Explore Workflows

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

Path: task_types/tt_kmer_build_tree.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c

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

Path: task_types/tt_kmer_top_n.cwl

Branch/Commit ID: e71779665f42fcf34601b0f65e030bb0dd47fa79

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: 9ee330737f4603e4e959ffe786fbb2046db70a00

An analysis workflow for paired-end RNA-Seq sequencing experiments that have used the ERCC ExFold Mix1 spike-in RNA for normalization. ### __Inputs__ *General Info (required\*):* - Experiment short name/Alias* - a unique name for the sample (e.g. what was used on tubes while processing it) - Cells* - sample cell type or organism name - Conditions* - experimental condition name - Catalog # - catalog number for cells from vender/supplier - Primary [genome index](https://scidap.com/tutorials/basic/genome-indices) for read alignment* - preprocessed genome index of sample organism for primary alignment and transcript counting - FASTQ file for R1* - read 1 file of a pair-end library - FASTQ file for R2* - read 2 file of a pair-end library - ERCC ExFold Mix1 Dilution Factor* - for calculating expected molecules per cell for each ERCC_ID (e.g. if diluting by 1:10, enter the value 0.10 for this input) - Volume (uL) of diluted mix per 1 million cells added to extracted RNA sample* - for calculating expected molecules per cell for each ERCC_ID (e.g if your sample has 4,000,000 cells and 8uL of diluted mix1 was added, enter the value 2.0 for this input) *Advanced:* - Number of bases to clip from the 3p end - used by bowtie aligner to trim <int> bases from 3' (right) end of reads - Number of bases to clip from the 5p end - used by bowtie aligner to trim <int> bases from 5' (left) end of reads - Call samtools rmdup to remove duplicates from sorted BAM file? - toggle on/off to remove duplicate reads from analysis - Fragment Length Filter will retain fragments between set base pair (bp) ranges for peak analysis - drop down menu - `Default_Range` retains fragments <1000 bp - `Histone_Binding_Library` retains fragments between 130-300 bp - `Transcription_Factor_Binding_Library` retains fragments <130 bp - Max distance (bp) from gene TSS (in both directions) overlapping which the peak will be assigned to the promoter region - default set to `1000` - Max distance (bp) from the promoter (only in upstream directions) overlapping which the peak will be assigned to the upstream region - default set to `20000` - Number of threads for steps that support multithreading - default set to `2` ### __Outputs__ Intermediate and final downloadable outputs include: - quality statistics and visualizations for both R1/R2 input FASTQ files - coordinate sorted BAM file with associated BAI file for primary alignment - read pileup/coverage in BigWig format (raw depth values) for IGV - common tss, gene, and isoform counts (Total, RPKM, and spike-normalized RPKM) ### __Data Analysis Steps__ Current workflow must be used with paired-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 2 (after running STAR) 5. Generate BigWig file using 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 8. Use isoforms RPKM file as input into ERCC normalization script that will calculate the expected molecules per cell (a proxy for copy number) for each ERCC_ID by multiplying “molecules_per_uL_mix1” (col 2 in 'ercc_exfold_mix1_expected_counts.tsv' file in the docker image) by the input dilution factor then multiplying by the input volume per 10^6 cell and dividing by 1,000,000 cells 9. A linear regression is performed on the resulting spike-in scatter plot of molecule per cell vs RPKM 10. The RPKM value for each gene is input to the resulting function to produce an expected copy number value that will be used as the renormalized value for the gene to be used in downstream differential expression ### __References__ - Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012, 9:357-359. - Twelve years of SAMtools and BCFtools. Danecek et al. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008 - Lovén, J. et al. Revisiting global gene expression analysis. Cell 151(3), 476–482 (2012).

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

Path: workflows/trim-rnaseq-pe-ercc.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

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

Path: cwltool/schemas/v1.0/v1.0/any-type-compat.cwl

Branch/Commit ID: 1eb6bfe3c77aebaf69453a669d21ae7a5a78056f

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 with Trim Galore. The pipeline was adapted for ATAC-Seq paired-end data analysis by updating genome coverage step. _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. A [FASTQ](http://maq.sourceforge.net/fastq.shtml) input file has to be provided. In outputs it returns coordinate sorted BAM file alongside with index BAI file, quality statistics for both the input FASTQ files, 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 running fastx_quality_stats (steps fastx_quality_stats_upstream and fastx_quality_stats_downstream) from FASTX-Toolkit to calculate quality statistics for both upstream and downstream input FASTQ files. At the same time Bowtie is used to align reads from input FASTQ files to reference genome (Step bowtie_aligner). The output of this step is unsorted SAM file which is being sorted and indexed by samtools sort and samtools index (Step samtools_sort_index). Depending on workflow’s input parameters indexed and sorted BAM file could be processed by samtools rmdup (Step samtools_rmdup) to remove all possible read duplicates. In a case when removing duplicates is not necessary the step returns original input BAM and BAI files without any processing. If the duplicates were removed the following step (Step samtools_sort_index_after_rmdup) reruns samtools sort and samtools index with BAM and BAI files, if not - the step returns original unchanged input files. Right after that macs2 callpeak performs peak calling (Step macs2_callpeak). On the base of returned outputs the next step (Step macs2_island_count) calculates the number of islands and estimated fragment size. If the last one is less that 80 (hardcoded in a workflow) macs2 callpeak is rerun again with forced fixed fragment size value (Step 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 estimated fragment size (Step 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 (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 (Step 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. To adapt the pipeline for ATAC-Seq data analysis we calculate genome coverage using only the first 9 bp from every read. 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-atacseq-pe.cwl

Branch/Commit ID: b5e16e359007150647b14dc6e038f4eb8dccda79

The original [BioWardrobe's](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) **RNA-Seq** basic analysis for a **paired-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 paired-end RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ files according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ files and generate quality statistics files 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) 4. Generate BigWig file on the base of sorted BAM file 5. Map input FASTQ files to predefined rRNA reference indices using Bowtie to define the level of rRNA contamination; export resulted statistics to file 6. 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-pe.cwl

Branch/Commit ID: 4dcc405133f22c63478b6091fb5f591b6be8950f

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

Path: definitions/pipelines/alignment_exome_mouse.cwl

Branch/Commit ID: 9143dc4ebacb9e1df36a712b0be6fa5d982b0c4f

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

Path: definitions/subworkflows/bam_to_trimmed_fastq.cwl

Branch/Commit ID: 3042812447d9e8889c6118986490e9c9b9b13223

https://github.com/nal-i5k/organism_onboarding.git

Path: CreateSymlink-workflow.cwl

Branch/Commit ID: add45db6f08de518e224bdc3c04094fd69cad2d2