Alignment-free and targeted assembly methods for gene fusions detection from RNA-seq data

• Grant number: 23/05887-5
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2023
• End date: February 28, 2027
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: João Meidanis
• Grantee: Lucas Peres Oliveira
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Gene fusion detection is essential for diagnosis, prognosis and treatment of cancer.Traditional methods for detecting gene fusions involve mapping short reads to a reference genome or transcriptome.However, these methods often yield a high number of false positives and require extensive filtering steps.Enhancing these methods with a targeted assembly subroutine mitigates this problem, but there is no best strategy for selecting reads for assembly.Alignment-free methods are an alternative and faster approach than mapping-based ones, making them attractive for a clinical setting, but their application for fusion detection remains scarce.Therefore, we will investigate the applicability of alignment-free methods for fusion detection as well as strategies for selecting reads for assembly.For the first, we will evaluate top-performing tools and identify how alignment-free algorithms can be improved for fusion detection.For the latter, we will assess structural variant calling techniques that are better suited for cancer data.We expect to optimize current tools and, potentially, develop novel algorithms for fusion detection.The resulting tools will be applied to real and simulated cancer data in order to assess accuracy and scalability. (AU)