Explore Workflows
View already parsed workflows here or click here to add your own
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ani_top_n
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https://github.com/ncbi/pgap.git
Path: task_types/tt_ani_top_n.cwl Branch/Commit ID: 61e3752f1f5e2ee498fa024c235226f8580be942 |
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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: 935a78f1aff757f977de4e3672aefead3b23606b |
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Build STAR indices
Workflow runs [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886) to build indices for reference genome provided in a single FASTA file as fasta_file input and GTF annotation file from annotation_gtf_file input. Generated indices are saved in a folder with the name that corresponds to the input genome. |
https://github.com/datirium/workflows.git
Path: workflows/star-index.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620 |
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Cell Ranger ARC Count Gene Expression + ATAC
Cell Ranger ARC Count Gene Expression + ATAC ============================================ |
https://github.com/datirium/workflows.git
Path: workflows/cellranger-arc-count.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620 |
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RNA-Seq pipeline single-read stranded mitochondrial
Slightly changed 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. An additional steps were added to map data to mitochondrial chromosome only and then merge the output. Experiment files in [FASTQ](http://maq.sourceforge.net/fastq.shtml) format either compressed or not can be used. Current workflow should be used only with single-read strand specific RNA-Seq data. It performs the following steps: 1. `STAR` to align reads from input FASTQ file according to the predefined reference indices; generate unsorted BAM file and alignment statistics file 2. `fastx_quality_stats` to analyze input FASTQ file and generate quality statistics file 3. `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-mitochondrial.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620 |
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AltAnalyze Build Reference Indices
AltAnalyze Build Reference Indices ================================== |
https://github.com/datirium/workflows.git
Path: workflows/altanalyze-prepare-genome.cwl Branch/Commit ID: e45ab1b9ac5c9b99fdf7b3b1be396dc42c2c9620 |
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AltAnalyze Build Reference Indices
AltAnalyze Build Reference Indices ================================== |
https://github.com/datirium/workflows.git
Path: workflows/altanalyze-prepare-genome.cwl Branch/Commit ID: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e |
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allele-vcf-alignreads-se-pe.cwl
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-vcf-alignreads-se-pe.cwl Branch/Commit ID: 58d8b329a6531237205cc36d70604ab0be064402 |
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assm_assm_blastn_wnode
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https://github.com/ncbi/pgap.git
Path: task_types/tt_assm_assm_blastn_wnode.cwl Branch/Commit ID: 664e99a23a3ed4ba36c08323ac597c4fbcd88df1 |
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Create Genomic Collection for Bacterial Pipeline
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https://github.com/ncbi/pgap.git
Path: genomic_source/wf_genomic_source.cwl Branch/Commit ID: be32f1363f9a9a9247d738e9593b207e9c5172c8 |
