Ancestral Sequence Reconstruction for Co-evolutionary models

TL;DR

This paper develops a Bayesian ancestral sequence reconstruction method for co-evolutionary models, accounting for correlations between sequence elements, and demonstrates improved accuracy over traditional independent-site methods.

Contribution

It introduces a novel reconstruction algorithm for co-evolving sequences modeled by Gaussian and Potts models, extending ancestral inference beyond independent-site assumptions.

Findings

Reconstruction accuracy improves when intra-species correlations are considered.

The method outperforms traditional independent-site algorithms for discrete sequences.

Analytical expressions quantify the quality of ancestral sequence estimates.

Abstract

The ancestral sequence reconstruction problem is the inference, back in time, of the properties of common sequence ancestors from measured properties of contemporary populations. Standard algorithms for this problem assume independent (factorized) evolution of the characters of the sequences, which is generally wrong (e.g. proteins and genome sequences). In this work, we have studied this problem for sequences described by global co-evolutionary models, which reproduce the global pattern of cooperative interactions between the elements that compose it. For this, we first modeled the temporal evolution of correlated real valued characters by a multivariate Ornstein-Uhlenbeck process on a finite tree. This represents sequences as Gaussian vectors evolving in a quadratic potential, who describe selection forces acting on the evolving entities. Under a Bayesian framework, we developed a reconstruction algorithm for these sequences and obtained an analytical expression to quantify the quality of our estimation. We extend this formalism to discrete valued sequences by applying our method to a Potts model. We showed that for both continuous and discrete configurations, there is a wide range of parameters where, to properly reconstruct the ancestral sequences, intra-species correlations must be taken into account. We also demonstrated that, for sequences with discrete elements, our reconstruction algorithm outperforms traditional schemes based on independent site approximations.