Literature DB >> 28293276

Approximating the correction of weighted and unweighted orthology and paralogy relations.

Riccardo Dondi1, Manuel Lafond2, Nadia El-Mabrouk3.   

Abstract

BACKGROUND: Given a gene family, the relations between genes (orthology/paralogy), are represented by a relation graph, where edges connect pairs of orthologous genes and "missing" edges represent paralogs. While a gene tree directly induces a relation graph, the converse is not always true. Indeed, a relation graph is not necessarily "satisfiable", i.e. does not necessarily correspond to a gene tree. And even if that holds, it may not be "consistent", i.e. the tree may not represent a true history in agreement with a species tree. Previous studies have addressed the problem of correcting a relation graph for satisfiability and consistency. Here we consider the weighted version of the problem, where a degree of confidence is assigned to each orthology or paralogy relation. We also consider a maximization variant of the unweighted version of the problem.
RESULTS: We provide complexity and algorithmic results for the approximation of the considered problems. We show that minimizing the correction of a weighted graph does not admit a constant factor approximation algorithm assuming the unique game conjecture, and we give an n-approximation algorithm, n being the number of vertices in the graph. We also provide polynomial time approximation schemes for the maximization variant for unweighted graphs.
CONCLUSIONS: We provided complexity and algorithmic results for variants of the problem of correcting a relation graph for satisfiability and consistency. For the maximization variants we were able to design polynomial time approximation schemes, while for the weighted minimization variants we were able to provide the first inapproximability results.

Entities:  

Keywords:  Approximation algorithms; Gene tree; Orthology; Paralogy; Species tree

Year:  2017        PMID: 28293276      PMCID: PMC5346272          DOI: 10.1186/s13015-017-0096-x

Source DB:  PubMed          Journal:  Algorithms Mol Biol        ISSN: 1748-7188            Impact factor:   1.405


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