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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Markov genealogy processes.

Aaron A King¹, Qianying Lin², Edward L Ionides³

¹Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA; Center for Computational Medicine & Biology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA.

Theoretical Population Biology

|December 13, 2021

Summary

This summary is machine-generated.

We developed new Markov processes for tracking population history and genetic relationships. This enables more accurate phylodynamic inference and efficient computational methods for analyzing evolutionary data.

Keywords:

Hidden Markov model Molecular epidemiology Partially observed Markov process Phylodynamics Phylogeny Statistical inference

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Area of Science:

Mathematical Biology
Computational Biology
Statistics

Background:

Phylodynamic inference relies on understanding the interplay between population dynamics and phylogenetic trees.
Existing methods for inferring evolutionary history from population processes can be computationally intensive.

Purpose of the Study:

To develop a novel mathematical framework for modeling genealogy-valued Markov processes.
To derive exact likelihood expressions for phylodynamic inference.
To create efficient algorithms for analyzing complex population histories.

Main Methods:

Construction of genealogy-valued Markov processes from continuous-time Markov population processes.
Derivation of conditional likelihood expressions for genealogies.
Development of a nonlinear filtering equation for inference.
Application of Monte Carlo methods for algorithm design.

Main Results:

Exact likelihood expressions for genealogies conditional on population history were derived.
A nonlinear filtering equation was established, facilitating efficient inference.
Demonstrated the applicability of the framework through various examples.
Showcased that existing full-information methods are special cases of this theory.

Conclusions:

The developed theory provides a unified and efficient approach to phylodynamic inference.
The new framework enhances the ability to analyze complex population processes and their impact on genetic diversity.
The derived algorithms offer significant computational advantages for phylodynamic studies.