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Graph Name Retrieved From View
workflow graph kmer_cache_store

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

Path: task_types/tt_kmer_cache_store.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph Single-cell Reference Indices

Single-cell Reference Indices Builds a Cell Ranger and Cell Ranger ARC compatible reference folders from the custom genome FASTA and gene GTF annotation files

 https://github.com/Barski-lab/sc-seq-analysis.git

Path: workflows/sc-ref-indices-wf.cwl

Branch/Commit ID: 8614e5d20f5e81dce537216bd340cdbc1067bbc7
workflow graph sec-wf-out.cwl

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

Path: tests/wf/sec-wf-out.cwl

Branch/Commit ID: 981c03099f79b5aad74555787d406f695dd0b320
workflow graph bam to trimmed fastqs and biscuit alignments

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

Path: definitions/subworkflows/bam_to_trimmed_fastq_and_biscuit_alignments.cwl

Branch/Commit ID: a7838a5ca72b25db5c2af20a15f34303a839980e
workflow graph kmer_seq_entry_extract_wnode

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

Path: task_types/tt_kmer_seq_entry_extract_wnode.cwl

Branch/Commit ID: 449f87c8365637e803ba66f83367e96f98c88f5c
workflow graph Nested workflow example

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

Path: tests/wf/nested.cwl

Branch/Commit ID: 981c03099f79b5aad74555787d406f695dd0b320
workflow graph RNA-Seq pipeline single-read strand specific

Note: should be updated The 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. 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-dutp.cwl

Branch/Commit ID: 12c29f88855329192bfff977f046990031f04931
workflow graph align_sort_sa

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

Path: task_types/tt_align_sort_sa.cwl

Branch/Commit ID: bba6c580ab88e077f6aa2c2ee7c73159f3f9156e
workflow graph DESeq - differential gene expression analysis

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: 12c29f88855329192bfff977f046990031f04931
workflow graph RNA-Seq pipeline paired-end

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: 12c29f88855329192bfff977f046990031f04931