Taming the Duplication-Loss-Coalescence Model with Integer Linear Programming

The duplication-loss-coalescence (DLC) parsimony model is invaluable for analyzing the complex scenarios of concurrent duplication loss and deep coalescence events in the evolution of gene families. However, inferring such scenarios for already moderately sized families is prohibitive owing to the c...

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Bibliographic Details
Published in:Journal of computational biology Vol. 28; no. 8; p. 758
Main Authors: Paszek, Jarosław, Markin, Alexey, Górecki, Paweł, Eulenstein, Oliver
Format: Journal Article
Language:English
Published: United States 01.08.2021
Subjects:
Algorithms
Computational Biology - methods
Evolution, Molecular
Gene Duplication
Models, Genetic
Multigene Family
Phylogeny
Programming, Linear
coalescence
phylogenetics
DLC
ILP
duplications
losses
reconciliation
ISSN:1557-8666, 1557-8666
Online Access:Get more information
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Summary:The duplication-loss-coalescence (DLC) parsimony model is invaluable for analyzing the complex scenarios of concurrent duplication loss and deep coalescence events in the evolution of gene families. However, inferring such scenarios for already moderately sized families is prohibitive owing to the computational complexity involved. To overcome this stringent limitation, we make the first step by describing a flexible integer linear programming (ILP) formulation for inferring DLC evolutionary scenarios. Then, to make the DLC model more scalable, we introduce four sensibly constrained versions of the model and describe modified versions of our ILP formulation reflecting these constraints. Our simulation studies showcase that our constrained ILP formulations compute evolutionary scenarios that are substantially larger than scenarios computable under our original ILP formulation and the original dynamic programming algorithm by Wu et al. Furthermore, scenarios computed under our constrained DLC models are remarkably accurate compared with corresponding scenarios under the original DLC model, which we also confirm in an empirical study with thousands of gene families.
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ISSN:1557-8666
1557-8666
DOI:10.1089/cmb.2021.0011