SCAMPP: Scalable alignment-based phylogenetic placement

Wedell, Eleanor

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https://hdl.handle.net/2142/115569 Copy

Description

Title

SCAMPP: Scalable alignment-based phylogenetic placement

Author(s)

Wedell, Eleanor

Issue Date

2022-04-25

Director of Research (if dissertation) or Advisor (if thesis)

Warnow, Tandy

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Phylogenetic placement
• Maximum likelihood
• Phylogenetics
• pplacer
• EPA-ng

Abstract

Phylogenetic placement, the problem of placing a "query” sequence into a precomputed phylogenetic “backbone” tree, is useful for constructing large trees, performing taxon identification of newly obtained sequences, and other applications. The most accurate current methods, such as pplacer and EPA-ng, are based on maximum likelihood and require that the query sequence be provided within a multiple sequence alignment that includes the leaf sequences in the backbone tree. This approach enables high accuracy but also makes these likelihood-based methods computationally intensive on large backbone trees, and can even lead to them failing when the backbone trees are very large (e.g., having 50,000 or more leaves). We present SCAMPP (SCAlable alignMent-based Phylogenetic Placement), a technique to extend the scalability of these likelihood-based placement methods to ultra-large backbone trees. We show that pplacer-SCAMPP and EPA-ng-SCAMPP both scale well to ultra-large backbone trees (even up to 200,000 leaves), with accuracy that improves on APPLES and APPLES-2, two recently developed fast phylogenetic placement methods that scale to ultra-large datasets. EPA-ng-SCAMPP and pplacer-SCAMPP are available at https://github.com/chry04/PLUSplacer.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Eleanor Wedell

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