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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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A New Context Tree Inference Algorithm for Variable Length Markov Chain Model with Applications to Biological

Shaokun An1, Jie Ren2, Fengzhu Sun2

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 22, 2022
PubMed
Summary
This summary is machine-generated.

A new method, VLMC-Consistent (VLMC-C), improves statistical inference for biological sequences by addressing biases in the original VLMC-Biased (VLMC-B) algorithm. VLMC-C offers more accurate context tree reconstruction and better model compression for molecular sequence analysis.

Keywords:
biological sequence analysesconsistent context algorithmvariable length Markov chainsword count statistics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • High-order Markov chains (MCs) are crucial for biological sequence analysis but face challenges due to parameter dimensionality.
  • The Variable Length Markov Chain (VLMC) model uses context trees for sparse MC inference.
  • The original VLMC algorithm's pruning step, using a fixed chi-square threshold, exhibits biases due to intercorrelated word counts.

Purpose of the Study:

  • To identify and address the systematic bias and error proneness in the original VLMC context tree inference algorithm (VLMC-Biased).
  • To propose a novel, more accurate algorithm for context tree inference in biological sequences.
  • To improve the accuracy of context tree reconstruction and model compression capacity.

Main Methods:

  • Developed VLMC-Consistent (VLMC-C), an adaptive tree-pruning algorithm.
  • Introduced branch-specific mixed chi-square distributions based on asymptotic normal distributions of word patterns.
  • Validated theoretical distributions with simulated data and compared VLMC-C against VLMC-B using simulated and real genome data.

Main Results:

  • The original VLMC algorithm (VLMC-B) shows systematic bias and error due to intercorrelated word counts affecting the statistic's distribution.
  • VLMC-C utilizes adaptive pruning with consistent branch-specific distributions, overcoming VLMC-B's limitations.
  • VLMC-C demonstrated superior performance over VLMC-B in context tree reconstruction accuracy and model compression.

Conclusions:

  • The proposed VLMC-Consistent (VLMC-C) algorithm provides a statistically sound and accurate method for inferring context trees in biological sequences.
  • VLMC-C effectively mitigates the biases inherent in the fixed-threshold approach of VLMC-B.
  • This advancement offers significant improvements for molecular sequence analysis and bioinformatics applications.