Explore Workflows

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

Path: tests/iwdr_with_nested_dirs.cwl

Branch/Commit ID: 31ec48a8d81ef7c1b2c5e9c0a19e7623efe4a1e2

GAT: Genomic Association Tester ============================================== A common question in genomic analysis is whether two sets of genomic intervals overlap significantly. This question arises, for example, in the interpretation of ChIP-Seq or RNA-Seq data. The Genomic Association Tester (GAT) is a tool for computing the significance of overlap between multiple sets of genomic intervals. GAT estimates significance based on simulation. Gat implemements a sampling algorithm. Given a chromosome (workspace) and segments of interest, for example from a ChIP-Seq experiment, gat creates randomized version of the segments of interest falling into the workspace. These sampled segments are then compared to existing genomic annotations. The sampling method is conceptually simple. Randomized samples of the segments of interest are created in a two-step procedure. Firstly, a segment size is selected from to same size distribution as the original segments of interest. Secondly, a random position is assigned to the segment. The sampling stops when exactly the same number of nucleotides have been sampled. To improve the speed of sampling, segment overlap is not resolved until the very end of the sampling procedure. Conflicts are then resolved by randomly removing and re-sampling segments until a covering set has been achieved. Because the size of randomized segments is derived from the observed segment size distribution of the segments of interest, the actual segment sizes in the sampled segments are usually not exactly identical to the ones in the segments of interest. This is in contrast to a sampling method that permutes segment positions within the workspace.

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

Path: workflows/gat-run.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f

Deprecated. AltAnalyze ICGS

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

Path: workflows/altanalyze-icgs.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf

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

Path: definitions/subworkflows/strelka_and_post_processing.cwl

Branch/Commit ID: 0c4f4e59c265eb22aed3d2d37b173cb5430773d2

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

Path: definitions/pipelines/bisulfite.cwl

Branch/Commit ID: cc3e7f1ccfdc7101c22bf88792608504eea7d53a

Workflow runs [Bowtie](http://bowtie-bio.sourceforge.net/tutorial.shtml) v1.2.0 (12/30/2016) to build indices for reference genome provided in a single FASTA file as fasta_file input. Generated indices are saved in a folder with the name that corresponds to the input genome

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

Path: workflows/bowtie-index.cwl

Branch/Commit ID: ee66d03be8a7fd61367db40c37a973ff55ece4da

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

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 5cb188131f786ed33156e2f0e3dd63ab9c04245d

Differential Binding Analysis of ChIP-Seq Peak Data --------------------------------------------------- DiffBind processes ChIP-Seq data enriched for genomic loci where specific protein/DNA binding occurs, including peak sets identified by ChIP-Seq peak callers and aligned sequence read datasets. It is designed to work with multiple peak sets simultaneously, representing different ChIP experiments (antibodies, transcription factor and/or histone marks, experimental conditions, replicates) as well as managing the results of multiple peak callers. For more information please refer to: ------------------------------------- Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, Brown GD, Gojis O, Ellis IO, Green AR, Ali S, Chin S, Palmieri C, Caldas C, Carroll JS (2012). “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature, 481, -4.

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

Path: workflows/diffbind.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4

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: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: e71779665f42fcf34601b0f65e030bb0dd47fa79