| Title | Generate gene expression profile from high-throughput sequencing data |
| Authors | Liu, Hui Jiang, Zhichao Fang, Xiangzhong Fu, Hanjiang Zheng, Xiaofei Cha, Lei Li, Wuju |
| Affiliation | Peking Univ, LMAM, Ctr Stat, Sch Math Sci, Beijing 100871, Peoples R China. Beijing Inst Radiat Med, Beijing 100850, Peoples R China. Beijing Inst Basic Med Sci, Ctr Computat Biol, Beijing 100850, Peoples R China. |
| Keywords | Next-generation sequencing multiple mapping Gibbs sampler least-square Bayesian RNA-SEQ DISCOVERY |
| Issue Date | 2011 |
| Publisher | frontiers of mathematics in china |
| Citation | FRONTIERS OF MATHEMATICS IN CHINA.2011,6,(6),1131-1145. |
| 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. |
| URI | http://hdl.handle.net/20.500.11897/314427 |
| ISSN | 1673-3452 |
| DOI | 10.1007/s11464-011-0123-z |
| Indexed | SCI(E) 中国科学引文数据库(CSCD) |
| Appears in Collections: | 数学科学学院 数学及其应用教育部重点实验室 |
