# Global Alignment of Molecular Sequences via Ancestral State Reconstruction

## TL;DR

This paper introduces a new probabilistic model and recursive method for aligning multiple molecular sequences considering insertions, deletions, and substitutions, improving phylogenetic analysis accuracy.

## Contribution

It develops a trace reconstruction framework on trees that accounts for indels, extending previous substitution-only models with rigorous guarantees.

## Key findings

- Provides a recursive alignment procedure with strong guarantees at low mutation rates.
- Establishes a framework for incorporating indels into phylogenetic reconstruction.
- Extends previous models by including insertions and deletions in sequence evolution.

## Abstract

Molecular phylogenetic techniques do not generally account for such common evolutionary events as site insertions and deletions (known as indels). Instead tree building algorithms and ancestral state inference procedures typically rely on substitution-only models of sequence evolution. In practice these methods are extended beyond this simplified setting with the use of heuristics that produce global alignments of the input sequences--an important problem which has no rigorous model-based solution. In this paper we consider a new version of the multiple sequence alignment in the context of stochastic indel models. More precisely, we introduce the following {\em trace reconstruction problem on a tree} (TRPT): a binary sequence is broadcast through a tree channel where we allow substitutions, deletions, and insertions; we seek to reconstruct the original sequence from the sequences received at the leaves of the tree. We give a recursive procedure for this problem with strong reconstruction guarantees at low mutation rates, providing also an alignment of the sequences at the leaves of the tree. The TRPT problem without indels has been studied in previous work (Mossel 2004, Daskalakis et al. 2006) as a bootstrapping step towards obtaining optimal phylogenetic reconstruction methods. The present work sets up a framework for extending these works to evolutionary models with indels.