您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

A fast and globally optimal solution for RNA-seq quantification

文献类型： 外文期刊

作者： Yi, Huiguang 1 ; Lin, Yanling 1 ; Chang, Qing 1 ; Jin, Wenfei 2 ;

作者机构： 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

关键词： alignment-free; RNA-seq quantification; globally optimal

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：9.5； 五年影响因子：10.6 ）

ISSN： 1467-5463

年卷期： 2023 年

页码：

收录情况： SCI

摘要： 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 .

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台