Evaluation of different methods for normalization of RNA-Seq data for the reconstruction of co-expression networks in Setaria viridis

• Grant number: 23/01665-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2023
• End date: December 31, 2023
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal Investigator: Renato Augusto Corrêa dos Santos
• Grantee: Bianca Santos Pastos
• Host Institution: Centro de Energia Nuclear na Agricultura (CENA). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Systems Biology makes it possible to take the complexity emerging from the interaction of different network elements into account. The increasing availability of public RNA sequencing (RNA-Seq) datasets in databases allows researchers to reconstruct co-expression networks, a first step to raise hypotheses on the regulation of gene expression. In Agriculture, discoveries based on co-expression networks can significantly help molecular breeding, in the development of plants that are more efficient for production of food and biofuels. However, one of the challenges associated with the reconstruction of such networks is the choice of methods for quantification and normalization of gene expression from raw RNA-Seq short reads. An appropriate approach to evaluate the accuracy of these methods considers functional relationships between network elements. Setaria viridis is a model of C4 grasses with several public RNA-Seq datasets available. Our research group has been developing work in Bioenergy and employing systems approaches to raise transcription factors that could regulate gene expression in plants of economic importance, including the development of a platform for comparative transcriptomic analysis, CoNekT Bioenergy. The main objectives of this project include (i) the reconstruction of co-expression networks using different normalization methods, (ii) the evaluation of such methods based on the co-annotation of Gene Ontology (GO) terms in produced networks, and (iii) the inclusion of the networks and associated attributes (e.g. accuracy metrics) in CoNekT Bioenergy.