Statistical estimates of multiple transcription factors binding in the model plant genomes based on ChIP-seq data

Abstract

The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail.

Plant Biology and Biotechnology: Focus on Genomics and Bioinformatics

“…Through the meta-analysis of five transcriptomic experiments the authors constructed a set of differentially expressed genes and transcriptional regulators. This work complements recent systems biology studies of metabolism in Arabidopsis [ 14 , 20 ].…”

“…It refers to the topics on computational genomics in model plants for biotechnology, discussing genomics and bioinformatics approaches [ 13 ]. High-throughput technologies make it possible to model protein–protein and gene regulatory interactions in plant cells, providing a basis for better crop production and sustainability [ 14 , 15 , 16 ]. Plant–pathogen interaction studies complement network modeling in this area [ 5 , 17 ].…”

Analysis of multiple transcription factors binding in the model plant genomes

“…It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. Methods and Algorithms: We used experimental ChIP-seq data to model TF binding as the alternative to sequence-based prediction [2,4]. This study presents application of the analysis of multiple TF binding profiles in evolutionarily distant model plant organisms.…”

Digital medicine education problems and online solutions

“…The situation with the frequent transition to distance learning in Russia from 2020 required the development of new methods of teaching, with the possibility for students to use Internet resources and do independent work without access to a stationary computer class, in remote mode. The authors proposed a training course on basics bioinformatics, the use of elements of which began at Novosibirsk State University in the early 2000s (http://lcg.nsu.ru/ru/home-2/), then was adapted for students at Sechenov University in Moscow (https://student.sechenov.ru/), and later, since 2021, at the Agrarian and Technological Institute of PFUR (https://www.rudn.ru/education/schoolsand-departments/institutions/agricultural-technology-institute) [5]. Results: The bioinformatics course was read for students at Sechenov University in Moscow; it was presented online only on the university's internal website (https://student.sechenov.ru/).…”

“…Course content includes the fundamentals of bioinformatics databases, online tools, and web services for medical consultations. Successful examples of published students work are presented [5,6]. Conclusion: Overall, the goal of the project is to teach digital specialties, bioinformatics in medicine using online and hybrid approaches.…”