Evaluation of alignment methods for next-generation sequencing data and their effects on differential gene expression analysis in sorghum

• Grant number: 15/26990-2
• Support type: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): April 01, 2016
• Effective date (End): December 31, 2016
• Field of knowledge: Agronomical Sciences - Agronomy - Crop Science
• Principal researcher: Gabriel Rodrigues Alves Margarido
• Grantee: Felipe Estevam Corrêa
• Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Sorghum is the fifth most economically important cereal in the world, with great potential to complement ethanol production in Brazil, especially during the sugarcane off season. Recently, efforts towards the genetic breeding of sorghum are gaining prominence. In this context, with the study of functional genomics it is possible to determine how differential gene expression in varying conditions can affect phenotypic traits, such as yield and stress resistance, in order to assist in breeding of this species. For such studies, it is necessary to use one of the several available biological sequence alignment tools. However, little is known about the advantages and disadvantages of each aligner. For this reason, this project aims to study the effect of different RNA sequence aligners in the alignment results and in the differential expression analysis. To achieve this, we will align RNA-seq datasets against the reference genome using the following aligners: BWA-mem, Bowtie2, SOAPsplice, MAQ, STAR and SNAP. After the alignment step, we will generate a transcript count table with which we will perform the differential expression analysis. By comparing the results obtained with the different tools, in terms of alignment accuracy, computational efficiency and transcript expression quantification, it will be possible to determine the effects of these aligners on the differential expression analysis. The results can be used to assist future decision making efforts about the most adequate aligners for various study conditions.