ImPhy: Imputing Phylogenetic Trees with Missing Information Using Mathematical Programming

Niko Yasui, Chrysafis Vogiatzis, Ruriko Yoshida, Kenji Fukumizu

Research output: Contribution to journalArticlepeer-review


Advances in modern genomics have allowed researchers to apply phylogenetic analyses on a genome-wide scale. While large volumes of genomic data can be generated cheaply and quickly, data missingness is a non-trivial and somewhat expected problem. Since the available information is often incomplete for a given set of genetic loci and individual organisms, a large proportion of trees that depict the evolutionary history of a single genetic locus, called gene trees, fail to contain all individuals. Data incompleteness causes difficulties in data collection, information extraction, and gene tree inference. Furthermore, identifying outlying gene trees, which can represent horizontal gene transfers, gene duplications, or hybridizations, is difficult when data is missing from the gene trees. The typical approach is to remove all individuals with missing data from the gene trees, and focus the analysis on individuals whose information is fully available - a huge loss of information. In this work, we propose and design an optimization-based imputation approach to infer the missing distances between leaves in a set of gene trees via a mixed integer non-linear programming model. We also present a new research pipeline, imPhy, that can (i) simulate a set of gene trees with leaves randomly missing in each tree, (ii) impute the missing pairwise distances in each gene tree, (iii) reconstruct the gene trees using the Neighbor Joining (NJ) and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) methods, and (iv) analyze and report the efficiency of the reconstruction. To impute the missing leaves, we employ our newly proposed non-linear programming framework, and demonstrate its capability in reconstructing gene trees with incomplete information in both simulated and empirical datasets. In the empirical datasets apicomplexa and lungfish, our imputation has very small normalized mean square errors, even in the extreme case where 50 percent of the individuals in each gene tree are missing. Data, software, and user manuals can be found at

Original languageEnglish (US)
Article number8554124
Pages (from-to)1222-1230
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number4
StatePublished - Jul 1 2020
Externally publishedYes


  • Gene trees
  • missing information
  • mixed integer non-linear programming

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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