| ID | 68931 | |||||||||||||||||||||
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| Build | R-qtl-1.66-4.fc40 | |||||||||||||||||||||
| Name | R-qtl | |||||||||||||||||||||
| Version | 1.66 | |||||||||||||||||||||
| Release | 4.fc40 | |||||||||||||||||||||
| Epoch | ||||||||||||||||||||||
| Arch | riscv64 | |||||||||||||||||||||
| Draft | False | |||||||||||||||||||||
| Summary | Tools for analyzing QTL experiments | |||||||||||||||||||||
| Description | R-qtl is an extensible, interactive environment for mapping quantitative trait loci (QTLs) in experimental crosses. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R-qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression. | |||||||||||||||||||||
| Build Time | 2024-05-02 07:15:06 GMT | |||||||||||||||||||||
| Size | 5.27 MB | |||||||||||||||||||||
| 87abd0686011e644346f3fb7887d852c | ||||||||||||||||||||||
| License | GPL-3.0-only | |||||||||||||||||||||
| Buildroot | f40-build-48216-2468 | |||||||||||||||||||||
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