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workflow graph GAT - Genomic Association Tester

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: 1131f82a53315cca217a6c84b3bd272aa62e4bca
workflow graph ani_top_n

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

Path: task_types/tt_ani_top_n.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c
workflow graph Trim Galore RNA-Seq pipeline single-read strand specific

Note: should be updated The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **single-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 file 2. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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 file 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-se-dutp.cwl

Branch/Commit ID: d1bef74924efcb8bfaa00987b3f148d5a192b7a9
workflow graph qc_workflow.cwl

https://github.com/mskcc/Innovation-Pipeline.git

Path: workflows/QC/qc_workflow.cwl

Branch/Commit ID: 9e6eae9eb8448e68d509397a46303551a93a164d
workflow graph kmer_cache_retrieve

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

Path: task_types/tt_kmer_cache_retrieve.cwl

Branch/Commit ID: 1bf7dc7b03ea3c64e54375cc5c3767849a801000
workflow graph kmer_ref_compare_wnode

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

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph scatter-wf1.cwl

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf1.cwl

Branch/Commit ID: 9e7c68c0834645ba53a7e2b5f70d53df9d051c92
workflow graph bgzip and index VCF

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

Path: definitions/subworkflows/bgzip_and_index.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e
workflow graph 00_qsm_pipeline_v1.cwl

https://github.com/MGuevaraO/qsm_pipeline.git

Path: 00_qsm_pipeline_v1.cwl

Branch/Commit ID: 882e36d61371650b06fe1a839a84c488ccdcf77f
workflow graph RNA-Seq pipeline paired-end strand specific

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 3. Use samtools sort to generate coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 1 (after running STAR) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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/rnaseq-pe-dutp.cwl

Branch/Commit ID: 2c486543c335bb99b245dfe7e2f033f535efb9cf