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Updated: May 27, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Objective method for estimating asymptotic parameters, with an application to sequence alignment.

Sergey Sheetlin¹, Yonil Park, John L Spouge

¹National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland 20894, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|November 9, 2011

Summary

This summary is machine-generated.

Physicists can now objectively estimate parameters for sequence alignment models using the ARRP program. This computational tool replaces subjective data analysis with an objective change-point detection method for improved accuracy.

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Area of Science:

Computational Biology
Statistical Mechanics
Polymer Physics

Background:

Biological sequence alignment is a key computational tool in molecular biology.
The underlying model relates to statistical mechanics of DNA/protein annealing and directed polymers in random media.
Recent advances enable rapid sequence alignment statistics computation, facilitating online biological search engines.

Purpose of the Study:

To develop an objective method for computing regression coefficients in the asymptotic regime of sequence alignment models.
To replace subjective parameter estimation techniques used by physicists.
To enhance the scientific objectivity of parameter estimates in sequence alignment.

Main Methods:

Developed a publicly available computer program named ARRP.
Implemented an objective change-point detection method.
Replaced subjective "eyeballing" of data with automated regime identification.

Main Results:

ARRP program provides an objective technique for computing regression coefficients.
The change-point detection method enhances confidence in the scientific objectivity of parameter estimates.
Reduced computation time for sequence alignment statistics to under 1 second.

Conclusions:

The ARRP program offers an objective alternative to subjective methods for estimating asymptotic regimes in sequence alignment.
Objective parameter estimation increases confidence in scientific findings.
Asymptotic regression techniques have broad applicability across physics disciplines.