Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 30 tok/s
Gemini 2.5 Pro 46 tok/s Pro
GPT-5 Medium 18 tok/s Pro
GPT-5 High 12 tok/s Pro
GPT-4o 91 tok/s Pro
Kimi K2 184 tok/s Pro
GPT OSS 120B 462 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

Pairwise sequence alignment at arbitrarily large evolutionary distance (2207.12543v1)

Published 25 Jul 2022 in q-bio.PE, cs.CE, math.PR, math.ST, and stat.TH

Abstract: Ancestral sequence reconstruction is a key task in computational biology. It consists in inferring a molecular sequence at an ancestral species of a known phylogeny, given descendant sequences at the tip of the tree. In addition to its many biological applications, it has played a key role in elucidating the statistical performance of phylogeny estimation methods. Here we establish a formal connection to another important bioinformatics problem, multiple sequence alignment, where one attempts to best align a collection of molecular sequences under some mismatch penalty score by inserting gaps. Our result is counter-intuitive: we show that perfect pairwise sequence alignment with high probability is possible in principle at arbitrary large evolutionary distances - provided the phylogeny is known and dense enough. We use techniques from ancestral sequence reconstruction in the taxon-rich setting together with the probabilistic analysis of sequence evolution models involving insertions and deletions.

Citations (1)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.