Explore Workflows

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

Path: testdata/workflow_input_sf_expr_v1_1.cwl

Branch/Commit ID: 8058c7477097f90205dd7d8481781eb3737ea9c9

Deprecated. Single-cell Assign Cell Types ========================================= Assigns cell types to Seurat clusters.

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

Path: workflows/sc-assign-cell-types.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

A basic analysis workflow for paired-read CUT&RUN and CUT&TAG sequencing experiments. These sequencing library prep methods are ultra-sensitive chromatin mapping technologies compared to the ChIP-Seq methodology. Its primary benefits include 1) length filtering, 2) a higher signal-to-noise ratio, and 3) built-in normalization for between sample comparisons. This workflow utilizes the tool [SEACR (Sparse Enrichment Analysis of CUT&RUN data)](https://github.com/FredHutch/SEACR) which calls enriched regions in the target sequence data by identifying the top 1% of regions by area under the curve (of the alignment pileup). This workflow is loosely based on the [CUT-RUNTools-2.0 pipeline](https://github.com/fl-yu/CUT-RUNTools-2.0) pipeline, and the ChIP-Seq pipeline from [BioWardrobe](https://biowardrobe.com) [PubMed ID:26248465](https://www.ncbi.nlm.nih.gov/pubmed/26248465) was used as a CWL template. ### __Inputs__ *General Info (required\*):* - Experiment short name/Alias* - a unique name for the sample (e.g. what was used on tubes while processing it) - Cells* - sample cell type or organism name - Conditions* - experimental condition name - Catalog # - catalog number for cells from vender/supplier - Primary [genome index](https://scidap.com/tutorials/basic/genome-indices) for peak calling* - preprocessed genome index of sample organism for primary alignment and peak calling - Secondary [genome index](https://scidap.com/tutorials/basic/genome-indices) for spike-in normalization* - preprocessed genome index of spike-in organism for secondary alignment (of unaligned reads from primary alignment) and spike-in normalization, default should be E. coli K-12 - FASTQ file for R1* - read 1 file of a pair-end library - FASTQ file for R2* - read 2 file of a pair-end library *Advanced:* - Number of bases to clip from the 3p end - used by bowtie aligner to trim <int> bases from 3' (right) end of reads - Number of bases to clip from the 5p end - used by bowtie aligner to trim <int> bases from 5' (left) end of reads - Call samtools rmdup to remove duplicates from sorted BAM file? - toggle on/off to remove duplicate reads from analysis - Fragment Length Filter will retain fragments between set base pair (bp) ranges for peak analysis - drop down menu - `Default_Range` retains fragments <1000 bp - `Histone_Binding_Library` retains fragments between 130-300 bp - `Transcription_Factor_Binding_Library` retains fragments <130 bp - Max distance (bp) from gene TSS (in both directions) overlapping which the peak will be assigned to the promoter region - default set to `1000` - Max distance (bp) from the promoter (only in upstream directions) overlapping which the peak will be assigned to the upstream region - default set to `20000` - Number of threads for steps that support multithreading - default set to `2` ### __Outputs__ Intermediate and final downloadable outputs include: - IGV with gene, BigWig (raw and normalized), and stringent peak tracks - quality statistics and visualizations for both R1/R2 input FASTQ files - coordinate sorted BAM file with associated BAI file for primary alignment - read pileup/coverage in BigWig format (raw and normalized) - cleaned bed files (containing fragment coordinates), and spike-in normalized SEACR peak-called BED files from both \"stringent\" and \"relaxed\" mode. - stringent peak call bed file with nearest gene annotations per peak ### __Data Analysis Steps__ 1. Trimming the adapters with TrimGalore. - This step is particularly important when the reads are long and the fragments are short - resulting in sequencing adapters at the ends of reads. If adapter is not removed the read will not map. TrimGalore can recognize standard adapters, such as Illumina or Nextera/Tn5 adapters. 2. Generate quality control statistics of trimmed, unmapped sequence data 3. (Optional) Clipping of 5' and/or 3' end by the specified number of bases. 4. Mapping reads to primary genome index with Bowtie. - Only uniquely mapped reads with less than 3 mismatches are used in the downstream analysis. Results are then sorted and indexed. Final outputs are in bam/bai format, which are also used to extrapolate effects of additional sequencing based on library complexity. 5. (Optional) Removal of duplicates (reads/pairs of reads mapping to exactly the same location). - This step is used to remove reads overamplified during amplification of the library. Unfortunately, it may also remove \"good\" reads. We usually do not remove duplicates unless the library is heavily duplicated. 6. Mapping unaligned reads from primary alignment to secondary genome index with Bowtie. - This step is used to obtain the number of reads for normalization, used to scale the pileups from the primary alignment. After normalization, sample pileups/peak may then be appropriately compared to one another assuming an equal use of spike-in material during library preparation. Note the default genome index for this step should be *E. coli* K-12 if no spike-in material was called out in the library protocol. Refer to [Step 16](https://www.protocols.io/view/cut-amp-tag-data-processing-and-analysis-tutorial-e6nvw93x7gmk/v1?step=16#step-4A3D8C70DC3011EABA5FF3676F0827C5) of the \"CUT&Tag Data Processing and Analysis Tutorial\" by Zheng Y et al (2020). Protocol.io. 7. Formatting alignment file to account for fragments based on paired-end BAM. - Generates a filtered and normalized bed file to be used as input for SEACR peak calling. 8. Call enriched regions using SEACR. - This step uses both stringent and relaxed peak calling modes with a FDR (false discovery rate) of 0.01, and no normalization to a control sample. The output of SEACR is the [called peaks BED format file](https://github.com/FredHutch/SEACR#description-of-output-fields). 9. Generation and formatting of output files. - This step collects read, alignment, and peak statistics, as well asgenerates BigWig coverage/pileup files for display on the browser using IGV. The coverage shows the number of fragments that cover each base in the genome both normalized and unnormalized to the calculated spike-in scaling factor. ### __References__ - Meers MP, Tenenbaum D, Henikoff S. (2019). Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics and Chromatin 12(1):42. - Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.

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

Path: workflows/cutandrun-seacr-pe.cwl

Branch/Commit ID: d76110e0bfc40c874f82e37cef6451d74df4f908

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/germline_detect_variants.cwl

Branch/Commit ID: 22fce2dbdada0c4135b6f0677f78535cf980cb07

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/vcf_eval_concordance.cwl

Branch/Commit ID: 3042812447d9e8889c6118986490e9c9b9b13223

Single-cell Multiome ATAC and RNA-Seq Filtering Analysis Filters single-cell multiome ATAC and RNA-Seq datasets based on the common QC metrics.

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

Path: workflows/sc-multiome-filter.cwl

Branch/Commit ID: 7030da528559c7106d156284e50ff0ecedab0c4e

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 6a55118f915e24d2ad008c93a02d9de5643f5511

Motif Finding with HOMER with random background regions --------------------------------------------------- HOMER contains a novel motif discovery algorithm that was designed for regulatory element analysis in genomics applications (DNA only, no protein). It is a differential motif discovery algorithm, which means that it takes two sets of sequences and tries to identify the regulatory elements that are specifically enriched in on set relative to the other. It uses ZOOPS scoring (zero or one occurrence per sequence) coupled with the hypergeometric enrichment calculations (or binomial) to determine motif enrichment. HOMER also tries its best to account for sequenced bias in the dataset. It was designed with ChIP-Seq and promoter analysis in mind, but can be applied to pretty much any nucleic acids motif finding problem. Here is how we generate background for Motifs Analysis ------------------------------------- 1. Take input file with regions in a form of “chr\" “start\" “end\" 2. Sort and remove duplicates from this regions file 3. Extend each region in 20Kb into both directions 4. Merge all overlapped extended regions 5. Subtract not extended regions from the extended ones 6. Randomly distribute not extended regions within the regions that we got as a result of the previous step 7. Get fasta file from these randomly distributed regions (from the previous step). Use it as background For more information please refer to: ------------------------------------- [Official documentation](http://homer.ucsd.edu/homer/motif/)

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

Path: workflows/homer-motif-analysis.cwl

Branch/Commit ID: 5561f7ee11dd74848680351411a19aa87b13d27b

https://github.com/ncbi/pgap.git

Path: bacterial_noncoding/wf_bacterial_noncoding.cwl

Branch/Commit ID: 1e16653514fd5629a704516eb447043c9fd0a53b

CNV Manta calling

https://gitlab.bsc.es/lrodrig1/structuralvariants_poc.git

Path: structuralvariants/cwl/subworkflows/cnv_manta.cwl

Branch/Commit ID: 3f6a871f81f343cf81a345f73ff2eeac70804b8c