Phylo_dCor: Distance correlation as a novel metric for phylogenetic profiling

Research output: Contribution to journalArticle

Abstract

Background: Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods. Results: Here, we propose an improvement of phylogenetic profiling that enables handling of large genomic datasets and infer global protein-protein interactions. This method uses the distance correlation as a new measure of phylogenetic profile similarity. We constructed robust reference sets and developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation that makes it applicable to large genomic data. Using Saccharomyces cerevisiae and Escherichia coli genome datasets, we showed that Phylo-dCor outperforms phylogenetic profiling methods previously described based on the mutual information and Pearson's correlation as measures of profile similarity. Conclusions: In this work, we constructed and assessed robust reference sets and propose the distance correlation as a measure for comparing phylogenetic profiles. To make it applicable to large genomic data, we developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation. Two R scripts that can be run on a wide range of machines are available upon request.

Original languageEnglish
Article number396
JournalBMC Bioinformatics
Volume18
Issue number1
DOIs
Publication statusPublished - Sep 5 2017

Keywords

  • Distance correlation
  • Phylogenetic profiling
  • Protein-protein interaction

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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