce transcriptome-wide maps of RNA binding with higher accuracy and resolution than standard RNA immunoprecipitation (RIP) profiling or purely computational approaches. The application of CLIP to Argonaute proteins has expanded the utility of this approach to mapping binding sites for microRNAs and other small regulatory RNAs. Finally, recent advances in data analysis take advantage of cross-link–induced mutation sites (CIMS) to refine RNA-binding maps to single-nucleotide resolution. Once IP conditions are established, HITS-CLIP takes ~8 d to prepare RNA for sequencing. Established pipelines for data analysis, including those for CIMS, take 3–4 d. Workflow -------- CLIP begins with the in-vivo cross-linking of RNA-protein complexes using ultraviolet light (UV). Upon UV exposure, covalent bonds are formed between proteins and nucleic acids that are in close proximity. (Darnell|2012) The cross-linked cells are then lysed, and the protein of interest is isolated via immunoprecipitation. In order to allow for sequence specific priming of reverse transcription, RNA adapters are ligated to the 3' ends, while radiolabeled phosphates are transferred to the 5' ends of the RNA fragments. The RNA-protein complexes are then separated from free RNA using gel electrophoresis and membrane transfer. Proteinase K digestion is then performed in order to remove protein from the RNA-protein complexes. This step leaves a peptide at the cross-link site, allowing for the identification of the cross-linked nucleotide. (König| McGlincy| Ule|2012) After ligating RNA linkers to the RNA 5' ends, cDNA is synthesized via RT-PCR. High-throughput sequencing is then used to generate reads containing distinct barcodes that identify the last cDNA nucleotide. Interaction sites can be identified by mapping the reads back to the transcriptome.

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

Path: workflows/clipseq-se.cwl

Branch/Commit ID: bfa3843bcf36125ff258d6314f64b41336f06e6b
workflow graph scatter-wf3_v1_2.cwl#main

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

Path: testdata/scatter-wf3_v1_2.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

Packed ID: main
workflow graph Build Bismark indices

Copy fasta_file file to the folder and run run bismark_genome_preparation script to prepare indices for Bismark Methylation Analysis. Bowtie2 aligner is used by default. The name of the output indices folder is equal to the genome input.

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

Path: workflows/bismark-index.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f
workflow graph Single-Cell Multiome ATAC-Seq and RNA-Seq Filtering Analysis

Single-Cell Multiome ATAC-Seq and RNA-Seq Filtering Analysis Removes low-quality cells from the outputs of the “Cell Ranger Count (RNA+ATAC)” and “Cell Ranger Aggregate (RNA+ATAC)” pipelines. The results of this workflow are used in the “Single-Cell RNA-Seq Dimensionality Reduction Analysis” and “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipelines.

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

Path: workflows/sc-multiome-filter.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908
workflow graph Kallisto transcript quant pipeline paired end

This workflow runs paired end RNA-Seq reads using the kallisto quant tool against a kallisto index reference genome (see \"Kallisto index pipeline\"). The kallisto transcript-level quantified samples are then compatible with the DESeq and GSEA downstream workflows. ### __Inputs__ - Kallisto index sample (of experimental organism) - R1/R2 FASTQ files of RNA-Seq read data - number of threads to use for multithreading processes ### __Outputs__ - kallisto quant file (transcript estimate tsv) ### __Data Analysis Steps__ 1. cwl calls dockercontainer robertplayer/scidap-kallisto to pseudo align reads using `kallisto quant`. 2. abundance tsv is formatted, and additional files are produced for gene and common TSS counts for use in differential expression analysis 3. read and alignment metrics are calculated for the sample piechart, and output to the overview.md file ### __References__ - Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525-527(2016), doi:10.1038/nbt.3519

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

Path: workflows/kallisto-quant-pe.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908