Evolutionary Inference via the Poisson Indel Process

TL;DR

This paper introduces the Poisson Indel Process, a new evolutionary model that simplifies the joint inference of phylogenetic trees and sequence alignments, making computations more efficient and feasible.

Contribution

The paper proposes the Poisson Indel Process, a novel model that reduces the complexity of joint phylogeny and alignment inference from exponential to linear time.

Findings

PIP model enables linear-time inference for phylogenetic analysis.

Bayesian inference with PIP shows comparable results to traditional methods.

The model offers a global Poisson process characterization of sequence evolution.

Abstract

We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classical evolutionary process, the TKF91 model, is a continuous-time Markov chain model comprised of insertion, deletion and substitution events. Unfortunately this model gives rise to an intractable computational problem---the computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a new stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The new model is closely related to the TKF91 model, differing only in its treatment of insertions, but the new model has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared to separate inference of phylogenies and alignments.