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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 MACS2 which calls enriched regions in the target sequence data by identifying the top regions by area under a poisson distribution (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_below_1000` retains fragments <1000 bp - `histones_130_to_300` retains fragments between 130-300 bp - `TF_below_130` 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 peak-called BED files (also includes \"narrow\" and \"broad\" peaks). - 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 peak calling. 8. Call enriched regions using MACS2. - This step called peaks (broad and narrow) using the MACS2 tool with default parameters and no normalization to a control sample. 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-macs2-pe.cwl

Branch/Commit ID: 46d3d403ddb240d5a8f4f31ab992b6d6a2686745

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

Path: definitions/pipelines/somatic_wgs.cwl

Branch/Commit ID: d3e4bf55753cd92f97537c7d701187ea92d1e5f0

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

Path: definitions/pipelines/detect_variants_wgs_nonhuman.cwl

Branch/Commit ID: ef7f3345b352319ec22dffba26c79df033b141f9

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

Path: definitions/subworkflows/cram_to_cnvkit.cwl

Branch/Commit ID: e59c77629936fad069007ba642cad49fef7ad29f

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/conflict-wf.cwl

Branch/Commit ID: 2256a30d0c1365b30e0a7338fb883c74674fcd25

Packed ID: collision

What is MAnorm? -------------- MAnorm is a robust model for quantitative comparison of ChIP-Seq data sets of TFs (transcription factors) or epigenetic modifications and you can use it for: * Normalization of two ChIP-seq samples * Quantitative comparison (differential analysis) of two ChIP-seq samples * Evaluating the overlap enrichment of the protein binding sites(peaks) * Elucidating underlying mechanisms of cell-type specific gene regulation How MAnorm works? ---------------- MAnorm uses common peaks of two samples as a reference to build the rescaling model for normalization, which is based on the empirical assumption that if a chromatin-associated protein has a substantial number of peaks shared in two conditions, the binding at these common regions will tend to be determined by similar mechanisms, and thus should exhibit similar global binding intensities across samples. The observed differences on common peaks are presumed to reflect the scaling relationship of ChIP-Seq signals between two samples, which can be applied to all peaks. What do the inputs mean? ---------------- ### General **Experiment short name/Alias** * short name for you experiment to identify among the others **ChIP-Seq PE sample 1** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 1 **ChIP-Seq PE sample 2** * previously analyzed ChIP-Seq paired-end experiment to be used as Sample 2 **Genome** * Reference genome to be used for gene assigning ### Advanced **Reads shift size for sample 1** * This value is used to shift reads towards 3' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **Reads shift size for sample 2** * This value is used to shift reads towards 5' direction to determine the precise binding site. Set as half of the fragment length. Default 100 **M-value (log2-ratio) cutoff** * Absolute M-value (log2-ratio) cutoff to define biased (differential binding) peaks. Default: 1.0 **P-value cutoff** * P-value cutoff to define biased peaks. Default: 0.01 **Window size** * Window size to count reads and calculate read densities. 2000 is recommended for sharp histone marks like H3K4me3 and H3K27ac, and 1000 for TFs or DNase-seq. Default: 2000

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

Path: workflows/manorm-pe.cwl

Branch/Commit ID: 46d3d403ddb240d5a8f4f31ab992b6d6a2686745

https://github.com/common-workflow-language/cwl-v1.2.git

Path: tests/conditionals/cond-wf-007.cwl

Branch/Commit ID: e62f99dd79d6cb9c157cceb458f74200da84f6e9

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

Path: bacterial_annot/wf_bacterial_annot_pass2.cwl

Branch/Commit ID: 8ea3637b0f11eac1ea5599c41d74e00d85fb778d

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

Path: definitions/pipelines/somatic_exome_cle_gathered.cwl

Branch/Commit ID: 3f3b186da9bf82a5e2ae74ba27aef35a46174ebe

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

Path: bacterial_annot/wf_bacterial_annot_pass4.cwl

Branch/Commit ID: a3affd1b9e3e16f0644a25fee1a7b87b99df57b0