skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices
Tipo de recurso Mostra resultados com: Mostra resultados com: Índice

CharPlant: A De Novo Open Chromatin Region Prediction Tool for Plant Genomes

Shen, Yin ; Chen, Ling-Ling ; Gao, Junxiang

Genomics, proteomics & bioinformatics, 2021-10, Vol.19 (5), p.860-871 [Periódico revisado por pares]

China: Elsevier B.V

Texto completo disponível

Citações Citado por
  • Título:
    CharPlant: A De Novo Open Chromatin Region Prediction Tool for Plant Genomes
  • Autor: Shen, Yin ; Chen, Ling-Ling ; Gao, Junxiang
  • Assuntos: Application Note ; Chromatin - genetics ; Chromatin accessibility ; Convolutional neural network ; De novo prediction ; Deoxyribonucleases - genetics ; DNA ; Genome, Plant ; High-Throughput Nucleotide Sequencing - methods ; Open chromatin region ; Plant genome ; Sequence Analysis, DNA - methods
  • É parte de: Genomics, proteomics & bioinformatics, 2021-10, Vol.19 (5), p.860-871
  • Descrição: 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. Moreover, 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 chromatin 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 convolutional 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 regulatory 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.
  • Editor: China: Elsevier B.V
  • Idioma: Inglês
Links
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.