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Algorithms 2013, 6(3), 430-441; doi:10.3390/a6030430

Efficient in silico Chromosomal Representation of Populations via Indexing Ancestral Genomes

1
Computational Biology Center, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA
2
Limagrain Europe, Centre de Recherche de Chappes, CS 3911, Route d'Ennezat, Chappes 63720, France
*
Author to whom correspondence should be addressed.
Received: 18 March 2013 / Revised: 18 July 2013 / Accepted: 23 July 2013 / Published: 30 July 2013
(This article belongs to the Special Issue Algorithms for Sequence Analysis and Storage)
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Abstract

One of the major challenges in handling realistic forward simulations for plant and animal breeding is the sheer number of markers. Due to advancing technologies, the requirement has quickly grown from hundreds of markers to millions. Most simulators are lagging behind in handling these sizes, since they do not scale well. We present a scheme for representing and manipulating such realistic size genomes, without any loss of information. Usually, the simulation is forward and over tens to hundreds of generations with hundreds of thousands of individuals at each generation. We demonstrate through simulations that our representation can be two orders of magnitude faster and handle at least two orders of magnitude more markers than existing software on realistic breeding scenarios.
Keywords: genome representation; phenotype computation; plant breeding; populationsimulation; segment genome representation; phenotype computation; plant breeding; populationsimulation; segment
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Haiminen, N.; Utro, F.; Lebreton, C.; Flament, P.; Karaman, Z.; Parida, L. Efficient in silico Chromosomal Representation of Populations via Indexing Ancestral Genomes. Algorithms 2013, 6, 430-441.

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