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Frontiers of Mathematics in China

, Volume 6, Issue 6, pp 1131–1145 | Cite as

Generate gene expression profile from high-throughput sequencing data

  • Hui Liu
  • Zhichao Jiang
  • Xiangzhong FangEmail author
  • Hanjiang Fu
  • Xiaofei Zheng
  • Lei Cha
  • Wuju LiEmail author
Research Article
  • 69 Downloads

Abstract

This work presents two methods, the Least-square and Bayesian method, to solve the multiple mapping problem in extracting gene expression profiles through the next-generation sequencing. We parallel the tag sequences to genome, and partition them to improving the methods’ efficiency. The essential feature of these methods is that they can solve the multiple mapping problem between genes and short-reads, while generating almost the same estimation in single-mapping situation as the traditional approaches. These two methods are compared by simulation and a real example, which was generated from radiation-induced lung cancer cells (A549), through mapping short-reads to human ncRNA database. The results show that the Bayesian method, as realized by Gibbs sampler, is more efficient and robust than the Least-square method.

Keywords

Next-generation sequencing multiple mapping Gibbs sampler least-square Bayesian 

MSC

62F15 62J05 62P10 

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School of Mathematical Sciences, Statistical Center, LMAMPeking UniversityBeijingChina
  2. 2.Beijing Institute of Radiation MedicineBeijingChina
  3. 3.Center of Computational BiologyBeijing Institute of Basic Medical SciencesBeijingChina

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