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Allele specific RNA-Seq single-read workflow

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

Path: workflows/allele-rnaseq-se.cwl

Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0

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: dda9e6e06a656b7b3fa7504156474b962fe3953c

https://github.com/Marco-Salvi/dtc51.git

Path: ST520106.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f

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

Path: definitions/subworkflows/filter_vcf.cwl

Branch/Commit ID: 31602b94b34ff55876147c7299e1bec47e8d1a31

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: 5561f7ee11dd74848680351411a19aa87b13d27b

# ChIP-Seq basic analysis workflow for single-read data Reads are aligned to the reference genome with [Bowtie](http://bowtie-bio.sourceforge.net/index.shtml). Results are saved as coordinate sorted [BAM](http://samtools.github.io/hts-specs/SAMv1.pdf) alignment and index BAI files. Optionally, PCR duplicates can be removed. To obtain coverage in [bigWig](https://genome.ucsc.edu/goldenpath/help/bigWig.html) format, average fragment length is calculated by [MACS2](https://github.com/taoliu/MACS), and individual reads are extended to this length in the 3’ direction. Areas of enrichment identified by MACS2 are saved in ENCODE [narrow peak](http://genome.ucsc.edu/FAQ/FAQformat.html#format12) or [broad peak](https://genome.ucsc.edu/FAQ/FAQformat.html#format13) formats. Called peaks together with the nearest genes are saved in TSV format. In addition to basic statistics (number of total/mapped/multi-mapped/unmapped/duplicate reads), pipeline generates several quality control measures. Base frequency plots are used to estimate adapter contamination, a frequent occurrence in low-input ChIP-Seq experiments. Expected distinct reads count from [Preseq](http://smithlabresearch.org/software/preseq/) can be used to estimate read redundancy for a given sequencing depth. Average tag density profiles can be used to estimate ChIP enrichment for promoter proximal histone modifications. Use of different parameters for different antibodies (calling broad or narrow peaks) is possible. Additionally, users can elect to use BAM file from another experiment as control for MACS2 peak calling. ## Cite as *Kartashov AV, Barski A. BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data. Genome Biol. 2015;16(1):158. Published 2015 Aug 7. [doi:10.1186/s13059-015-0720-3](https://www.ncbi.nlm.nih.gov/pubmed/26248465)* ## Software versions - Bowtie 1.2.0 - Samtools 1.4 - Preseq 2.0 - MACS2 2.1.1.20160309 - Bedtools 2.26.0 - UCSC userApps v358 ## Inputs | ID | Label | Description | Required | Default | Upstream analyses | | ------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------: | ------- | ------------------------------- | | **fastq\_file** | FASTQ file | Single-read sequencing data in FASTQ format (fastq, fq, bzip2, gzip, zip) | + | | | | **indices\_folder** | Genome indices | Directory with the genome indices generated by Bowtie | + | | genome\_indices/bowtie\_indices | | **annotation\_file** | Genome annotation file | Genome annotation file in TSV format | + | | genome\_indices/annotation | | **genome\_size** | Effective genome size | The length of the mappable genome (hs, mm, ce, dm or number, for example 2.7e9) | + | | genome\_indices/genome\_size | | **chrom\_length** | Chromosome lengths file | Chromosome lengths file in TSV format | + | | genome\_indices/chrom\_length | | **broad\_peak** | Call broad peaks | Make MACS2 call broad peaks by linking nearby highly enriched regions | + | | | | **control\_file** | Control ChIP-Seq single-read experiment | Indexed BAM file from the ChIP-Seq single-read experiment to be used as a control for MACS2 peak calling | | Null | control\_file/bambai\_pair | | **exp\_fragment\_size** | Expected fragment size | Expected fragment size for read extenstion towards 3' end if *force\_fragment\_size* was set to True or if calculated by MACS2 fragment size was less that 80 bp | | 150 | | | **force\_fragment\_size** | Force peak calling with expected fragment size | Make MACS2 don't build the shifting model and use expected fragment size for read extenstion towards 3' end | | False | | | **clip\_3p\_end** | Clip from 3' end | Number of base pairs to clip from 3' end | | 0 | | | **clip\_5p\_end** | Clip from 5' end | Number of base pairs to clip from 5' end | | 0 | | | **remove\_duplicates** | Remove PCR duplicates | Remove PCR duplicates from sorted BAM file | | False | | | **threads** | Number of threads | Number of threads for those steps that support multithreading | | 2 | | ## Outputs | ID | Label | Description | Required | Visualization | | ------------------------ | ---------------------------------- | ------------------------------------------------------------------------------------ | :------: | ------------------------------------------------------------------ | | **fastx\_statistics** | FASTQ quality statistics | FASTQ quality statistics in TSV format | + | *Base Frequency* and *Quality Control* plots in *QC Plots* tab | | **bambai\_pair** | Aligned reads | Coordinate sorted BAM alignment and index BAI files | + | *Nucleotide Sequence Alignments* track in *IGV Genome Browser* tab | | **bigwig** | Genome coverage | Genome coverage in bigWig format | + | *Genome Coverage* track in *IGV Genome Browser* tab | | **iaintersect\_result** | Gene annotated peaks | MACS2 peak file annotated with nearby genes | + | *Peak Coordinates* table in *Peak Calling* tab | | **atdp\_result** | Average Tag Density Plot | Average Tag Density Plot file in TSV format | + | *Average Tag Density Plot* in *QC Plots* tab | | **macs2\_called\_peaks** | Called peaks | Called peaks file with 1-based coordinates in XLS format | + | | | **macs2\_narrow\_peaks** | Narrow peaks | Called peaks file in ENCODE narrow peak format | | *Narrow peaks* track in *IGV Genome Browser* tab | | **macs2\_broad\_peaks** | Broad peaks | Called peaks file in ENCODE broad peak format | | *Broad peaks* track in *IGV Genome Browser* tab | | **preseq\_estimates** | Expected Distinct Reads Count Plot | Expected distinct reads count file from Preseq in TSV format | | *Expected Distinct Reads Count Plot* in *QC Plots* tab | | **workflow\_statistics** | Workflow execution statistics | Overall workflow execution statistics from bowtie\_aligner and samtools\_rmdup steps | + | *Overview* tab and experiment's preview | | **bowtie\_log** | Read alignment log | Read alignment log file from Bowtie | + | |

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

Path: workflows/chipseq-se.cwl

Branch/Commit ID: 36fd18f11e939d3908b1eca8d2939402f7a99b0f

Some workflow engines won't stage files in our nested structure, so parse it out here

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

Path: definitions/subworkflows/sequence_align_and_tag_adapter.cwl

Branch/Commit ID: 2f65fc96207a71b1cda4e246f808bed056608cd0

https://github.com/Marco-Salvi/dtc51.git

Path: ST520103.cwl

Branch/Commit ID: 272db37d2b8108a146769f0fb0383bb824c9788f

https://github.com/CRI-iAtlas/iatlas-workflows.git

Path: Kallisto/workflow/kallisto_synapse_single_end_workflow.cwl

Branch/Commit ID: c7fa0fa9ef94c657b664f680462dbc3f5b7a32e8

Runs ChIP-Seq BioWardrobe basic analysis with paired-end input data files.

https://github.com/Barski-lab/workflows.git

Path: workflows/chipseq-pe.cwl

Branch/Commit ID: 852fa49a70fe0965de6892fa0832f30b710f0e75