LanceOTron CLI
A bare-bones interface to the trained LanceOTron (LoT) model from the command line.
Installation
pip install lanceotron
Local installation
- Clone the repository.
- Install dependencies with pip.
- Install the package.
- 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.
Metadata
Release files for lanceotron 1.2.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lanceotron-1.2.7.tar.gz | 1.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lanceotron-1.2.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.2 MB
Release files / lanceotron-1.2.7.tar.gz
| Download URL | lanceotron-1.2.7.tar.gz |
|---|---|
| Size | 1.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.18
|
Release files / lanceotron-1.2.7-py3-none-any.whl
| Download URL | lanceotron-1.2.7-py3-none-any.whl |
|---|---|
| Size | 1.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
8e1a8086a4e384b98559a483ad49ddf1cfab94d8fe7ab70b506dd5d07e3dc389
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.18
|