Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data
文献类型：期刊论文
| 作者 | Wan, Lin3; Kang, Xin2; Ren, Jie1; Sun, Fengzhu1 |
| 刊名 | QUANTITATIVE BIOLOGY
 |
| 出版日期 | 2020 |
| 卷号 | 8期号:2页码:143-154 |
| 关键词 | DNA FREQUENCIES PREDICTION Markov chains next generation sequencing transition probabilities confidence intervals |
| ISSN号 | 2095-4689 |
| 英文摘要 | Background Markov chains (MC) have been widely used to model molecular sequences. The estimations of MC transition matrix and confidence intervals of the transition probabilities from long sequence data have been intensively studied in the past decades. In next generation sequencing (NGS), a large amount of short reads are generated. These short reads can overlap and some regions of the genome may not be sequenced resulting in a new type of data. Based on NGS data, the transition probabilities ofMCcan be estimated by moment estimators. However, the classical asymptotic distribution theory for MC transition probability estimators based on long sequences is no longer valid. Methods In this study, we present the asymptotic distributions of several statistics related to MC based on NGS data. We show that, after scaling by the effective coverage d defined in a previous study by the authors, these statistics based on NGS data approximate to the same distributions as the corresponding statistics for long sequences. Results We apply the asymptotic properties of these statistics for finding the theoretical confidence regions for MC transition probabilities based on NGS short reads data. We validate our theoretical confidence intervals using both simulated data and real data sets, and compare the results with those by the parametric bootstrap method. Conclusions We find that the asymptotic distributions of these statistics and the theoretical confidence intervals of transition probabilities based on NGS data given in this study are highly accurate, providing a powerful tool for NGS data analysis. |
| 资助项目 | [NSFC] ; [National Key R&D Program of China] ; [NCMIS of CAS] ; [LSC of CAS] ; [Youth Innovation Promotion Association of CAS] ; [US National Science Foundation (NSF)] ; [National Institutes of Health (NIH)] |
| 语种 | 英语 |
| CSCD记录号 | CSCD:6791308 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/57719]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 作者单位 | 1.University Southern Calif, Quantitat & Computat Biol Program, Los Angeles, CA 90089 USA 2.复旦大学 3.中国科学院数学与系统科学研究院 |
| 推荐引用方式 GB/T 7714 | Wan, Lin,Kang, Xin,Ren, Jie,et al. Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data[J]. QUANTITATIVE BIOLOGY,2020,8(2):143-154. |
| APA | Wan, Lin,Kang, Xin,Ren, Jie,&Sun, Fengzhu.(2020).Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.QUANTITATIVE BIOLOGY,8(2),143-154. |
| MLA | Wan, Lin,et al."Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data".QUANTITATIVE BIOLOGY 8.2(2020):143-154. |
入库方式： OAI收割
来源：数学与系统科学研究院
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