Multiple sequence alignment with sequence length heterogeneity and its applications
Shen, Chengze
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https://hdl.handle.net/2142/129918
Description
Title
Multiple sequence alignment with sequence length heterogeneity and its applications
Author(s)
Shen, Chengze
Issue Date
2025-07-07
Director of Research (if dissertation) or Advisor (if thesis)
Warnow, Tandy
Doctoral Committee Chair(s)
Warnow, Tandy
Committee Member(s)
El-Kebir, Mohammed
Gropp, William D
Williams, Kelly P.
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Multiple Sequence Alignment
Metagenomic Analysis
Language
eng
Abstract
Two major challenges in the field of bioinformatics are multiple sequence alignment (MSA) and abundance profiling. Both are hard problems that can become computationally prohibitive with a large amount of input data, which most existing methods can fail to handle. In addition, only a few approaches can adequately align input sequences with various lengths (i.e., sequence length heterogeneity), which are often present in metagenomic data used for profiling species abundances. With the increasing availability and size of sequence data that may have sequence length heterogeneity, new approaches are required to perform scalable and accurate analyses. In this thesis, we show our progress in developing new alignment methods that deal with sequence length heterogeneity and can scale to large data using divide-and-conquer. We also apply the new alignment methods to perform abundance profiling and demonstrate improved accuracy. We hope that the newer methods can help scientists conduct more accurate and effective analyses on larger and larger datasets.
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