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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >LIKELIHOOD OF A PARTICULAR ORDER OF GENETIC MARKERS AND THE CONSTRUCTION OF GENETIC MAPS
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LIKELIHOOD OF A PARTICULAR ORDER OF GENETIC MARKERS AND THE CONSTRUCTION OF GENETIC MAPS

机译:特定的遗传标记顺序和遗传图谱的构建

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We model the recombination process of fungal systems via chromatid exchange in meiosis, which accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers, for both random spore and tetrad data. First, a probability model framework is developed for two genes and then generalized for an arbitrary number of genes. Maximum likelihood estimators (MLEs) for both random and tetrad data are developed. It is shown that the MLE of recombination for tetrad data is uniformly more efficient over that from random spore data by a factor of at least 4 usually. The MLE for the generalized probability framework is computed using the expectation-maximization (EM) algorithm. Pearson's chi-squared statistic is computed as a measure of goodness of fit using a product-multinomial setup. We implement our model with genetic marker data on the whole genome of Neurospora crassa. Simulated annealing is used to search for the best order of genetic markers for each chromosome, and the goodness of fit value is evaluated for model assumptions. Inferred map orders are corroborated by genomic sequence, with the exception of linkage groups I, II, and V.
机译:我们通过减数分裂中的染色单体交换对真菌系统的重组过程进行建模,该过程考虑了遗传间隔中任何指定的遗传标记顺序的任何类型的二价构型,包括随机孢子和四倍体数据。首先,为两个基因开发一个概率模型框架,然后对任意数量的基因进行泛化。针对随机和四元数据开发了最大似然估计器(MLE)。结果表明,四分体数据的重组MLE比随机孢子数据的重组MLE通常效率至少高4倍。使用期望最大化(EM)算法来计算广义概率框架的MLE。皮尔逊(Pearson)的卡方统计量是使用乘积多项式设置进行拟合优度的度量。我们用遗传标记数据对整个神经孢霉基因组实施模型。使用模拟退火来搜索每个染色体的最佳遗传标记顺序,并针对模型假设评估拟合值的优度。除连接基团I,II和V外,基因组序列证实了推断的图谱顺序。

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