Multiple sequence alignment with sequence length heterogeneity and its applications

Shen, Chengze

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https://hdl.handle.net/2142/129918 Copy

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

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.

Graduation Semester

2025-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129918

Copyright and License Information

Copyright 2025 Chengze Shen

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