Explore Workflows

Principal Component Analysis -------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy.

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

Path: workflows/pca.cwl

Branch/Commit ID: ddc35c559d1ac6aab4972fe1a2b63300c4373f54

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

Path: cwltool/schemas/v1.0/v1.0/any-type-compat.cwl

Branch/Commit ID: 7c7615c44b80f8e76e659433f8c7875603ae0b25

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

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

Branch/Commit ID: 203797516329f7fb8aa5e763e6f9b331c63c3060

Packed ID: main

ATAC-seq pipeline - reads: SE - with blacklist removal

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

Path: v1.0/ATAC-seq_pipeline/pipeline-se-blacklist-removal.cwl

Branch/Commit ID: 3a4314c66c1eb090e656af5a0d388cec87d65318

Calculates the rescue ratio (see Gabe's protocols paper), given two eCLIP IP samples and 2 size-matched input samples. Also returns the reproducible peaks given these two samples. This is different from the 1input workflow in that each INPUT is first merged together and is used downstream instead of the 1input version, which remains unmodified. Merged inputs are NOT used in calculating true reproducible peaks.

https://github.com/YeoLab/merge_peaks.git

Path: cwl/wf_rescue_ratio_1input.cwl

Branch/Commit ID: 55f4f4f9c10a09ce03c5c531dd176e6080118977

Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome).

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

Path: workflows/bismark-methylation-se.cwl

Branch/Commit ID: ce058d892d330125cd03d0a0d5fb3b321cda0be3

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

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

Branch/Commit ID: 4635090ef98247b1902b3c7a25c007d9db1cb883

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/scatter-valuefrom-wf4.cwl

Branch/Commit ID: c7c97715b400ff2194aa29fc211d3401cea3a9bf

Packed ID: main

https://github.com/mskcc/roslin-variant.git

Path: setup/cwl/modules/facets.cwl

Branch/Commit ID: 8766def7f70f329e2a4c56239f141685a873599b

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: 7ced5a5259dbd8b3fc64456beaeffd44f4a24081