Explore Workflows

https://github.com/hubmapconsortium/salmon-rnaseq.git

Path: pipeline.cwl

Branch/Commit ID: 85892d9bfdccc57a6ed8231014bbd606c38f2b86

https://github.com/hubmapconsortium/azimuth-annotate.git

Path: pipeline.cwl

Branch/Commit ID: 0da4327ec159bd913ff080fc53897b27ed263bdd

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-007_nojs.cwl

Branch/Commit ID: 707ebcd2173889604459c5f4ffb55173c508abb3

Cell Ranger Count (RNA) Quantifies single-cell gene expression of the sequencing data from a single 10x Genomics library. The results of this workflow are primarily used in either “Single-Cell RNA-Seq Filtering Analysis” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” pipelines.

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

Path: workflows/single-cell-preprocess-cellranger.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

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-dutp.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

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

Path: testdata/workflow_input_sf_expr.cwl

Branch/Commit ID: 15c8467d6d3c31a95ccc682095cf34aad125ca8c

Single-Cell ATAC-Seq Cluster Analysis Clusters cells by similarity of chromatin accessibility data from the outputs of the “Single-Cell ATAC-Seq Dimensionality Reduction Analysis” pipeline. The results of this workflow are used in the “Single-Cell Manual Cell Type Assignment”, “Single-Cell ATAC-Seq Differential Accessibility Analysis”, and “Single-Cell ATAC-Seq Genome Coverage” pipelines.

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

Path: workflows/sc-atac-cluster.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: 1cfd46014be8d867044cb10d1ddde0cb3068ee84

This workflow taxonomically classifies paired-end sequencing reads in FASTQ format, that have been optionally adapter trimmed with trimgalore, using Kraken2 and a user-selected pre-built database from a list of [genomic index files](https://benlangmead.github.io/aws-indexes/k2). ### __Inputs__ Kraken2 database for taxonomic classification: - [Viral (0.5 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_viral_20221209.tar.gz), all refseq viral genomes - [MinusB (8.7 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_minusb_20221209.tar.gz), standard minus bacteria (archaea, viral, plasmid, human1, UniVec_Core) - [PlusPFP-16 (15.0 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_pluspfp_16gb_20221209.tar.gz), standard (archaea, bacteria, viral, plasmid, human1, UniVec_Core) + (protozoa, fungi & plant) capped at 16 GB (shrunk via random kmer downselect) - [EuPathDB46 (34.1 GB)](https://genome-idx.s3.amazonaws.com/kraken/k2_eupathdb48_20201113.tar.gz), eukaryotic pathogen genomes with contaminants removed (https://veupathdb.org/veupathdb/app) - [16S_gg_13_5 (73 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Greengenes13.5_20200326.tgz), Greengenes 16S rRNA database ([release 13.5](https://greengenes.secondgenome.com/?prefix=downloads/greengenes_database/gg_13_5/), 20200326)\n - [16S_silva_138 (112 MB)](https://genome-idx.s3.amazonaws.com/kraken/16S_Silva138_20200326.tgz), SILVA 16S rRNA database ([release 138.1](https://www.arb-silva.de/documentation/release-1381/), 20200827) Read 1 file: - FASTA/Q input R1 from a paired end library Read 2 file: - FASTA/Q input R2 from a paired end library Number of threads for steps that support multithreading: - Number of threads for steps that support multithreading - default set to `4` Advanced Inputs Tab (Optional): - Number of bases to clip from the 3p end - Number of bases to clip from the 5p end ### __Outputs__ - k2db, an upstream database used by kraken2 classifier ### __Data Analysis 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 ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. (Optional) Clipping of 5' and/or 3' end by the specified number of bases. 4. Mapping reads to primary genome index with Bowtie. ### __References__ - Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0

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

Path: workflows/kraken2-classify-pe.cwl

Branch/Commit ID: 261c0232a7a40880f2480b811ed2d7e89c463869

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

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

Path: workflows/deseq.cwl

Branch/Commit ID: 564156a9e1cc7c3679a926c479ba3ae133b1bfd4