Explore Workflows
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Cellranger aggr - aggregates data from multiple Cellranger runs
Devel version of Single-Cell Cell Ranger Aggregate ================================================== Workflow calls \"cellranger aggr\" command to combine output files from \"cellranger count\" (the molecule_info.h5 file from each run) into a single feature-barcode matrix containing all the data. When combining multiple GEM wells, the barcode sequences for each channel are distinguished by a GEM well suffix appended to the barcode sequence. Each GEM well is a physically distinct set of GEM partitions, but draws barcode sequences randomly from the pool of valid barcodes, known as the barcode whitelist. To keep the barcodes unique when aggregating multiple libraries, we append a small integer identifying the GEM well to the barcode nucleotide sequence, and use that nucleotide sequence plus ID as the unique identifier in the feature-barcode matrix. For example, AGACCATTGAGACTTA-1 and AGACCATTGAGACTTA-2 are distinct cell barcodes from different GEM wells, despite having the same barcode nucleotide sequence. This number, which tells us which GEM well this barcode sequence came from, is called the GEM well suffix. The numbering of the GEM wells will reflect the order that the GEM wells were provided in the \"molecule_info_h5\" and \"gem_well_labels\" inputs. When combining data from multiple GEM wells, the \"cellranger aggr\" pipeline automatically equalizes the average read depth per cell between groups before merging. This approach avoids artifacts that may be introduced due to differences in sequencing depth. It is possible to turn off normalization or change the way normalization is done through the \"normalization_mode\" input. The \"none\" value may be appropriate if you want to maximize sensitivity and plan to deal with depth normalization in a downstream step. |
https://github.com/datirium/workflows.git
Path: workflows/cellranger-aggr.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f |
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SoupX - an R package for the estimation and removal of cell free mRNA contamination
Devel version of Single-Cell Advanced Cell Ranger Pipeline (SoupX) ================================================================= |
https://github.com/datirium/workflows.git
Path: workflows/soupx.cwl Branch/Commit ID: a8eaf61c809d76f55780b14f2febeb363cf6373f |
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umi duplex alignment fastq workflow
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https://github.com/genome/analysis-workflows.git
Path: definitions/pipelines/alignment_umi_duplex.cwl Branch/Commit ID: dc2c019c1aa24cc01b451a0f048cf94a35f163c4 |
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extract_gencoll_ids
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https://github.com/ncbi/pgap.git
Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 6a29751f2b16659c1592f1e94837c989e68f3b8b |
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extract_capture_kit.cwl
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https://github.com/NCI-GDC/gdc-dnaseq-cwl.git
Path: workflows/bamfastq_align/extract_capture_kit.cwl Branch/Commit ID: 1326fb7fedca91a274fb7596c9052a4d279eacf9 |
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tt_kmer_top_n.cwl
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_top_n.cwl Branch/Commit ID: 09774c78a965dd8f6c315597a53eef5998a3c1b6 |
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extract_gencoll_ids
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https://github.com/ncbi/pgap.git
Path: task_types/tt_extract_gencoll_ids.cwl Branch/Commit ID: 09774c78a965dd8f6c315597a53eef5998a3c1b6 |
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Generate genome index STAR RNA
Workflow makes indices for [STAR](https://github.com/alexdobin/STAR) v2.5.3a (03/17/2017) PMID: [23104886](https://www.ncbi.nlm.nih.gov/pubmed/23104886). It performs the following steps: 1. Runs `STAR --runMode genomeGenerate` to generate indices, based on [FASTA](http://zhanglab.ccmb.med.umich.edu/FASTA/) and [GTF](http://mblab.wustl.edu/GTF2.html) input files, returns results as an array of files 2. Transforms array of files into [Direcotry](http://www.commonwl.org/v1.0/CommandLineTool.html#Directory) data type 3. Separates *chrNameLength.txt* file as an output |
https://github.com/datirium/workflows.git
Path: workflows/star-index.cwl Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0 |
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Align reference proteins plane complete workflow
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https://github.com/ncbi/pgap.git
Path: protein_alignment/wf_protein_alignment.cwl Branch/Commit ID: 803f6367d1b279a7b6dc1a4e8ae43f1bbec9f760 |
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RNA-Seq pipeline paired-end 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 pair-end** 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 the pair-end 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-pe-dutp-mitochondrial.cwl Branch/Commit ID: 2b8146f76595f0c4d8bf692de78b21280162b1d0 |
