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Graph	Name	Retrieved From	View
	Trim Galore SMARTer RNA-Seq pipeline paired-end strand specific https://chipster.csc.fi/manual/library-type-summary.html Modified original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) RNA-Seq basic analysis for a pair-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 files 2. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 3. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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 files 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-pe-smarter-dutp.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620
	protein annotation Proteins - predict, filter, cluster, identify, annotate	https://github.com/MG-RAST/pipeline.git Path: CWL/Workflows/protein-filter-annotation.workflow.cwl Branch/Commit ID: 091374dc59a23966338638a668ae397d4ee20b2f
	hello_world_checker.cwl	https://github.com/Richard-Hansen/hello_world.git Path: hello_world_checker.cwl Branch/Commit ID: 3f52d3e0e82c2df70ecada74cab879c490b9c2ee
	Cut-n-Run pipeline paired-end Experimental pipeline for Cut-n-Run analysis. Uses mapping results from the following experiment types: - `chipseq-pe.cwl` - `trim-chipseq-pe.cwl` - `trim-atacseq-pe.cwl` Note, the upstream analyses should not have duplicates removed	https://github.com/datirium/workflows.git Path: workflows/trim-chipseq-pe-cut-n-run.cwl Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4
	ChIP-Seq pipeline paired-end 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 paired-end experiment. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. The pipeline produces a sorted BAM file alongside with index BAI file, quality statistics of the input FASTQ file, coverage by estimated fragments as a 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. 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 an unsorted SAM file which is being sorted and indexed by `samtools sort` and `samtools index` samtools\_sort\_index. Depending 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 BAM and BAI files are returned. Otherwise step samtools\_sort\_index\_after\_rmdup repeat `samtools sort` and `samtools index` with BAM and BAI files without duplicates. Next `macs2 callpeak` performs peak calling macs2\_callpeak and the next step reports macs2\_island\_count the number of islands and estimated fragment size. If the latter is less that 80bp (hardcoded in the workflow) `macs2 callpeak` is rerun again with forced fixed fragment size value (macs2\_callpeak\_forced). It is also possible to force MACS2 to use pre set fragment size in the first place. 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 from BAM file and save it in BigWig format. For that purpose bamtools stats returns the number of mapped reads which is then used as scaling factor by bedtools genomecov when it performs coverage calculation and saves it as a BEDgraph file whichis then 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 nearest 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 from the BAM file.	https://github.com/datirium/workflows.git Path: workflows/chipseq-pe.cwl Branch/Commit ID: 8049a781ac4aae579fbd3036fa0bf654532f15be
	Filter differentially expressed genes from DESeq for Tag Density Profile Analyses 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: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620
	RNA-Seq pipeline single-read stranded mitochondrial Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b
	allele-alignreads-se-pe.cwl Workflow maps FASTQ files from `fastq_files` input into reference genome `reference_star_indices_folder` and insilico generated `insilico_star_indices_folder` genome (concatenated genome for both `strain1` and `strain2` strains). For both genomes STAR is run with `outFilterMultimapNmax` parameter set to 1 to discard all of the multimapped reads. For insilico genome SAM file is generated. Then it's splitted into two SAM files based on strain names and then sorted by coordinates into the BAM format. For reference genome output BAM file from STAR slignment is also coordinate sorted.	https://github.com/datirium/workflows.git Path: subworkflows/allele-alignreads-se-pe.cwl Branch/Commit ID: 58d8b329a6531237205cc36d70604ab0be064402
	group-isoforms-batch.cwl Workflow runs group-isoforms.cwl tool using scatter for isoforms_file input. genes_filename and common_tss_filename inputs are ignored.	https://github.com/datirium/workflows.git Path: tools/group-isoforms-batch.cwl Branch/Commit ID: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e
	Trim Galore ChIP-Seq pipeline single-read . 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: 4360fb2e778ecee42e5f78f83b78c65ab3a2b1df