Chris1221/lanceotron

Command-line interface to the LanceOTron deep learning peak caller

LanceOTron CLI

A bare-bones interface to the trained LanceOTron (LoT) model from the command line.

Installation

pip install lanceotron

Local installation

1. Clone the repository.
2. Install dependencies with pip.
3. Install the package.
4. Run tests to ensure that everything is working.

git clone git@github.com:Chris1221/lanceotron.git; cd lanceotron # Step 1
pip install -r requirements.txt # Step 2
pip install -e . # Step 3
python -m unittest

Usage

To see available commands, use the --help flag.

lanceotron --help

Call Peaks

To call peaks from a bigWig track, use the callPeaks command.

Option	Description	Default
file	BigWig Track to analyse
-t, --threshold	Threshold for selecting candidate peaks	4
-w, --window	Window size for rolling mean to select candidate peaks	400
-f, --folder	Output folder	"./"
--skipheader	Skip writing the header	False

Call Peaks with Input

To call peaks from a bigWig track with an input file, use the callPeaks_Input command.

Option	Description	Default
file	BigWig track to analyse
-i, --input	Control input track to calculate significance of peaks
-t, --threshold	Threshold for selecting candidate peaks	4
-w, --window	Window size for rolling mean to select candidate peaks	400
-f, --folder	Output folder	"./"
--skipheader	Skip writing the header	False

Score a Bed file

To score the peaks in an existing Bed file, use the scoreBed command.

Option	Description	Default
file	BigWig Track to analyse
-b, --bed	Bed file of regions to be scored
-f, --folder	Output folder	"./"
--skipheader	Skip writing the header	False

Examples

There is a basic bigWig file included in the test subdirectory. To try out the caller, execute it on this file.

lanceotron callPeaks test/chr22.bw -f output_folder

Citation

@article {Hentges2021.01.25.428108,
	author = {Hentges, Lance D. and Sergeant, Martin J. and Downes, Damien J. and Hughes, Jim R. and Taylor, Stephen},
	title = {LanceOtron: a deep learning peak caller for ATAC-seq, ChIP-seq, and DNase-seq},
	year = {2021},
	doi = {10.1101/2021.01.25.428108},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/01/27/2021.01.25.428108},
	journal = {bioRxiv}
}

Building the documentation

To serve the documentation locally, use

python -m mkdocs serve

Bug Reports and Improvement Suggestions

Please raise an issue if there is anything you wish to ask or contribute.