The budgeted maximum coverage problem
Information Processing Letters
Approximation algorithms
Reconstructing sibling relationships in wild populations
Bioinformatics
Set covering approach for reconstruction of sibling relationships
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
On approximating four covering and packing problems
Journal of Computer and System Sciences
On Approximating an Implicit Cover Problem in Biology
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Discovering kinship through small subsets
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Computers and Operations Research
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
SibJoin: a fast heuristic for half-sibling reconstruction
WABI'12 Proceedings of the 12th international conference on Algorithms in Bioinformatics
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With improved tools for collecting genetic data from natural and experimental populations, new opportunities arise to study fundamental biological processes, including behavior, mating systems, adaptive trait evolution, and dispersal patterns. Full use of the newly available genetic data often depends upon reconstructing genealogical relationships of individual organisms, such as sibling reconstruction. This paper presents a new optimization framework for sibling reconstruction from single generation microsatellite genetic data. Our framework is based on assumptions of parsimony and combinatorial concepts of Mendel's inheritance rules. Here, we develop a novel optimization model for sibling reconstruction as a large-scale mixed-integer program (MIP), shown to be a generalization of the set covering problem. We propose a new heuristic approach to efficiently solve this large-scale optimization problem. We test our approach on real biological data as presented in other studies as well as simulated data, and compare our results with other state-of-the-art sibling reconstruction methods. The empirical results show that our approaches are very efficient and outperform other methods while providing the most accurate solutions for two benchmark data sets. The results suggest that our framework can be used as an analytical and computational tool for biologists to better study ecological and evolutionary processes involving knowledge of familial relationships in a wide variety of biological systems.