A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses
文献类型：期刊论文
| 作者 | An, Shaokun3; Ren, Jie2; Sun, Fengzhu2; Wan, Lin1,3 |
| 刊名 | JOURNAL OF COMPUTATIONAL BIOLOGY
 |
| 出版日期 | 2022-04-22 |
| 页码 | 18 |
| ISSN号 | 1066-5277 |
| 关键词 | biological sequence analyses consistent context algorithm variable length Markov chains word count statistics |
| DOI | 10.1089/cmb.2021.0604 |
| 英文摘要 | The statistical inference of high-order Markov chains (MCs) for biological sequences is vital for molecular sequence analyses but can be hindered by the high dimensionality of free parameters. In the seminal article by Buhlmann and Wyner, variable length Markov chain (VLMC) model was proposed to embed the full-order MC in a sparse structured context tree. In the key procedure of tree pruning of their proposed context algorithm, the word count-based statistic for each branch was defined and compared with a fixed cutoff threshold calculated from a common chi-square distribution to prune the branch of the context tree. In this study, we find that the word counts for each branch are highly intercorrelated, resulting in non-negligible effects on the distribution of the statistic of interest. We demonstrate that the inferred context tree based on the original context algorithm by Buhlmann and Wyner, which uses a fixed cutoff threshold based on a common chi-square distribution, can be systematically biased and error prone. We denote the original context algorithm as VLMC-Biased (VLMC-B). To solve this problem, we propose a new context tree inference algorithm using an adaptive tree-pruning scheme, termed VLMC-Consistent (VLMC-C). The VLMC-C is founded on the consistent branch-specific mixed chi-square distributions calculated based on asymptotic normal distribution of multiple word patterns. We validate our theoretical branch-specific asymptotic distribution using simulated data. We compare VLMC-C with VLMC-B on context tree inference using both simulated and real genome sequence data and demonstrate that VLMC-C outperforms VLMC-B for both context tree reconstruction accuracy and model compression capacity. |
| 资助项目 | National Key Research and Development Program of China[2019YFA0709501] ; National Natural Science Foundation of China[12071466] ; National Institutes of Health[R01GM120624] ; National Institutes of Health[1R01GM131407] ; National Science Foundation[EF-2125142] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| 出版者 | MARY ANN LIEBERT, INC |
| WOS记录号 | WOS:000792234400001 |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/60417]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Wan, Lin |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Southern Calif Los Angeles, Quantitat & Computat Biol Dept, Los Angeles, CA USA 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | An, Shaokun,Ren, Jie,Sun, Fengzhu,et al. A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses[J]. JOURNAL OF COMPUTATIONAL BIOLOGY,2022:18. |
| APA | An, Shaokun,Ren, Jie,Sun, Fengzhu,&Wan, Lin.(2022).A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses.JOURNAL OF COMPUTATIONAL BIOLOGY,18. |
| MLA | An, Shaokun,et al."A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses".JOURNAL OF COMPUTATIONAL BIOLOGY (2022):18. |
入库方式： OAI收割
来源：数学与系统科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。