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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 PE sample 1** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 **ChIP-Seq PE sample 2** * previously analyzed ChIP-Seq paired-end 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-pe.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0

Single-Cell ATAC-Seq Genome Coverage Generates genome coverage tracks from chromatin accessibility data of selected cells

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

Path: workflows/sc-atac-coverage.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

https://github.com/NCGM-genome/WGSpipeline.git

Path: Workflows/germline-gpu-v4.0.1.cwl

Branch/Commit ID: b8262067df44ce67268f8af00a043f2187c604bb

Motif Finding with HOMER with custom background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis-bg.cwl

Branch/Commit ID: 2caa50434966ebdf4b33e5ca689c2e4df32f9058

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/subworkflows/samtools_sort.cwl

Branch/Commit ID: e1fd26587a78afc376c10bf6db36abd2c840768e

A basic differential methylation analysis workflow using BismarkCov formatted bed files as input to the RnBeads tool. Analysis is conducted on region and sites levels according to the sample groups specified by user (limited to 2 conditions in this workflow implementation). See report html files for detailed descriptions of analyses and results interpretation. ### __Inputs__ *General Info:* - Experiment short name/Alias* - a unique name for the sample (e.g. what was used on tubes while processing it) - Condition 1 name - name defining condition/group 1 - Condition 2 name - name defining condition/group 2 - Bismark coverage files* for condition1 - minumum of 2 is required for analysis - Bismark coverage files* for condition2 - minumum of 2 is required for analysis - Sample genome - available options: hg19, hg38, mm9, mm10, rn5 - Genome type - indicate mismark index used for upstream samples (input for conditions 1 and 2) *Advanced:* - Number of threads for steps that support multithreading - default set to `4` *[BismarkCov formatted bed](https://www.bioinformatics.babraham.ac.uk/projects/bismark/Bismark_User_Guide.pdf): The genome-wide cytosine report (optional) is tab-delimited in the following format (1-based coords): <chromosome> <position> <strand> <count methylated> <count unmethylated> <C-context> <trinucleotide context> ### __Outputs__ Intermediate and final downloadable outputs include: - sig_dm_sites.bed ([bed for IGV](https://genome.ucsc.edu/FAQ/FAQformat.html#format1); sig diff meth sites) - sig_dm_sites_annotated.tsv (tsv for TABLE; for each site above, closest single gene annotation) - Site_id, unique indentifer per methylated site - Site_Chr, chromosome of methylated site - Site_position, 1-based position in chr of methylated site - Site_strand, strand of methylated site - Log2_Meth_Quotient, log2 of the quotient in methylation: log2((mean.g1+epsilon)/(mean.g2+epsilon)), where epsilon:=0.01. In case of paired analysis, it is the mean of the pairwise quotients. - FDR, adjusted p-values, all <0.10 assumed to be significant - Coverage_score, value between 0-1000 reflects strength of mean coverage difference between conditions and equals [1000-(1000/(meancov_g1-meancov_g2)^2](https://www.wolframalpha.com/input?i=solve+1000-%281000%2F%28x%5E2%29%29), if meancov_g1-meancov_g2==0, score=0, elif score<1==1, else score - meancov_g1, mean coverage of condition1 - meancov_g2, mean coverage of condition2 - refSeq_id, RefSeq gene id - Gene_id, gene symbol - Chr, gene chromosome - txStart, gene transcription start position - tsEnd, gene transcription end position - txStrand, gene strand - stdout and stderr log files - Packaged RnBeads reports directory (reports.tar.gz) contains: reports/ ├── configuration ├── data_import.html ├── data_import_data ├── data_import_images ├── data_import_pdfs ├── differential_methylation.html ├── differential_methylation_data ├── differential_methylation_images ├── differential_methylation_pdfs ├── preprocessing.html ├── preprocessing_data ├── preprocessing_images ├── preprocessing_pdfs ├── quality_control.html ├── quality_control_data ├── quality_control_images ├── quality_control_pdfs ├── tracks_and_tables.html ├── tracks_and_tables_data ├── tracks_and_tables_images └── tracks_and_tables_pdfs Reported methylation is in the form of regions (genes, promoters, cpg, tiling) and specific sites: - genes - Ensembl gene definitions are downloaded using the biomaRt package. - promoters - A promoter is defined as the region spanning 1,500 bases upstream and 500 bases downstream of the transcription start site of the corresponding gene - cpg - the CpG islands from the UCSC Genome Browser - tiling - a window size of 5 kilobases are defined over the whole genome - sites - all cytosines in the context of CpGs in the respective genome ### __Data Analysis Steps__ 1. generate sample sheet with associated conditions for testing in RnBeads 2. setup rnbeads analyses in R, and run differential methylation analysis 3. process output diffmeth files for regions and sites 4. find single closest gene annotations for all significantly diffmeth sites 5. package and save rnbeads report directory 6. clean up report dir for html outputs ### __References__ - https://rnbeads.org/materials/example_3/differential_methylation.html - Makambi, K. (2003) Weighted inverse chi-square method for correlated significance tests. Journal of Applied Statistics, 30(2), 225234 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216143/ - Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C. Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods. 2014 Nov;11(11):1138-1140. doi: 10.1038/nmeth.3115. Epub 2014 Sep 28. PMID: 25262207; PMCID: PMC4216143.

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

Path: workflows/diffmeth.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

https://github.com/Marco-Salvi/cwl-test.git

Path: wf5201/ST520108.cwl

Branch/Commit ID: 0e6cfe0646173e228b2fce63e23ed8f9d78598b0

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

Path: workflows/dev/ultra-opt/workflows/kfdrc_bwamem_subwf.cwl

Branch/Commit ID: af97e25cb213233a4923c881f7c6210b57960cb9

Generates a FASTA file with the DNA sequences for all transcripts in a GFF file and builds kallisto index

https://github.com/Barski-lab/workflows.git

Path: tools/genome-kallisto-index.cwl

Branch/Commit ID: 8f5444418aad3424ccb05a3e618bd773f99f8e6e

Modified 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-dutp.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f