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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: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: testdata/count-lines7-wf_v1_0.cwl

Branch/Commit ID: c46a3ad4e488e75b7a58032c129f0605f5e84f40

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: a9d8c3c491945e8ebd6bb777c6bdd1a7e5671556

Filters differentially expressed genes from DESeq for Tag Density Profile Analyses ================================================================================== Tool filters output from DESeq pipeline run for genes to create a file with regions of interest for Tag Density Profile Analyses.

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

Path: workflows/filter-deseq-for-heatmap.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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 **strand specific single-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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) 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/rnaseq-se-dutp.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

A workflow for the Broad Institute's best practices gatk4 germline variant calling pipeline. ## __Outputs__ #### Primary Output files: - bqsr2_indels.vcf, filtered and recalibrated indels (IGV browser) - bqsr2_snps.vcf, filtered and recalibrated snps (IGV browser) - bqsr2_snps.ann.vcf, filtered and recalibrated snps with effect annotations #### Secondary Output files: - sorted_dedup_reads.bam, sorted deduplicated alignments (IGV browser) - raw_indels.vcf, first pass indel calls - raw_snps.vcf, first pass snp calls #### Reports: - overview.md (input list, alignment metrics, variant counts) - insert_size_histogram.pdf - recalibration_plots.pdf - snpEff_summary.html ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - BWA index: BWA index sample that contains reference genome FASTA with associated indices. - SNPEFF database: Name of SNPEFF database to use for SNP effect annotation. - Read 1 file: First FASTQ file (generally contains \"R1\" in the filename) - Read 2 file: Paired FASTQ file (generally contains \"R2\" in the filename) #### Advanced - Ploidy: number of copies per chromosome (default should be 2) - SNP filters: see Step 6 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ - Indel filters: see Step 7 Notes: https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ #### SNPEFF notes: Get snpeff databases using `docker run --rm -ti gatk4-dev /bin/bash` then running `java -jar $SNPEFF_JAR databases`. Then, use the first column as SNPEFF input (e.g. \"hg38\"). - hg38, Homo_sapiens (USCS), http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_hg38.zip - mm10, Mus_musculus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_mm10.zip - dm6.03, Drosophila_melanogaster, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_dm6.03.zip - Rnor_6.0.86, Rattus_norvegicus, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_Rnor_6.0.86.zip - R64-1-1.86, Saccharomyces_cerevisiae, http://downloads.sourceforge.net/project/snpeff/databases/v4_3/snpEff_v4_3_R64-1-1.86.zip ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. Run germline variant calling pipeline, custom wrapper script implementing Steps 1 - 17 of the Broad Institute's best practices gatk4 germline variant calling pipeline (https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/) ### __References__ 1. https://gencore.bio.nyu.edu/variant-calling-pipeline-gatk4/ 2. https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels- 3. https://software.broadinstitute.org/software/igv/VCF

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

Path: workflows/vc-germline-pe.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: protein_alignment/wf_seed.cwl

Branch/Commit ID: 4ffbad9ffeab15ec8af5f6f91bd352ef96d1ef77

A CWL workflow for discovering known or novel miRNAs from deep sequencing data using the miRDeep2 tool. The ExoCarta exosome database is also used for identifying exosome-related miRNAs, and TargetScan's organism-specific databases are used for identifying miRNA gene targets. ## __Outputs__ #### Primary Output files: - mirs_known.tsv, detected known mature miRNAs, \"Known miRNAs\" tab - mirs_novel.tsv, detected novel mature miRNAs, \"Novel miRNAs\" tab #### Secondary Output files: - mirs_known_exocarta_deepmirs.tsv, list of detected miRNA also in ExoCarta's exosome database, \"Detected Exosome miRNAs\" tab - mirs_known_gene_targets.tsv, pre-computed gene targets of known mature mirs, downloadable - known_mirs_mature.fa, known mature mir sequences, downloadable - known_mirs_precursor.fa, known precursor mir sequences, downloadable - novel_mirs_mature.fa, novel mature mir sequences, downloadable - novel_mirs_precursor.fa, novel precursor mir sequences, downloadable #### Reports: - overview.md (input list, alignment & mir metrics), \"Overview\" tab - mirdeep2_result.html, summary of mirdeep2 results, \"miRDeep2 Results\" tab ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - Bowtie2 index: Bowtie2 index directory of the reference genome. - Reference Genome FASTA: Reference genome FASTA file to be used for alignment. - Genome short name: Name used for setting organism name, genus, species, and tax ID. - Input FASTQ file: FASTQ file from a single-end miRNA sequencing run. #### Advanced - Adapter: Adapter sequence to be trimmed from miRNA sequence reads. (Default: TCGTAT) - Threads: Number of threads to use for steps that support multithreading (Default: 4). ## Hints & Tips: #### For the identification of novel miRNA candidates, the following may be used as a filtering guideline: 1. miRDeep score > 4 (some authors use 1) 2. not present a match with rfam 3. should present a significant RNAfold (\"yes\") 4. a number of mature reads > 10 5. if applicable, novel mir must be expressed in multiple samples #### For filtering mirbase by organism. | genome | organism | division | name | tree | NCBI-taxid | | ---- | --- | --- | ----------- | ----------- | ----------- | | hg19 | hsa | HSA | Homo sapiens | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Primates;Hominidae | 9606 | | hg38 | hsa | HSA | Homo sapiens | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Primates;Hominidae | 9606 | | mm10 | mmu | MMU | Mus musculus | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Rodentia | 10090 | | rn7 | rno | RNO | Rattus norvegicus | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Rodentia | 10116 | | dm3 | dme | DME | Drosophila melanogaster | Metazoa;Bilateria;Ecdysozoa;Arthropoda;Hexapoda | 7227 | ## __Data Analysis Steps__ 1. The miRDeep2 Mapper module processes Illumina FASTQ output and maps it to the reference genome. 2. The miRDeep2 miRDeep2 module identifies known and novel (mature and precursor) miRNAs. 3. The ExoCarta database of miRNA found in exosomes is then used to find overlap between mirs_known.tsv and exosome associated miRNAs. 4. Finally, TargetScan organism-specific miRNA gene target database is used to find overlap between mirs_known.tsv and gene targets. ## __References__ 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245920 2. https://github.com/rajewsky-lab/mirdeep2 3. https://biocontainers.pro/tools/mirdeep2 4. https://www.mirbase.org/ 5. http://exocarta.org/index.html 6. https://www.targetscan.org/vert_80/

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

Path: workflows/mirna-mirdeep2-se.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: definitions/pipelines/somatic_exome_gathered.cwl

Branch/Commit ID: 5fda2d9eb52a363bd51011b3851c2afb86318c0c