A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses

doi:10.1089/cmb.2021.0604

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	A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological Sequence Analyses
	An, Shaokun 1; Ren, Jie 2; Sun, Fengzhu 2; Wan, Lin 1,3
	2022-04-22
发表期刊	JOURNAL OF COMPUTATIONAL BIOLOGY
ISSN	1066-5277
页码	18
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.
关键词	biological sequence analyses consistent context algorithm variable length Markov chains word count statistics
DOI	10.1089/cmb.2021.0604
收录类别	SCI
语种	英语
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
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000792234400001
出版者	MARY ANN LIEBERT, INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60418
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, 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 100190, 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.