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

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-05-24 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae074
Felicitas Kindel, Sebastian Triesch, Urte Schlüter, Laura Alexandra Randarevitch, Vanessa Reichel-Deland, Andreas P M Weber, Alisandra K Denton
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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.

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植物启动子和增强子区域的跨物种预测。
动机识别顺式调控元件(CRE)对于分析基因调控网络至关重要。下一代测序方法是为识别 CREs 而开发的,但要对少数基因组位点进行有针对性的分析,需要相当大的花费。因此,预测这些方法的结果将大大减少成本和时间投入:我们介绍了一种深度神经网络--Predmoter,它能预测植物基因组的转座酶可访问染色质测序(ATAC-seq)和组蛋白染色质免疫沉淀DNA测序(ChIP-seq)读数覆盖率。Predmoter 仅使用 DNA 序列作为输入。我们在 21 个物种上训练了最终模型,其中 13 个物种的 ATAC-seq 数据和 17 个物种的 ChIP-seq 数据已经公开。我们在拟南芥和黑麦草上评估了我们的模型。我们的最佳模型能准确预测 ATAC 和组蛋白 ChIP-seq 的峰位置和模式。标注推测可访问的染色质区域为识别 CREs 提供了有价值的信息。结合其他硅学数据,这可以大大缩小可通过实验验证的 DNA 蛋白相互作用对的搜索空间:Predmoter 的源代码可在以下网址获取:https://github.com/weberlab-hhu/Predmoter。Predmoter 将 fasta 文件作为输入,并输出 h5 文件以及可选的 bigWig 和 bedGraph 文件。
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