Explore Workflows

Creates indices for: * [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) * [bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) It performs the following steps: 1. `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Outputs indices as [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file from Directory output 4. `bowtie-build` to generate indices requires genome [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) file as input, returns results as a group of main and secondary files

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

Path: workflows/genome-indices.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

Run metrics workflow

https://github.com/sentieon/sentieon-cwl.git

Path: stage/metrics-flow.cwl

Branch/Commit ID: d20382adfe7285cb517a25d95d2bcb7586546e23

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

Path: task_types/tt_hmmsearch_wnode_plus_qdump.cwl

Branch/Commit ID: 54c5074587af001a44eccb4762a4cb25fa24cb3e

Single-Cell Manual Cell Type Assignment Assigns identities to cells clustered with any of the “Single-Cell Cluster Analysis” pipelines. For “Single-Cell RNA-Seq Cluster Analysis” the results of this workflow are used in the “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, and — when combined with outputs from the “Cell Ranger Count (RNA+VDJ)” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” workflow — in the “Single-Cell Immune Profiling Analysis” pipeline. For “Single-Cell ATAC-Seq Cluster Analysis”, the results of this workflow are used in the “Single-Cell ATAC-Seq Differential Accessibility Analysis” and “Single-Cell ATAC-Seq Genome Coverage” pipelines. For “Single-Cell WNN Cluster Analysis”, the results of this workflow are used in all of the above, except the “Single-Cell Immune Profiling Analysis” pipeline.

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

Path: workflows/sc-ctype-assign.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3

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: d6f58c383d0676269afb519399061191a1144a6a

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

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

Branch/Commit ID: bf93dd3e6e3261e1455530984ce045f283535d17

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

Path: definitions/pipelines/panel_of_normals.cwl

Branch/Commit ID: 67f56d3b9c70ad56019ed8aa8d50a128e02be43b

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

Path: definitions/pipelines/alignment_umi_duplex.cwl

Branch/Commit ID: 00df82a529a58d362158110581e1daa28b4d7ecb

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/bulk_analysis.cwl

Branch/Commit ID: 00c5e3ad729c1712945c69b58bd9868115d11bfd

Slightly changed original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for **strand specific pair-end** experiment. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with the pair-end strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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/rnaseq-pe-dutp-mitochondrial.cwl

Branch/Commit ID: 30031ca5e69cec603c4733681de54dc7bffa20a3