# <code>MotifPeeker</code><br>Benchmarking Epigenomic Profiling Methods Using Motif Enrichment
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**Authors:** ***Hiranyamaya (Hiru) Dash, Thomas Roberts, Nathan
Skene***
**Updated:** ***Nov-12-2024***
## 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.
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## 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
if(!require("remotes")) install.packages("remotes")
remotes::install_github("neurogenomics/MotifPeeker")
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 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.1
##
## 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_4.4.1 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_1.48 yulab.utils_0.1.7 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_1.0.2 digest_0.6.37
## [34] grid_4.4.1 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_4.4.1 pkgconfig_2.0.3
## [46] htmltools_0.5.8.1
</details>
