University of Illinois Urbana-Champaign

Large-scale methods for multiple sequence alignment and phylogeny estimation

Smirnov, Vladimir A

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https://hdl.handle.net/2142/113015

Description

Title
Large-scale methods for multiple sequence alignment and phylogeny estimation
Author(s)
Smirnov, Vladimir A
Issue Date
2021-07-12
Director of Research (if dissertation) or Advisor (if thesis)
Warnow, Tandy
Doctoral Committee Chair(s)
Warnow, Tandy
Committee Member(s)
  • Peng, Jian
  • Forsyth, David
  • Treangen, Todd
  • Pop, Mihai
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2022-01-12T21:45:35Z
Keyword(s)
  • bioinformatics
  • computational biology
  • genetic sequences
  • multiple sequence alignment
  • phylogeny
  • big data
Abstract
Bioinformatic analyses generally involve passing genetic sequence data through a pipeline of transformations. These prominently include multiple sequence alignment (MSA) and phylogeny estimation, which are both core challenges in computational biology. Their established mathematical formulations are usually difficult NP-hard problems that are attacked with careful and laborious heuristics. Consequently, the most accurate methods tend to be the most computationally expensive with increasing dataset size, and this limits the analysis to datasets of only modest volume. Larger datasets are becoming increasingly available and require qualitatively different approaches. In this thesis, we consider divide-and-conquer frameworks that employ slow-but-accurate base methods to efficiently solve the problem piecewise. The continued development of accurate and efficient large-scale methods will, it is hoped, facilitate more effective bioinformatic analysis of larger and larger datasets.
Graduation Semester
2021-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/113015
Copyright and License Information
Copyright 2021 Vladimir Smirnov

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