CharPlant:A De Novo Open Chromatin Region Prediction Tool for Plant Genomes
- Author:
Shen YIN
1
;
Chen LING-LING
;
Gao JUNXIANG
Author Information
1. Hubei Key Laboratory of Agricultural Bioinformatics,College of Informatics,Huazhong Agricultural University,Wuhan 430070,China
- Keywords:
Open chromatin region;
Chromatin accessibility;
Convolutional neural net-work;
De novo prediction;
Plant genome
- From:
Genomics, Proteomics & Bioinformatics
2021;19(5):860-871
- CountryChina
- Language:Chinese
-
Abstract:
Chromatin accessibility is a highly informative structural feature for understanding gene transcription regulation,because it indicates the degree to which nuclear macromolecules such as proteins and RNAs can access chromosomal DNA.Studies have shown that chromatin accessibility is highly dynamic during stress response,stimulus response,and developmental transition.More-over,physical access to chromosomal DNA in eukaryotes is highly cell-specific.Therefore,current technologies such as DNase-seq,ATAC-seq,and FAIRE-seq reveal only a portion of the open chro-matin regions(OCRs)present in a given species.Thus,the genome-wide distribution of OCRs remains unknown.In this study,we developed a bioinformatics tool called CharPlant for the de novo prediction of OCRs in plant genomes.To develop this tool,we constructed a three-layer con-volutional neural network(CNN)and subsequently trained the CNN using DNase-seq and ATAC-seq datasets of four plant species.The model simultaneously learns the sequence motifs and regu-latory logics,which are jointly used to determine DNA accessibility.All of these steps are integrated into CharPlant,which can be run using a simple command line.The results of data analysis using CharPlant in this study demonstrate its prediction power and computational efficiency.To our knowledge,CharPlant is the first de novo prediction tool that can identify potential OCRs in the whole genome.The source code of CharPlant and supporting files are freely available from https://github.com/Yin-Shen/CharPlant.
