Explore Workflows

DESeq2 Multi-factor Analysis Runs DeSeq2 multi-factor analysis with manual control over major parameters

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

Path: workflows/deseq-multi-factor.cwl

Branch/Commit ID: b4d578c2ba4713a5a22163d9f8c7105acda1f22e

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

Path: task_types/tt_blastp_wnode_naming.cwl

Branch/Commit ID: 16e3915d2a357e2a861b30911c832e5ddc0c1784

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

Path: task_types/tt_cluster_and_qdump.cwl

Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8

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-read** 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-read RNA-Seq data. It performs the following steps: 1. Use STAR to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. Use fastx_quality_stats to analyze input FASTQ file and generate quality statistics file 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) 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.cwl

Branch/Commit ID: 954bb2f213d97dfef1cddaf9e830169a92ad0c6b

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

Path: cwltool/schemas/v1.0/v1.0/step-valuefrom3-wf.cwl

Branch/Commit ID: 1eb6bfe3c77aebaf69453a669d21ae7a5a78056f

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

Path: definitions/pipelines/alignment_wgs_mouse.cwl

Branch/Commit ID: 789267ce0e3fed674ea5212a562315218fcf1bfc

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