Predmoter-cross-species Prediction of Plant Promoter and Enhancer Regions

Overview

Journal Bioinform Adv

Publisher Oxford University Press

Specialty Biology

Date 2024 Jun 6

PMID 38841126

Authors

Felicitas Kindel

Sebastian Triesch

Urte Schluter

Laura Alexandra Randarevitch

Vanessa Reichel-Deland

Andreas P M Weber

Alisandra K Denton

Affiliations

Soon will be listed here.

Abstract

Motivation: Identifying -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 and . 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 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.

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