Explore Workflows

Single-Cell ATAC-Seq Filtering Analysis Removes low-quality cells from the outputs of either the “Cell Ranger Count (ATAC)” or “Cell Ranger Aggregate (ATAC)” pipeline. The results of this workflow are used in the “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipeline.

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

Path: workflows/sc-atac-filter.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 72804b6506c9f54ec75627f82aafe6a28d7a49fa

Runs DESeq2 using LRT (Likelihood Ratio Test) ============================================= The LRT examines two models for the counts, a full model with a certain number of terms and a reduced model, in which some of the terms of the full model are removed. The test determines if the increased likelihood of the data using the extra terms in the full model is more than expected if those extra terms are truly zero. The LRT is therefore useful for testing multiple terms at once, for example testing 3 or more levels of a factor at once, or all interactions between two variables. The LRT for count data is conceptually similar to an analysis of variance (ANOVA) calculation in linear regression, except that in the case of the Negative Binomial GLM, we use an analysis of deviance (ANODEV), where the deviance captures the difference in likelihood between a full and a reduced model. When one performs a likelihood ratio test, the p values and the test statistic (the stat column) are values for the test that removes all of the variables which are present in the full design and not in the reduced design. This tests the null hypothesis that all the coefficients from these variables and levels of these factors are equal to zero. The likelihood ratio test p values therefore represent a test of all the variables and all the levels of factors which are among these variables. However, the results table only has space for one column of log fold change, so a single variable and a single comparison is shown (among the potentially multiple log fold changes which were tested in the likelihood ratio test). This indicates that the p value is for the likelihood ratio test of all the variables and all the levels, while the log fold change is a single comparison from among those variables and levels. **Technical notes** 1. At least two biological replicates are required for every compared category 2. Metadata file describes relations between compared experiments, for example ``` ,time,condition DH1,day5,WT DH2,day5,KO DH3,day7,WT DH4,day7,KO DH5,day7,KO ``` where `time, condition, day5, day7, WT, KO` should be a single words (without spaces) and `DH1, DH2, DH3, DH4, DH5` correspond to the experiment aliases set in **RNA-Seq experiments** input. 3. Design and reduced formulas should start with **~** and include categories or, optionally, their interactions from the metadata file header. See details in DESeq2 manual [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions) and [here](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#likelihood-ratio-test) 4. Contrast should be set based on your metadata file header and available categories in a form of `Factor Numerator Denominator`, where `Factor` - column name from metadata file, `Numerator` - category from metadata file to be used as numerator in fold change calculation, `Denominator` - category from metadata file to be used as denominator in fold change calculation. For example `condition WT KO`.

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

Path: workflows/deseq-lrt.cwl

Branch/Commit ID: bf80c9339d81a78aefb8de661bff998ed86e836e

https://github.com/mskcc/innovation-pipeline.git

Path: workflows/subworkflows/manta.cwl

Branch/Commit ID: b0f226a9ac5152f3afe0d38c8cd54aa25b8b01cf

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/count-lines9-wf.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

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: 7030da528559c7106d156284e50ff0ecedab0c4e

https://github.com/common-workflow-language/cwl-v1.1.git

Path: tests/scatter-valuefrom-wf3.cwl

Branch/Commit ID: 664835e83eb5e57eee18a04ce7b05fb9d70d77b7

Packed ID: main

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

Path: definitions/subworkflows/bam_readcount.cwl

Branch/Commit ID: b7d9ace34664d3cedb16f2512c8a6dc6debfc8ca

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

Path: task_types/tt_kmer_gc_extract_wnode.cwl

Branch/Commit ID: e6fd7898b71a89b667d2eb38f412999920be5902

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

Path: cwltool/schemas/v1.0/v1.0/count-lines12-wf.cwl

Branch/Commit ID: 7dec97bb8f0bc2d9e9eb710faf41f2e98cc7cdda