Explore Workflows
View already parsed workflows here or click here to add your own
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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 |
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gcaccess_from_list
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https://github.com/ncbi/pgap.git
Path: task_types/tt_gcaccess_from_list.cwl Branch/Commit ID: 5e92165ac2c11608ab2db42fe2d66eabe72dbb40 |
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PCA - Principal Component Analysis
Principal Component Analysis --------------- Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. |
https://github.com/datirium/workflows.git
Path: workflows/pca.cwl Branch/Commit ID: 935a78f1aff757f977de4e3672aefead3b23606b |
