Explore Workflows
View already parsed workflows here or click here to add your own
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Genome conversion and annotation
Workflow for genome annotation from EMBL format |
https://git.wur.nl/unlock/cwl.git
Path: cwl/workflows/workflow_sapp_microbes.cwl Branch/Commit ID: cd0c19d51068c5407cd70b718a561d4662819d87 |
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kmer_cache_retrieve
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https://github.com/ncbi/pgap.git
Path: task_types/tt_kmer_cache_retrieve.cwl Branch/Commit ID: ef266744578e2dcbce57c110c6fa3b9eee91e316 |
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allele-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-alignreads-se-pe.cwl Branch/Commit ID: 3ceeb2e90f49579369b2e10485908516348381a9 |
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SoupX (workflow) - an R package for the estimation and removal of cell free mRNA contamination
Wrapped in a workflow SoupX tool for easy access to Cell Ranger pipeline compressed outputs. |
https://github.com/datirium/workflows.git
Path: tools/soupx-subworkflow.cwl Branch/Commit ID: 1a46cb0e8f973481fe5ae3ae6188a41622c8532e |
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cluster_blastp_wnode and gpx_qdump combined
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https://github.com/ncbi/pgap.git
Path: task_types/tt_cluster_and_qdump.cwl Branch/Commit ID: c18a7e5164cb6b19f06b3d1e869407c118a87f7e |
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Bismark Methylation - pipeline for BS-Seq data analysis
Sequence reads are first cleaned from adapters and transformed into fully bisulfite-converted forward (C->T) and reverse read (G->A conversion of the forward strand) versions, before they are aligned to similarly converted versions of the genome (also C->T and G->A converted). Sequence reads that produce a unique best alignment from the four alignment processes against the bisulfite genomes (which are running in parallel) are then compared to the normal genomic sequence and the methylation state of all cytosine positions in the read is inferred. A read is considered to align uniquely if an alignment has a unique best alignment score (as reported by the AS:i field). If a read produces several alignments with the same number of mismatches or with the same alignment score (AS:i field), a read (or a read-pair) is discarded altogether. On the next step we extract the methylation call for every single C analysed. The position of every single C will be written out to a new output file, depending on its context (CpG, CHG or CHH), whereby methylated Cs will be labelled as forward reads (+), non-methylated Cs as reverse reads (-). The output of the methylation extractor is then transformed into a bedGraph and coverage file. The bedGraph counts output is then used to generate a genome-wide cytosine report which reports the number on every single CpG (optionally every single cytosine) in the genome, irrespective of whether it was covered by any reads or not. As this type of report is informative for cytosines on both strands the output may be fairly large (~46mn CpG positions or >1.2bn total cytosine positions in the human genome). |
https://github.com/datirium/workflows.git
Path: workflows/bismark-methylation-se.cwl Branch/Commit ID: 6bf56698c6fe6e781723dea32bc922b91ef49cf3 |
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Seurat Cluster
Seurat Cluster ============== Runs filtering, integration, and clustering analyses for Cell Ranger Count Gene Expression or Cell Ranger Aggregate experiments. |
https://github.com/datirium/workflows.git
Path: workflows/seurat-cluster.cwl Branch/Commit ID: c9e7f3de7f6ba38ee663bd3f9649e8d7dbac0c86 |
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align_sort_sa
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https://github.com/ncbi/pgap.git
Path: task_types/tt_align_sort_sa.cwl Branch/Commit ID: 664e99a23a3ed4ba36c08323ac597c4fbcd88df1 |
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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: 104059e07a2964673e21d371763e33c0afeb2d03 |
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bact_get_kmer_reference
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https://github.com/ncbi/pgap.git
Path: task_types/tt_bact_get_kmer_reference.cwl Branch/Commit ID: c18a7e5164cb6b19f06b3d1e869407c118a87f7e |
