EvoMiner: frequent subtree mining in phylogenetic databases

Akshay Deepak, David Fernández-Baca, Srikanta Tirthapura, Michael J. Sanderson, Michelle M. McMahon

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The problem of mining collections of trees to identify common patterns, called frequent subtrees (FSTs), arises often when trying to interpret the results of phylogenetic analysis. FST mining generalizes the well-known maximum agreement subtree problem. Here we present EvoMiner, a new algorithm for mining frequent subtrees in collections of phylogenetic trees. EvoMiner is an Apriori-like levelwise method, which uses a novel phylogeny-specific constant-time candidate generation scheme, an efficient fingerprinting-based technique for downward closure, and a lowest-common-ancestor-based support counting step that requires neither costly subtree operations nor database traversal. Our algorithm achieves speedups of up to 100 times or more over Phylominer, the current state-of-the-art algorithm for mining phylogenetic trees. EvoMiner can also work in depth-first enumeration mode to use less memory at the expense of speed. We demonstrate the utility of FST mining as a way to extract meaningful phylogenetic information from collections of trees when compared to maximum agreement subtrees and majority-rule trees—two commonly used approaches in phylogenetic analysis for extracting consensus information from a collection of trees over a common leaf set.

Original languageEnglish (US)
Pages (from-to)559-590
Number of pages32
JournalKnowledge and Information Systems
Volume41
Issue number3
DOIs
StatePublished - Nov 7 2014

Keywords

  • Data mining
  • Evolutionary bioinformatics
  • Maximum agreement subtree
  • Pattern discovery
  • Phylogenetics

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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