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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: 581156366f91861bd4dbb5bcb59f67d468b32af3
workflow graph DiffBind - Differential Binding Analysis of ChIP-Seq Peak Data

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: 581156366f91861bd4dbb5bcb59f67d468b32af3
workflow graph EMG pipeline v3.0 (paired end version)

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3-paired.cwl

Branch/Commit ID: b6d3aaf3fa6695061208c6cdca3d7881cc45400d
workflow graph ChIP-Seq pipeline single-read

# 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: 7fb8a1ebf8145791440bc2fed9c5f2d78a19d04c
workflow graph EMG pipeline v3.0 (single end version)

https://github.com/ProteinsWebTeam/ebi-metagenomics-cwl.git

Path: workflows/emg-pipeline-v3.cwl

Branch/Commit ID: a8abd0e66de7b5ffe24cfe7f39d7027103c6d3b4
workflow graph kmer_top_n_extract

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

Path: task_types/tt_kmer_top_n_extract.cwl

Branch/Commit ID: 4b73bfeb967ee9f57a0410276f7c39e784f0846f
workflow graph Find reads with predicted coding sequences above 60 AA in length

https://github.com/EBI-Metagenomics/ebi-metagenomics-cwl.git

Path: workflows/orf_prediction.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f
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: 9850a859de1f42d3d252c50e15701928856fe774
workflow graph rRNA_selection.cwl

https://github.com/proteinswebteam/ebi-metagenomics-cwl.git

Path: tools/rRNA_selection.cwl

Branch/Commit ID: 71d9c83761ea301a895dd669902979ef5a4b279b
workflow graph gather AML trio outputs

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

Path: definitions/pipelines/aml_trio_cle_gathered.cwl

Branch/Commit ID: 174f3b239018328cec1d821947438b457552724c