https://github.com/neurogenomics/motifpeeker
Benchmark Epigenomic Profiling Methods with Motif Enrichment as Key Metric
https://github.com/neurogenomics/motifpeeker
bioconductor bioconductor-package chip-seq epigenomics interactive-report motif-enrichment-analysis r-package
Last synced: 2 months ago
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Benchmark Epigenomic Profiling Methods with Motif Enrichment as Key Metric
- Host: GitHub
- URL: https://github.com/neurogenomics/motifpeeker
- Owner: neurogenomics
- License: gpl-3.0
- Created: 2024-06-18T10:23:30.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2025-01-06T11:32:17.000Z (5 months ago)
- Last Synced: 2025-02-08T17:17:00.763Z (4 months ago)
- Topics: bioconductor, bioconductor-package, chip-seq, epigenomics, interactive-report, motif-enrichment-analysis, r-package
- Language: R
- Homepage: https://neurogenomics.github.io/MotifPeeker/
- Size: 6.85 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
Awesome Lists containing this project
README
---
output:
github_document
---
```{r, echo = FALSE, include = FALSE}
pkg <- read.dcf("DESCRIPTION", fields = "Package")[1]
title <- gsub("\n"," ",read.dcf("DESCRIPTION", fields = "Title")[1])
description <- gsub("\n"," ",read.dcf("DESCRIPTION", fields = "Description")[1])
URL <- read.dcf('DESCRIPTION', fields = 'URL')[1]
owner <- strsplit(URL,"/")[[1]][4]
repo <- strsplit(URL,"/")[[1]][5]
```
# MotifPeeker
Benchmarking Epigenomic Profiling Methods Using Motif Enrichment
{style='height: 300px !important;'}
[](https://bioconductor.org/packages/devel/bioc/html/MotifPeeker.html#since)
`r rworkflows::use_badges(add_hex = FALSE, add_authors = FALSE)`
**Authors:** ***Hiranyamaya (Hiru) Dash, Thomas Roberts, Maria Weinert, Nathan Skene***
**Updated:** ***`r format(Sys.Date(), '%b-%d-%Y')`***
## 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.
## 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, eval = FALSE}
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, eval = FALSE}
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, eval = FALSE}
# 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://`r owner`.github.io/`r repo`)
#### [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, eval = FALSE}
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, eval = FALSE}
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, eval = FALSE}
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
- `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.
### Optional Inputs
These input parameters optional, but *recommended* to add more analyses, or
enhance them:
Details
- `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.
### 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
- `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.
### 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
- `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*.
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)
UK Dementia Research Institute
Department of Brain Sciences
Faculty of Medicine
Imperial College London
[GitHub](https://github.com/neurogenomics)
### Session Info
```{r}
utils::sessionInfo()
```