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Graph Name Retrieved From View
workflow graph chksum_for_corrupted_fastq_files.cwl

https://github.com/cancerit/workflow-seq-import.git

Path: cwls/chksum_for_corrupted_fastq_files.cwl

Branch/Commit ID: 0.5.0

workflow graph 01-qc-pe.cwl

ATAC-seq 01 QC - reads: PE

https://github.com/alexbarrera/GGR-cwl.git

Path: v1.0/ATAC-seq_pipeline/01-qc-pe.cwl

Branch/Commit ID: v1.0

workflow graph annotator_sub_wf.cwl

This is a subworkflow of the main oxog_varbam_annotat_wf workflow - this is not meant to be run as a stand-alone workflow!

https://github.com/ICGC-TCGA-PanCancer/OxoG-Dockstore-Tools.git

Path: annotator_sub_wf.cwl

Branch/Commit ID: develop

workflow graph fp_filter workflow

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

Path: definitions/subworkflows/fp_filter.cwl

Branch/Commit ID: master

workflow graph CODEX analysis pipeline using Cytokit

https://github.com/hubmapconsortium/codex-pipeline.git

Path: steps/ometiff_second_stitching.cwl

Branch/Commit ID: 16794b6

workflow graph CODEX analysis pipeline using Cytokit

https://github.com/hubmapconsortium/codex-pipeline.git

Path: steps/ometiff_second_stitching.cwl

Branch/Commit ID: cf68e50

workflow graph wf.cwl#VDJ_Assemble_and_Annotate_Contigs_IG.cwl

https://github.com/aheinzel/tmp_rhapsody_for_cwl_vis.git

Path: wf.cwl

Branch/Commit ID: main

Packed ID: VDJ_Assemble_and_Annotate_Contigs_IG.cwl

workflow graph pack.cwl

create textures and pack them to be a stellaris mod

https://gitlab.com/unduthegun/stellaris-emblem-lab.git

Path: pack/pack.cwl

Branch/Commit ID: master

workflow graph Raw sequence data to BQSR

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

Path: definitions/subworkflows/sequence_to_bqsr.cwl

Branch/Commit ID: master

workflow graph Differential Methylation Workflow

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: master