Wu, Siyuan, and Schmitz, Ulf (2023) Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination. Computational and Structural Biotechnology Journal, 21. pp. 2373-2380.

Preview

PDF (Published Version) - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Single-cell sequencing technologies have revolutionised the life sciences and biomedical research. Single-cell sequencing provides high-resolution data on cell heterogeneity, allowing high-fidelity cell type identification, and lineage tracking. Computational algorithms and mathematical models have been developed to make sense of the data, compensate for errors and simulate the biological processes, which has led to breakthroughs in our understanding of cell differentiation, cell-fate determination and tissue cell composition. The development of long-read (a.k.a. third-generation) sequencing technologies has produced powerful tools for investigating alternative splicing, isoform expression (at the RNA level), genome assembly and the detection of complex structural variants (at the DNA level).

In this review, we provide an overview of the recent advancements in single-cell and long-read sequencing technologies, with a particular focus on the computational algorithms that help in correcting, analysing, and interpreting the resulting data. Additionally, we review some mathematical models that use single-cell and long-read sequencing data to study cell-fate determination and alternative splicing, respectively. Moreover, we highlight the emerging opportunities in modelling cell-fate determination that result from the combination of single-cell and long-read sequencing technologies.

Item ID:	78005
Item Type:	Article (Research - C1)
ISSN:	2001-0370
Keywords:	Mathematical modelling; RNA velocity; Alternative splicing; Isoform expression; Pesudotemopral trajectory inference; Transcriptome diversity
Copyright Information:	© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Funders:	National Health and Medical Research Council (NHMRC), Cancer Council NSW, Tropical Australian Academic Health Centre (TAAHC)
Projects and Grants:	NHMRC #1196405
Date Deposited:	11 Apr 2023 01:47
FoR Codes:	31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310299 Bioinformatics and computational biology not elsewhere classified @ 30% 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics @ 40% 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310114 Systems biology @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences @ 60% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280118 Expanding knowledge in the mathematical sciences @ 40%
Downloads:	Total: 80 Last 12 Months: 13
	More Statistics