DeepSRE: Identification of sterol responsive elements and nuclear transcription factors Y proximity in human DNA by Convolutional Neural Network analysis

Autor: Rossella Spina, Maurizio Averna, Francesca Fayer, Angelo B. Cefalù, Davide Noto, Antonina Giammanco
Přispěvatelé: Noto D., Giammanco A., Spina R., Fayer F., Cefalu A.B., Averna M.
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Metabolic Processes
Settore MED/09 - Medicina Interna
Conservation Biology
Gene Expression
Biochemistry
Conservation Science
Data Management
Regulation of gene expression
Multidisciplinary
Gene Ontologies
Genomics
Lipids
Phylogenetics
Cholesterol
Conservation Genetics
Medicine
Settore MED/46 - Scienze Tecniche Di Medicina Di Laboratorio
Research Article
Computer and Information Sciences
Sp1 Transcription Factor
Sequence analysis
Science
DNA transcription
Computational biology
Biology
Data mining
Deep Learning
Genetics
Transcription factor
DNA-binding proteins
Genetics
Humans
Gene Regulation
Evolutionary Systematics
Binding site
Gene
Transcription factor
Taxonomy
Evolutionary Biology
Models
Genetic
Ecology and Environmental Sciences
Biology and Life Sciences
Computational Biology
Proteins
Promoter
DNA Patterns
DNA
Sequence Analysis
DNA
Genome Analysis
Regulatory Proteins
Sterol regulatory element-binding protein
Metabolism
Serum Response Element
CCAAT-Binding Factor
Transcription Factors
Zdroj: PLoS ONE, Vol 16, Iss 3, p e0247402 (2021)
PLoS ONE
Popis: SREBP1 and 2, are cholesterol sensors able to modulate cholesterol-related gene expression responses. SREBPs binding sites are characterized by the presence of multiple target sequences as SRE, NFY and SP1, that can be arranged differently in different genes, so that it is not easy to identify the binding site on the basis of direct DNA sequence analysis. This paper presents a complete workflow based on a one-dimensional Convolutional Neural Network (CNN) model able to detect putative SREBPs binding sites irrespective of target elements arrangements. The strategy is based on the recognition of SRE linked (less than 250 bp) to NFY sequences according to chromosomal localization derived from TF Immunoprecipitation (TF ChIP) experiments. The CNN is trained with several 100 bp sequences containing both SRE and NF-Y. Once trained, the model is used to predict the presence of SRE-NFY in the first 500 bp of all the known gene promoters. Finally, genes are grouped according to biological process and the processes enriched in genes containing SRE-NFY in their promoters are analyzed in details. This workflow allowed to identify biological processes enriched in SRE containing genes not directly linked to cholesterol metabolism and possible novel DNA patterns able to fill in for missing classical SRE sequences.
Databáze: OpenAIRE
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