Name Mode Size
.github 040000
R 040000
data-raw 040000
data 040000
inst 040000
man 040000
pkgdown 040000
tests 040000
vignettes 040000
.BBSoptions 100644 0 kb
.Rbuildignore 100644 0 kb
.gitignore 100644 1 kb
DESCRIPTION 100644 3 kb
INSTALL 100644 1 kb
LICENSE.md 100644 34 kb
NAMESPACE 100644 2 kb
NEWS.md 100644 2 kb
README.Rmd 100644 12 kb
README.md 100644 14 kb
README.md
# <code>MotifPeeker</code><br>Benchmarking Epigenomic Profiling Methods Using Motif Enrichment <img src="https://github.com/neurogenomics/MotifPeeker/raw/master/inst/hex/hex.png" style="height: 300px !important;" /> [![License: GPL (\>= 3)](https://img.shields.io/badge/license-GPL%20(%3E=%203)-blue.svg)](https://cran.r-project.org/web/licenses/GPL%20(%3E=%203)) [![](https://img.shields.io/badge/devel%20version-0.99.12-black.svg)](https://github.com/neurogenomics/MotifPeeker) [![](https://img.shields.io/github/languages/code-size/neurogenomics/MotifPeeker.svg)](https://github.com/neurogenomics/MotifPeeker) [![](https://img.shields.io/github/last-commit/neurogenomics/MotifPeeker.svg)](https://github.com/neurogenomics/MotifPeeker/commits/master) <br> [![R build status](https://github.com/neurogenomics/MotifPeeker/workflows/rworkflows/badge.svg)](https://github.com/neurogenomics/MotifPeeker/actions) [![](https://codecov.io/gh/neurogenomics/MotifPeeker/branch/master/graph/badge.svg)](https://app.codecov.io/gh/neurogenomics/MotifPeeker) <br> <a href='https://app.codecov.io/gh/neurogenomics/MotifPeeker/tree/master' target='_blank'><img src='https://codecov.io/gh/neurogenomics/MotifPeeker/branch/master/graphs/icicle.svg' title='Codecov icicle graph' width='200' height='50' style='vertical-align: top;'></a> **Authors:** ***Hiranyamaya (Hiru) Dash, Thomas Roberts, Maria Weinert, Nathan Skene*** **Updated:** ***Dec-02-2024*** ## Introduction `MotifPeeker` is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: 1. **General Metrics**: Provides an overview of metrics related to dataset peaks, including FRiP scores, peak widths, and motif-to-summit distances. 2. **Known Motif Enrichment Analysis**: Presents statistics on the frequency of enriched user-supplied motifs in the datasets and compares them between the common and unique peaks from comparison and reference datasets. 3. **Discovered Motif Enrichment Analysis**: Details the statistics of motifs discovered in common and unique peaks from comparison and reference datasets. Examines motif similarities and identifies the closest known motifs in the JASPAR or the provided database. <!-- If you use `MotifPeeker`, please cite: --> <!-- > --> ## Installation `MotifPeeker` uses [`memes`](https://www.bioconductor.org/packages/release/bioc/html/memes.html) which relies on a local install of the [MEME suite](https://meme-suite.org/meme/), which can be installed as follows: ``` bash MEME_VERSION=5.5.5 # or the latest version wget https://meme-suite.org/meme/meme-software/$MEME_VERSION/meme-$MEME_VERSION.tar.gz tar zxf meme-$MEME_VERSION.tar.gz cd meme-$MEME_VERSION ./configure --prefix=$HOME/meme --with-url=http://meme-suite.org/ \ --enable-build-libxml2 --enable-build-libxslt make make install # Add to PATH echo 'export PATH=$HOME/meme/bin:$HOME/meme/libexec/meme-$MEME_VERSION:$PATH' >> ~/.bashrc echo 'export MEME_BIN=$HOME/meme/bin' >> ~/.bashrc source ~/.bashrc ``` **NOTE:** It is important that Perl dependencies associated with MEME suite are also installed, particularly `XML::Parser`, which can be installed using the following command in the terminal: ``` bash cpan install XML::Parser ``` For more information, refer to the [Perl dependency section of the MEME suite](https://meme-suite.org/meme/doc/install.html#prereq_perl). Once the MEME suite and its associated Perl dependencies are installed, the development version of `MotifPeeker` can be installed using the following code: ``` r # Install latest version of MotifPeeker BiocManager::install("MotifPeeker", version = "devel", dependencies = TRUE) # Load the package library(MotifPeeker) ``` Alternatively, you can use the [Docker/Singularity container](https://neurogenomics.github.io/MotifPeeker/articles/docker.html) to run the package out-of-the-box. ## Documentation #### [MotifPeeker Website](https://neurogenomics.github.io/MotifPeeker) #### [Get Started](https://neurogenomics.github.io/MotifPeeker/articles/MotifPeeker.html) #### [Docker/Singularity Container](https://neurogenomics.github.io/MotifPeeker/articles/docker.html) #### [Example Reports](https://neurogenomics.github.io/MotifPeeker/articles/examples.html) #### [Troubleshooting](https://neurogenomics.github.io/MotifPeeker/articles/troubleshooting.html) ## Usage Load the package and example datasets. ``` r library(MotifPeeker) data("CTCF_ChIP_peaks", package = "MotifPeeker") data("CTCF_TIP_peaks", package = "MotifPeeker") data("motif_MA1102.2", package = "MotifPeeker") data("motif_MA1930.2", package = "MotifPeeker") ``` Prepare input files. ``` r peak_files <- list(CTCF_ChIP_peaks, CTCF_TIP_peaks) alignment_files <- list( system.file("extdata", "CTCF_ChIP_alignment.bam", package = "MotifPeeker"), system.file("extdata", "CTCF_TIP_alignment.bam", package = "MotifPeeker") ) motif_files <- list(motif_MA1102.2, motif_MA1930.2) ``` Run `MotifPeeker()`: ``` r MotifPeeker( peak_files = peak_files, reference_index = 2, # Set TIP-seq experiment as reference alignment_files = alignment_files, exp_labels = c("ChIP", "TIP"), exp_type = c("chipseq", "tipseq"), genome_build = "hg38", motif_files = motif_files, cell_counts = NULL, # No cell-count information motif_discovery = TRUE, motif_discovery_count = 3, motif_db = NULL, download_buttons = TRUE, out_dir = tempdir(), workers = 2, debug = FALSE, quiet = FALSE, verbose = TRUE ) ``` ### Required Inputs These input parameters must be provided: <details> <summary> <strong>Details</strong> </summary> - `peak_files`: A list of path to peak files or `GRanges` objects with the peaks to analyse. Currently, only peak files from `MACS2/3` (`.narrowPeak`) and `SEACR` (`.bed`) are supported. ENCODE file IDs can also be provided to automatically fetch peak file(s) from the ENCODE database. - `reference_index`: An integer specifying the index of the reference dataset in the `peak_files` list to use as reference for various comparisons. (default = 1) - `genome_build`: A character string or a `BSgenome` object specifying the genome build of the datasets. At the moment, only hg38 and hg19 are supported as abbreviated input. - `out_dir`: A character string specifying the output directory to save the HTML report and other files. </details> ### Optional Inputs These input parameters optional, but *recommended* to add more analyses, or enhance them: <details> <summary> <strong>Details</strong> </summary> - `alignment_files`: A list of path to alignment files or `Rsamtools::BamFile` objects with the alignment sequences to analyse. Alignment files are used to calculate read-related metrics like FRiP score. ENCODE file IDs can also be provided to automatically fetch alignment file(s) from the ENCODE database. - `exp_labels`: A character vector of labels for each peak file. If not provided, capital letters will be used as labels in the report. - `exp_type`: A character vector of experimental types for each peak file. Useful for comparison of different methods. If not provided, all datasets will be classified as “unknown” experiment types in the report. `exp_type` is used only for labelling. It does not affect the analyses. You can also input custom strings. Datasets will be grouped as long as they match their respective `exp_type`. Supported experimental types are: - `chipseq`: ChIP-seq data - `tipseq`: TIP-seq data - `cuttag`: CUT&Tag data - `cutrun`: CUT&Run data - `motif_files`: A character vector of path to motif files, or a vector of `universalmotif-class` objects. Required to run Known Motif Enrichment Analysis. JASPAR matrix IDs can also be provided to automatically fetch motifs from the JASPAR. - `motif_labels`: A character vector of labels for each motif file. Only used if path to file names are passed in motif_files. If not provided, the motif file names will be used as labels. - `cell_counts`: An integer vector of experiment cell counts for each peak file (if available). Creates additional comparisons based on cell counts. - `motif_db`: Path to `.meme` format file to use as reference database, or a list of `universalmotif-class` objects. Results from motif discovery are searched against this database to find similar motifs. If not provided, JASPAR CORE database will be used, making this parameter **truly optional**. **NOTE**: p-value estimates are inaccurate when the database has fewer than 50 entries. </details> ### Other Options For more information on additional parameters, please refer to the documentation for [`MotifPeeker()`](https://neurogenomics.github.io/MotifPeeker/reference/MotifPeeker.html). ### Runtime Guidance For 4 datasets, the runtime is approximately 3 minutes with motif_discovery disabled. However, motif discovery can take hours to complete. To make computation faster, we highly recommend tuning the following arguments: <details> <summary> <strong>Details</strong> </summary> - `workers`: Running motif discovery in parallel can significantly reduce runtime, but it is very memory-intensive, consuming upwards of 10GB of RAM per thread. Memory starvation can greatly slow the process, so set `workers` with caution. - `motif_discovery_count`: The number of motifs to discover per sequence group exponentially increases runtime. We recommend no more than 5 motifs to make a meaningful inference. - `trim_seq_width`: Trimming sequences before running motif discovery can significantly reduce the search space. Sequence length can exponentially increase runtime. We recommend running the script with `motif_discovery = FALSE` and studying the motif-summit distance distribution under general metrics to find the sequence length that captures most motifs. A good starting point is 150 but it can be reduced further if appropriate. </details> ### Outputs `MotifPeeker` generates its output in a new folder within he `out_dir` directory. The folder is named `MotifPeeker_YYYYMMDD_HHMMSS` and contains the following files: - `MotifPeeker.html`: The main HTML report, including all analyses and plots. - Output from various MEME suite tools in their respecive sub-directories, if `save_runfiles` is set to `TRUE`. ## Datasets `MotifPeeker` comes with several datasets bundled: <details> <summary> <strong>Details</strong> </summary> - `CTCF_TIP_peaks`: Human CTCF peak file generated with TIP-seq using HCT116 cell-line. No control files were used to generate the peak file. The peaks were called using `MACS3` with `CTCF_TIP_alignment.bam` as input. - `CTCF_ChIP_peaks`: Human CTCF peak file generated with ChIP-seq using HCT116 cell-line. No control files were used to generate the peak file. The peaks were called using `MACS3` with `CTCF_ChIP_alignment.bam` as input. - `motif_MA1102.3`: The JASPAR motif for CTCFL (MA1102.3) for *Homo Sapiens*. Sourced from [JASPAR](https://jaspar.elixir.no/matrix/MA1102.3/) - `motif_MA1930.2`: The JASPAR motif for CTCFL (MA1930.2) for *Homo Sapiens*. Sourced from [JASPAR](https://jaspar.elixir.no/matrix/MA1930.2/) - `CTCF_TIP_alignment.bam`: Human CTCF alignment file generated with TIP-seq using HCT116 cell-line. The alignment file was generated using the [`nf-core/cutandrun`](https://nf-co.re/cutandrun/3.2.2) pipeline. Raw read files were sourced from *NIH Sequence Read Archives* [*ID: SRR16963166*](https://trace.ncbi.nlm.nih.gov/Traces/index.html?view=run_browser&acc=SRR16963166). Only available as *extdata*. - `CTCF_ChIP_alignment.bam`: Human CTCF alignment file generated with ChIP-seq using HCT116 cell-line. Sourced from [ENCODE (Accession: ENCFF091ODJ)](https://www.encodeproject.org/files/ENCFF091ODJ/). Only available as *extdata*. </details> Please note that the peaks and alignments included are a very small subset (*chr10:65,654,529-74,841,155*) of the actual data. It only serves as an example to demonstrate the package and run tests to maintain the integrity of the package. ## Licensing Restrictions MotifPeeker incorporates the MEME Suite, which is available free of charge for educational, research, and non-profit purposes. Users intending to use MotifPeeker for commercial purposes are required to purchase a license for the MEME Suite. For more details, please refer to the [MEME Suite Copyright Page](https://meme-suite.org/meme/doc/copyright.html). ## Contact ### [Neurogenomics Lab](https://www.neurogenomics.co.uk/inst/report/EpiCompare.html) UK Dementia Research Institute Department of Brain Sciences Faculty of Medicine Imperial College London [GitHub](https://github.com/neurogenomics) <hr> ### Session Info <details> ``` r utils::sessionInfo() ``` ## R version 4.4.2 (2024-10-31) ## Platform: aarch64-apple-darwin20 ## Running under: macOS Sequoia 15.1.1 ## ## Matrix products: default ## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib ## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0 ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## time zone: Europe/London ## tzcode source: internal ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## loaded via a namespace (and not attached): ## [1] gtable_0.3.6 jsonlite_1.8.9 renv_1.0.11 ## [4] dplyr_1.1.4 compiler_4.4.2 BiocManager_1.30.25 ## [7] tidyselect_1.2.1 rvcheck_0.2.1 scales_1.3.0 ## [10] yaml_2.3.10 fastmap_1.2.0 here_1.0.1 ## [13] ggplot2_3.5.1 R6_2.5.1 generics_0.1.3 ## [16] knitr_1.49 yulab.utils_0.1.8 tibble_3.2.1 ## [19] desc_1.4.3 dlstats_0.1.7 rprojroot_2.0.4 ## [22] munsell_0.5.1 pillar_1.9.0 RColorBrewer_1.1-3 ## [25] rlang_1.1.4 utf8_1.2.4 badger_0.2.4 ## [28] xfun_0.49 fs_1.6.5 cli_3.6.3 ## [31] magrittr_2.0.3 rworkflows_1.0.3 digest_0.6.37 ## [34] grid_4.4.2 rstudioapi_0.17.1 lifecycle_1.0.4 ## [37] vctrs_0.6.5 evaluate_1.0.1 glue_1.8.0 ## [40] data.table_1.16.2 fansi_1.0.6 colorspace_2.1-1 ## [43] rmarkdown_2.29 tools_4.4.2 pkgconfig_2.0.3 ## [46] htmltools_0.5.8.1 </details>