Explore Workflows

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

Path: testdata/scatter-wf1_v1_0.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

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

Path: testdata/count-lines7-wf_v1_0.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

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

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

Path: workflows/cellranger-arc-count.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

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: fa4f172486288a1a9d23864f1d6962d85a453e16

Cell Ranger Aggregate (RNA+ATAC) Combines outputs from multiple runs of “Cell Ranger Count (RNA+ATAC)” pipeline. The results of this workflow are primarily used in “Single-Cell Multiome ATAC and RNA-Seq Filtering Analysis” pipeline.

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

Path: workflows/cellranger-arc-aggr.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

A CWL workflow for discovering known or novel miRNAs from deep sequencing data using the miRDeep2 tool. The ExoCarta exosome database is also used for identifying exosome-related miRNAs, and TargetScan's organism-specific databases are used for identifying miRNA gene targets. ## __Outputs__ #### Primary Output files: - mirs_known.tsv, detected known mature miRNAs, \"Known miRNAs\" tab - mirs_novel.tsv, detected novel mature miRNAs, \"Novel miRNAs\" tab #### Secondary Output files: - mirs_known_exocarta_deepmirs.tsv, list of detected miRNA also in ExoCarta's exosome database, \"Detected Exosome miRNAs\" tab - mirs_known_gene_targets.tsv, pre-computed gene targets of known mature mirs, downloadable - known_mirs_mature.fa, known mature mir sequences, downloadable - known_mirs_precursor.fa, known precursor mir sequences, downloadable - novel_mirs_mature.fa, novel mature mir sequences, downloadable - novel_mirs_precursor.fa, novel precursor mir sequences, downloadable #### Reports: - overview.md (input list, alignment & mir metrics), \"Overview\" tab - mirdeep2_result.html, summary of mirdeep2 results, \"miRDeep2 Results\" tab ## __Inputs__ #### General Info - Sample short name/Alias: unique name for sample - Experimental condition: condition, variable, etc name (e.g. \"control\" or \"20C 60min\") - Cells: name of cells used for the sample - Catalog No.: vender catalog number if available - Bowtie2 index: Bowtie2 index directory of the reference genome. - Reference Genome FASTA: Reference genome FASTA file to be used for alignment. - Genome short name: Name used for setting organism name, genus, species, and tax ID. - Input FASTQ file: FASTQ file from a single-end miRNA sequencing run. #### Advanced - Adapter: Adapter sequence to be trimmed from miRNA sequence reads. (Default: TCGTAT) - Threads: Number of threads to use for steps that support multithreading (Default: 4). ## Hints & Tips: #### For the identification of novel miRNA candidates, the following may be used as a filtering guideline: 1. miRDeep score > 4 (some authors use 1) 2. not present a match with rfam 3. should present a significant RNAfold (\"yes\") 4. a number of mature reads > 10 5. if applicable, novel mir must be expressed in multiple samples #### For filtering mirbase by organism. | genome | organism | division | name | tree | NCBI-taxid | | ---- | --- | --- | ----------- | ----------- | ----------- | | hg19 | hsa | HSA | Homo sapiens | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Primates;Hominidae | 9606 | | hg38 | hsa | HSA | Homo sapiens | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Primates;Hominidae | 9606 | | mm10 | mmu | MMU | Mus musculus | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Rodentia | 10090 | | rn7 | rno | RNO | Rattus norvegicus | Metazoa;Bilateria;Deuterostoma;Chordata;Vertebrata;Mammalia;Rodentia | 10116 | | dm3 | dme | DME | Drosophila melanogaster | Metazoa;Bilateria;Ecdysozoa;Arthropoda;Hexapoda | 7227 | ## __Data Analysis Steps__ 1. The miRDeep2 Mapper module processes Illumina FASTQ output and maps it to the reference genome. 2. The miRDeep2 miRDeep2 module identifies known and novel (mature and precursor) miRNAs. 3. The ExoCarta database of miRNA found in exosomes is then used to find overlap between mirs_known.tsv and exosome associated miRNAs. 4. Finally, TargetScan organism-specific miRNA gene target database is used to find overlap between mirs_known.tsv and gene targets. ## __References__ 1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3245920 2. https://github.com/rajewsky-lab/mirdeep2 3. https://biocontainers.pro/tools/mirdeep2 4. https://www.mirbase.org/ 5. http://exocarta.org/index.html 6. https://www.targetscan.org/vert_80/

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

Path: workflows/mirna-mirdeep2-se.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Single-Cell RNA-Seq Dimensionality Reduction Analysis Removes noise and confounding sources of variation by reducing dimensionality of gene expression data from the outputs of “Single-Cell RNA-Seq Filtering Analysis” or “Single-Cell Multiome ATAC and RNA-Seq Filtering Analysis” pipelines. The results of this workflow are primarily used in “Single-Cell RNA-Seq Cluster Analysis” or “Single-Cell WNN Cluster Analysis” pipelines.

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

Path: workflows/sc-rna-reduce.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Workflow runs homer-make-tag-directory.cwl tool using scatter for the following inputs - bam_file - fragment_size - total_reads `dotproduct` is used as a `scatterMethod`, so one element will be taken from each array to construct each job: 1) bam_file[0] fragment_size[0] total_reads[0] 2) bam_file[1] fragment_size[1] total_reads[1] ... N) bam_file[N] fragment_size[N] total_reads[N] `bam_file`, `fragment_size` and `total_reads` arrays should have the identical order.

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

Path: tools/heatmap-prepare.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Deprecated. AltAnalyze ICGS

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

Path: workflows/altanalyze-icgs.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16

Single-Cell Manual Cell Type Assignment Assigns identities to cells clustered with any of the “Single-Cell Cluster Analysis” pipelines. For “Single-Cell RNA-Seq Cluster Analysis” the results of this workflow are used in the “Single-Cell RNA-Seq Differential Expression Analysis”, “Single-Cell RNA-Seq Trajectory Analysis”, and — when combined with outputs from the “Cell Ranger Count (RNA+VDJ)” or “Cell Ranger Aggregate (RNA, RNA+VDJ)” workflow — in the “Single-Cell Immune Profiling Analysis” pipeline. For “Single-Cell ATAC-Seq Cluster Analysis”, the results of this workflow are used in the “Single-Cell ATAC-Seq Differential Accessibility Analysis” and “Single-Cell ATAC-Seq Genome Coverage” pipelines. For “Single-Cell WNN Cluster Analysis”, the results of this workflow are used in all of the above, except the “Single-Cell Immune Profiling Analysis” pipeline.

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

Path: workflows/sc-ctype-assign.cwl

Branch/Commit ID: fa4f172486288a1a9d23864f1d6962d85a453e16