A fast and globally optimal solution for RNA-seq quantification

Yi, Huiguang1,2; Lin, Yanling; Chang, Qing1; Jin, Wenfei2

2023-09-20

10.1093/bib/bbad298

BRIEFINGS IN BIOINFORMATICS   Impact Factor & Quartile

1467-5463

1477-4054

Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation A(T)Ax = A(T)b. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at .

alignment-free RNA-seq quantification globally optimal

SCI

English

Corresponding

Funds for Shenzhen Basic Research Institutions[JCKY2020-44] ; National Key Ramp;D Program of China["2021YFF1200900","2021YFA0909300"] ; National Natural Science Foundation of China["32170646","81872330"] ; Shenzhen Science and Technology Program[KQTD20180411143432337] ; Shenzhen Innovation Committee of Science and Technology[ZDSYS20200811144002008]

Biochemistry & Molecular Biology ; Mathematical & Computational Biology

Biochemical Research Methods ; Mathematical & Computational Biology

WOS:001050942100001

OXFORD UNIV PRESS

COMPUTER SCIENCE

Web of Science

1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Beijing, Peoples R China
2.Southern Univ Sci & Technol, Sch Life Sci, Shenzhen, Peoples R China

Yi, Huiguang,Lin, Yanling,Chang, Qing,et al. A fast and globally optimal solution for RNA-seq quantification[J]. BRIEFINGS IN BIOINFORMATICS,2023,24(5).

Yi, Huiguang,Lin, Yanling,Chang, Qing,&Jin, Wenfei.(2023).A fast and globally optimal solution for RNA-seq quantification.BRIEFINGS IN BIOINFORMATICS,24(5).

Yi, Huiguang,et al."A fast and globally optimal solution for RNA-seq quantification".BRIEFINGS IN BIOINFORMATICS 24.5(2023).