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Efficient in silico Chromosomal Representation of Populations via Indexing Ancestral Genomes
Computational Biology Center, IBM T. J. Watson Research, Yorktown Heights, NY 10598, USA
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; in revised form: 18 July 2013 / Accepted: 23 July 2013 / Published: 30 July 2013
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
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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.
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(3):430-441.
Haiminen, Niina; Utro, Filippo; Lebreton, Claude; Flament, Pascal; Karaman, Zivan; Parida, Laxmi. 2013. "Efficient in silico Chromosomal Representation of Populations via Indexing Ancestral Genomes." Algorithms 6, no. 3: 430-441.