isqg : a binary framework for in silico quantitative genetics
Toledo, F.H.
isqg : a binary framework for in silico quantitative genetics - Bethesda, MD (USA) : Genetics Society of America, 2019.
Peer review Open Access
The 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.
Text in English
2160-1836
https://doi.org/10.1534/g3.119.400373
Bioinformatics
Recombination
Simulation
Genetics
isqg : a binary framework for in silico quantitative genetics - Bethesda, MD (USA) : Genetics Society of America, 2019.
Peer review Open Access
The 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.
Text in English
2160-1836
https://doi.org/10.1534/g3.119.400373
Bioinformatics
Recombination
Simulation
Genetics