Analysing the Protein-DNA Binding Sites in Arabidopsis thaliana from ChIP-seq Experiments

Ginés Almagro-Hernández, Juana-Maria Vivo, Manuel Franco and Jesualdo Tomás Fernández-Breis
Additional contact information
Ginés Almagro-Hernández: Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, 30100 Murcia, Spain
Manuel Franco: Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), 30120 Murcia, Spain
Jesualdo Tomás Fernández-Breis: Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, 30100 Murcia, Spain

Mathematics, 2021, vol. 9, issue 24, 1-26

Abstract: Computational genomics aim at supporting the discovery of how the functionality of the genome of the organism under study is affected both by its own sequence and structure, and by the network of interaction between this genome and different biological or physical factors. In this work, we focus on the analysis of ChIP-seq data, for which many methods have been proposed in the recent years. However, to the best of our knowledge, those methods lack an appropriate mathematical formalism. We have developed a method based on multivariate models for the analysis of the set of peaks obtained from a ChIP-seq experiment. This method can be used to characterize an individual experiment and to compare different experiments regardless of where and when they were conducted. The method is based on a multivariate hypergeometric distribution, which fits the complexity of the biological data and is better suited to deal with the uncertainty generated in this type of experiments than the dichotomous models used by the state of the art methods. We have validated this method with Arabidopsis thaliana datasets obtained from the Remap2020 database, obtaining results in accordance with the original study of these samples. Our work shows a novel way for analyzing ChIP-seq data.

Keywords: bioinformatics; computational genomics; ChIP-seq experiment; protein binding functional regions; multivariate hypergeometric distribution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/24/3239/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/24/3239/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:24:p:3239-:d:702479

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().