000			02121nab|a22003377a|4500
999			_c60870 _d60862
001			60870
003			MX-TxCIM
005			20240919020951.0
008			190831s2019||||xxu|||p|op||||00||0|eng|d
022			_a2160-1836
024	8		_ahttps://doi.org/10.1534/g3.119.400373
040			_aMX-TxCIM
041			_aeng
100	1		_aToledo, F.H. _8I1706676 _91999 _gGenetic Resources Program
245	1		_aisqg : _ba binary framework for in silico quantitative genetics
260			_aBethesda, MD (USA) : _bGenetics Society of America, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aThe DNA is the fundamental basis of genetic information, just as bits are for computers. Whenever computers are used to represent genetic data, the computational encoding must be efficient to allow the representation of processes driving the inheritance and variability. This is especially important across simulations in view of the increasing complexity and dimensions brought by genomics. This paper introduces a new binary representation of genetic information. Algorithms as bitwise operations that mimic the inheritance of a wide range of polymorphisms are also presented. Different kinds and mixtures of polymorphisms are discussed and exemplified. Proposed algorithms and data structures were implemented in C++ programming language and is available to end users in the R package "isqg" which is available at the R repository (CRAN). Supplementary data are available online.
546			_aText in English
650		7	_2AGROVOC _98703 _aBioinformatics
650		7	_2AGROVOC _99053 _aRecombination
650		7	_2AGROVOC _98687 _aSimulation
650		7	_2AGROVOC _91130 _aGenetics
700	1		_aPerez-Rodriguez, P. _92703
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aBurgueño, J. _8INT3239 _9907 _gGenetic Resources Program
773	0		_tG3: Genes, Genomes, Genetics _gv. 9, no. 8, p. 2425-2428 _dBethesda, MD (USA) : Genetics Society of America, 2019 _x2160-1836 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20224
942			_cJA _n0 _2ddc