Explore Workflows

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

Path: task_types/tt_hmmsearch_wnode.cwl

Branch/Commit ID: a2d6cd4c53bf3501f6bd79edebb7ca30bba8456f

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

Path: cwltool/schemas/v1.0/v1.0/count-lines4-wf.cwl

Branch/Commit ID: c6cced7a2e6389d2eb43342e702677ccb7c7497c

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: 3fc68366adb179927af5528c27b153abaf94494d

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf3.cwl

Branch/Commit ID: 4700fbee9a5a3271eef8bc9ee595619d0720431b

Packed ID: main

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

Path: cwltool/schemas/v1.0/v1.0/count-lines8-wf.cwl

Branch/Commit ID: 2ae8117360a3cd4909d9d3f2b35c30bfffb25d0a

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: tools/SSU-from-tablehits.cwl

Branch/Commit ID: 5e8217435bcdd597b2ad236f3e847d13d4c21824

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq SE sample 1** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 1 **ChIP-Seq SE sample 2** * previously analyzed ChIP-Seq single-read experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-se.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0

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

Path: cwltool/schemas/v1.0/v1.0/steplevel-resreq.cwl

Branch/Commit ID: 7ec307b01442936fad9b1149f4500496557505ff

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

Path: definitions/pipelines/somatic_exome.cwl

Branch/Commit ID: 22fce2dbdada0c4135b6f0677f78535cf980cb07

A basic analysis workflow for paired-read CUT&RUN and CUT&TAG sequencing experiments. These sequencing library prep methods are ultra-sensitive chromatin mapping technologies compared to the ChIP-Seq methodology. Its primary benefits include 1) length filtering, 2) a higher signal-to-noise ratio, and 3) built-in normalization for between sample comparisons. This workflow utilizes the tool [SEACR (Sparse Enrichment Analysis of CUT&RUN data)](https://github.com/FredHutch/SEACR) which calls enriched regions in the target sequence data by identifying the top 1% of regions by area under the curve (of the alignment pileup). This workflow is loosely based on the [CUT-RUNTools-2.0 pipeline](https://github.com/fl-yu/CUT-RUNTools-2.0) pipeline, and the ChIP-Seq pipeline from [BioWardrobe](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) was used as a CWL template. ### __Inputs__ *General Info (required\*):* - Experiment short name/Alias* - a unique name for the sample (e.g. what was used on tubes while processing it) - Cells* - sample cell type or organism name - Conditions* - experimental condition name - Catalog # - catalog number for cells from vender/supplier - Primary [genome index](https://scidap.com/tutorials/basic/genome-indices) for peak calling* - preprocessed genome index of sample organism for primary alignment and peak calling - Secondary [genome index](https://scidap.com/tutorials/basic/genome-indices) for spike-in normalization* - preprocessed genome index of spike-in organism for secondary alignment (of unaligned reads from primary alignment) and spike-in normalization, default should be E. coli K-12 - FASTQ file for R1* - read 1 file of a pair-end library - FASTQ file for R2* - read 2 file of a pair-end library *Advanced:* - Number of bases to clip from the 3p end - used by bowtie aligner to trim <int> bases from 3' (right) end of reads - Number of bases to clip from the 5p end - used by bowtie aligner to trim <int> bases from 5' (left) end of reads - Call samtools rmdup to remove duplicates from sorted BAM file? - toggle on/off to remove duplicate reads from analysis - Fragment Length Filter will retain fragments between set base pair (bp) ranges for peak analysis - drop down menu - `Default_Range` retains fragments <1000 bp - `Histone_Binding_Library` retains fragments between 130-300 bp - `Transcription_Factor_Binding_Library` retains fragments <130 bp - Max distance (bp) from gene TSS (in both directions) overlapping which the peak will be assigned to the promoter region - default set to `1000` - Max distance (bp) from the promoter (only in upstream directions) overlapping which the peak will be assigned to the upstream region - default set to `20000` - Number of threads for steps that support multithreading - default set to `2` ### __Outputs__ Intermediate and final downloadable outputs include: - IGV with gene, BigWig (raw and normalized), and stringent peak tracks - quality statistics and visualizations for both R1/R2 input FASTQ files - coordinate sorted BAM file with associated BAI file for primary alignment - read pileup/coverage in BigWig format (raw and normalized) - cleaned bed files (containing fragment coordinates), and spike-in normalized SEACR peak-called BED files from both \"stringent\" and \"relaxed\" mode. - stringent peak call bed file with nearest gene annotations per peak ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. (Optional) Clipping of 5' and/or 3' end by the specified number of bases. 4. Mapping reads to primary genome index with Bowtie. - Only uniquely mapped reads with less than 3 mismatches are used in the downstream analysis. Results are then sorted and indexed. Final outputs are in bam/bai format, which are also used to extrapolate effects of additional sequencing based on library complexity. 5. (Optional) Removal of duplicates (reads/pairs of reads mapping to exactly the same location). - This step is used to remove reads overamplified during amplification of the library. Unfortunately, it may also remove \"good\" reads. We usually do not remove duplicates unless the library is heavily duplicated. 6. Mapping unaligned reads from primary alignment to secondary genome index with Bowtie. - This step is used to obtain the number of reads for normalization, used to scale the pileups from the primary alignment. After normalization, sample pileups/peak may then be appropriately compared to one another assuming an equal use of spike-in material during library preparation. Note the default genome index for this step should be *E. coli* K-12 if no spike-in material was called out in the library protocol. Refer to [Step 16](https://www.protocols.io/view/cut-amp-tag-data-processing-and-analysis-tutorial-e6nvw93x7gmk/v1?step=16#step-4A3D8C70DC3011EABA5FF3676F0827C5) of the \"CUT&Tag Data Processing and Analysis Tutorial\" by Zheng Y et al (2020). Protocol.io. 7. Formatting alignment file to account for fragments based on paired-end BAM. - Generates a filtered and normalized bed file to be used as input for SEACR peak calling. 8. Call enriched regions using SEACR. - This step used both stringent and relaxed peak calling modes with a FDR (false discovery rate) of 0.01, and no normalization to a control sample. The output of SEACR is the [called peaks BED format file](https://github.com/FredHutch/SEACR#description-of-output-fields). 9. Generation and formatting of output files. - This step collects read, alignment, and peak statistics, as well asgenerates BigWig coverage/pileup files for display on the browser using IGV. The coverage shows the number of fragments that cover each base in the genome both normalized and unnormalized to the calculated spike-in scaling factor. ### __References__ - Meers MP, Tenenbaum D, Henikoff S. (2019). Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics and Chromatin 12(1):42. - Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.

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

Path: workflows/cutandrun-pe.cwl

Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9