Explore Workflows

Differential Binding Analysis of ChIP-Seq, ATAC-Seq, or CUT&RUN/Tag Peak Data with spike-in --------------------------------------------------- DiffBind processes ChIP-Seq, ATAC-Seq, or CUT&RUN/Tag data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by peak caller tools and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP, ATAC, or CUT&RUN/Tag experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. This specific workflow is designed for experiments that use a spike-in control for each sample. These spike-in reads are used to normalize the datasets during differential analysis using the RLE method (for either edgeR or DESeq) while accounting for background (spike-in). For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind-for-spikein.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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: cbefc215d8286447620664fb47076ba5d81aa47f

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

Path: definitions/pipelines/alignment_exome_mouse.cwl

Branch/Commit ID: 789267ce0e3fed674ea5212a562315218fcf1bfc

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe.cwl

Branch/Commit ID: a0b22644ca178b640fb74849d23b7c631022f0b5

Proteins - predict, filter, cluster, identify, annotate

https://github.com/MG-RAST/pipeline.git

Path: CWL/Workflows/protein-filter-annotation.workflow.cwl

Branch/Commit ID: 721aaf285e1848c3c52da38a1fed95192aeff8f4

https://github.com/kids-first/kf-alignment-workflow.git

Path: workflows/kfdrc_alignment_fqinput_CramOnly_wf.cwl

Branch/Commit ID: 55315b6abb488f1f25fe725407814e8d4c23ba81

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

Path: cwltool/schemas/v1.0/v1.0/js-expr-req-wf.cwl

Branch/Commit ID: 4fd5ca5a927594c361a9320d5331b326d06cecd3

Packed ID: wf

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

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

Branch/Commit ID: 875b928ce50a3202f5954843b79ea86683c160fa

Packed ID: main

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

Path: task_types/tt_extract_gencoll_ids.cwl

Branch/Commit ID: 7b21dc40840852f3942c31b9c472346ea3f9a3ca

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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.cwl

Branch/Commit ID: c6bfa0de917efb536dd385624fc7702e6748e61d