Explore Workflows

Workflow for Metagenomics from raw reads to annotated bins.<br> Summary - MetaBAT2 (binning) - CheckM (bin completeness and contamination) - GTDB-Tk (bin taxonomic classification) - BUSCO (bin completeness) **All tool CWL files and other workflows can be found here:**<br> Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl<br> Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows<br> The dependencies are either accessible from https://unlock-icat.irods.surfsara.nl (anonymous,anonymous)<br> and/or<br> By using the conda / pip environments as shown in https://git.wur.nl/unlock/docker/-/blob/master/kubernetes/scripts/setup.sh<br>

https://git.wur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_metagenomics_binning.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0

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

Path: workflows/emg-pipeline-v4-paired.cwl

Branch/Commit ID: 7bb76f33bf40b5cd2604001cac46f967a209c47f

https://github.com/pitagora-network/DAT2-cwl.git

Path: workflow/rna-seq/rnaseq-star-rsem-deseq2/rnaseq-star-rsem-deseq2.cwl

Branch/Commit ID: 6db4456ca7314b036e59f50910654066da99772a

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

Path: tests/wf/scatterfail.cwl

Branch/Commit ID: 2710cfe731374cf7244116dd7186fc2b6e4af344

Workflow for Metagenomics from raw reads to annotated bins.<br> Summary - MetaBAT2 (binning) - CheckM (bin completeness and contamination) - GTDB-Tk (bin taxonomic classification) - BUSCO (bin completeness) **All tool CWL files and other workflows can be found here:**<br> Tools: https://git.wur.nl/unlock/cwl/-/tree/master/cwl<br> Workflows: https://git.wur.nl/unlock/cwl/-/tree/master/cwl/workflows<br> The dependencies are either accessible from https://unlock-icat.irods.surfsara.nl (anonymous,anonymous)<br> and/or<br> By using the conda / pip environments as shown in https://git.wur.nl/unlock/docker/-/blob/master/kubernetes/scripts/setup.sh<br>

https://git.wageningenur.nl/unlock/cwl.git

Path: cwl/workflows/workflow_metagenomics_binning.cwl

Branch/Commit ID: b9097b82e6ab6f2c9496013ce4dd6877092956a0

https://github.com/PMCC-BioinformaticsCore/janis-pipelines.git

Path: janis_pipelines/wgs_somatic/cwl/tools/split_bam_subpipeline.cwl

Branch/Commit ID: ccca639fe0b3a8104ff9fcfa285f1134706032b8

Feature expression merge - combines feature expression from several experiments ========================================================================= Workflows merges RPKM (by default) gene expression from several experiments based on the values from GeneId, Chrom, TxStart, TxEnd and Strand columns (by default). Reported unique columns are renamed based on the experiments names.

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

Path: workflows/feature-merge.cwl

Branch/Commit ID: 581156366f91861bd4dbb5bcb59f67d468b32af3

https://github.com/nci-gdc/gdc-dnaseq-cwl.git

Path: workflows/bamfastq_align/exome_metrics.cwl

Branch/Commit ID: dd7f86b3cc10eb1cda07dc2fc279ba2529c8ad61

. This ChIP-Seq pipeline is based on the original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **ChIP-Seq** basic analysis workflow for a **single-read** experiment with Trim Galore. ### Data Analysis SciDAP starts from the .fastq files which most DNA cores and commercial NGS companies return. Starting from raw data allows us to ensure that all experiments have been processed in the same way and simplifies the deposition of data to GEO upon publication. The data can be uploaded from users computer, downloaded directly from an ftp server of the core facility by providing a URL or from GEO by providing SRA accession number. Our current pipelines include the following 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 end of read. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nexterra/Tn5 adapters. 2. QC 3. (Optional) trimming adapters on 5' or 3' end by the specified number of bases. 4. Mapping reads with BowTie. Only uniquely mapped reads with less than 3 mismatches are used in the downstream analysis. Results are saved as a .bam file. 5. (Optional) Removal of duplicates (reads/pairs of reads mapping to exactly same location). This step is used to remove reads overamplified in PCR. Unfortunately, it may also remove \"good\" reads. We usually do not remove duplicates unless the library is heavily duplicated. Please note that MACS2 will remove 'excessive' duplicates during peak calling ina smart way (those not supported by other nearby reads). 6. Peakcalling by MACS2. (Optionally), it is possible to specify read extension length for MACS2 to use if the length determined automatically is wrong. 7. Generation of BigWig coverage files for display on the browser. The coverage shows the number of fragments at each base in the genome normalized to the number of millions of mapped reads. In the case of PE sequencing the fragments are real, but in the case of single reads the fragments are estimated by extending reads to the average fragment length found by MACS2 or specified by the user in 6. ### Details _Trim Galore_ is a wrapper around [Cutadapt](https://github.com/marcelm/cutadapt) and [FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, reads coverage in a form of BigWig file, peaks calling data in a form of narrowPeak or broadPeak files, islands with the assigned nearest genes and region type, data for average tag density plot (on the base of BAM file). Workflow starts with step *fastx\_quality\_stats* from FASTX-Toolkit to calculate quality statistics for input FASTQ file. At the same time `bowtie` is used to align reads from input FASTQ file to reference genome *bowtie\_aligner*. The output of this step is unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` *samtools\_sort\_index*. Based on workflow’s input parameters indexed and sorted BAM file can be processed by `samtools rmdup` *samtools\_rmdup* to get rid of duplicated reads. If removing duplicates is not required the original input BAM and BAI files return. Otherwise step *samtools\_sort\_index\_after\_rmdup* repeat `samtools sort` and `samtools index` with BAM and BAI files. Right after that `macs2 callpeak` performs peak calling *macs2\_callpeak*. On the base of returned outputs the next step *macs2\_island\_count* calculates the number of islands and estimated fragment size. If the last one is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (*macs2\_callpeak\_forced*). If at the very beginning it was set in workflow input parameters to force run peak calling with fixed fragment size, this step is skipped and the original peak calling results are saved. In the next step workflow again calculates the number of islands and estimates fragment size (*macs2\_island\_count\_forced*) for the data obtained from *macs2\_callpeak\_forced* step. If the last one was skipped the results from *macs2\_island\_count\_forced* step are equal to the ones obtained from *macs2\_island\_count* step. Next step (*macs2\_stat*) is used to define which of the islands and estimated fragment size should be used in workflow output: either from *macs2\_island\_count* step or from *macs2\_island\_count\_forced* step. If input trigger of this step is set to True it means that *macs2\_callpeak\_forced* step was run and it returned different from *macs2\_callpeak* step results, so *macs2\_stat* step should return [fragments\_new, fragments\_old, islands\_new], if trigger is False the step returns [fragments\_old, fragments\_old, islands\_old], where sufix \"old\" defines results obtained from *macs2\_island\_count* step and sufix \"new\" - from *macs2\_island\_count\_forced* step. The following two steps (*bamtools\_stats* and *bam\_to\_bigwig*) are used to calculate coverage on the base of input BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads number which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it in BED format. The last one is then being sorted and converted to BigWig format by bedGraphToBigWig tool from UCSC utilities. Step *get\_stat* is used to return a text file with statistics in a form of [TOTAL, ALIGNED, SUPRESSED, USED] reads count. Step *island\_intersect* assigns genes and regions to the islands obtained from *macs2\_callpeak\_forced*. Step *average\_tag\_density* is used to calculate data for average tag density plot on the base of BAM file.

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

Path: workflows/trim-chipseq-se.cwl

Branch/Commit ID: 9e3c3e65c19873cd1ed3cf7cc3b94ebc75ae0cc5

CNV Manta calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/cnv_manta.cwl

Branch/Commit ID: f248ac3ccbf6840af721251c7e9451abd9b2c09f