Predmoter—cross-species prediction of plant promoter and enhancer regions

Original Publication

Felicitas Kindel, Sebastian Triesch, Urte Schlüter, Laura Alexandra Randarevitch, Vanessa Reichel-Deland, Andreas P M Weber, Alisandra K Denton, Predmoter—cross-species prediction of plant promoter and enhancer regions, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae074, https://doi.org/10.1093/bioadv/vbae074

Abstract

Motivation

Identifying cis-regulatory elements (CREs) is crucial for analyzing gene regulatory networks. Next generation sequencing methods were developed to identify CREs but represent a considerable expenditure for targeted analysis of few genomic loci. Thus, predicting the outputs of these methods would significantly cut costs and time investment.

Results

We present Predmoter, a deep neural network that predicts base-wise Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We trained our final model on 21 species for 13 of which ATAC-seq data and for 17 of which ChIP-seq data was publicly available. We evaluated our models on Arabidopsis thaliana and Oryza sativa. Our best models showed accurate predictions in peak position and pattern for ATAC- and histone ChIP-seq. Annotating putatively accessible chromatin regions provides valuable input for the identification of CREs. In conjunction with other in silico data, this can significantly reduce the search space for experimentally verifiable DNA–protein interaction pairs.

Availability and implementation

The source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter. Predmoter takes a fasta file as input and outputs h5, and optionally bigWig and bedGraph files.

License

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Data availability

The datasets in this article were derived from sources in the public domain as listed in section S1.1, Tables S2 and S3.