Explore Workflows

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/scatter-wf2_v1_2.cwl

Branch/Commit ID: 0ab1d42d10f7311bb4032956c4a6f3d2730d9507

https://github.com/common-workflow-language/cwl-utils.git

Path: testdata/count-lines7-wf_v1_0.cwl

Branch/Commit ID: e78db9870cb744fe36674f43b3223c688e9989e1

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

Path: task_types/tt_kmer_gc_extract_wnode.cwl

Branch/Commit ID: ca75d68eb74c93b35b404ec7908dc5b260e16466

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

Path: definitions/pipelines/somatic_exome_gathered.cwl

Branch/Commit ID: 789267ce0e3fed674ea5212a562315218fcf1bfc

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

Path: definitions/pipelines/immuno.cwl

Branch/Commit ID: 3034168d652bfa930ba09af20e473a4564a8010d

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

Path: task_types/tt_taxonomy_check_16S.cwl

Branch/Commit ID: 49732e54e2fe2eafd2f82df3c482c73e642f6d64

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

Path: cwltool/schemas/v1.0/v1.0/scatter-wf4.cwl

Branch/Commit ID: 203797516329f7fb8aa5e763e6f9b331c63c3060

Packed ID: main

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: 93b844a80f4008cc973ea9b5efedaff32a343895

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

Path: vecscreen/vecscreen.cwl

Branch/Commit ID: 5282690e0f634a5f83107ba878fe62cbbb347408

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

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: ce433f771ebf5677c9f40858e2ae91b1a7e75d30