Determining cellular gene isoform abundance from long-read RNA sequencing data

Determining cellular gene isoform abundance from long-read RNA sequencing data

Advisor/s

A/Prof Ulf Schmitz

College or Research Centre

College of Public Health, Medical & Veterinary Science

Summary of Project

Advances in third-generation sequencing approaches (long-read RNA sequencing) have enabled the study of cellular transcriptomes in greater detail than ever before. Recent reports suggest that genes can express up to 30 different isoforms via alternative splicing.

In this project, we will employ algorithms for transcript isoform analysis (e.g. tappAS, FulQuant, and IRFinder-S) for a thorough analysis of human and mouse transcriptomes from different cell and tissue types. We will determine whether transcript isoform abundance can be associated with cell/tissues characteristics such as cell state, condition (healthy/diseased), or doubling time. Moreover, we will assess functions of genes with excessively large numbers of expressed isoforms.

Key Words

bioinformatics; long-read sequencing; alternative splicing; transcriptomics complexity; data analytics

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Updated: 25 Feb 2022