Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution.

By Maria Anisimova, J. Bielawski, Ziheng Yang. Molecular biology and evolution, 2001

705 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e26f0efcf787da71171d0f8e0fc45f9735539e39

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Citations in the corpus

Not so different after all: a comparison of methods for detecting amino acid sites under selection. (2005, background): “Previous studies (Anisimova, Bielawski, and Yang 2001, 2002) have found that the likelihood approach of Nielsen and Yang performed well in the absence of synonymous rate variation; in this paper, we are primarily interested in the behavior of the methods when both synonymous and nonsynonymous rates…”
New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. (2010, methodology): “This measure of phylogenetic signal approximately corresponds to the expected number of substitutions per branch and is close to the tree length–based criterion used in Anisimova et al. (2001).”
Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. (2002, methodology): “Computer simulations also confirmed the power of the site-specific analysis (Anisimova, Bielawski, and Yang 2001; see Yang and Bielawski 2000 for a review).”
Positive selection at reproductive ADAM genes with potential intercellular binding activity. (2004, background): “It is also possible that failure to reject the one-ratio model for phylogenies in which rodent lineages outnumber primate lineages is due to lack of power (Anisimova, Bielawski, and Yang 2001).”
γ-MYN: a new algorithm for estimating Ka and Ks with consideration of variable substitution rates (2009, methodology): “To overcome the weakness, there have been methods developed, such as Likelihood Ratio Test (LRT) [53-56] implemented in PAML [57] and Hyphy software [58], to identify positive selection [45,59-61], which tend to be qualitative.”
Class of multiple sequence alignment algorithm affects genomic analysis. (2013, methodology): “The full details of these models and the applicability of a 22 for hypothesis testing are discussed in detail elsewhere (Anisimova et al. 2001; Yang 2006).”
EasyCodeML: A visual tool for analysis of selection using CodeML (2019, neutral): “Pairwise comparisons of these models can be performed using likelihood‐ratio tests (Anisimova et al., 2001).”
A recent polyploidy superimposed on older large-scale duplications in the Arabidopsis genome. (2003, methodology): “However, they estimated that these blocks fell into four dif- Genome Research 141 www.genome.org ferent age classes ranging from 100–200 Mya, using a questionable dating strategy (Sankoff 2001; Wolfe 2001).”
Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. (2003, methodology): “For each data set, we reconstructedsimulation examined the accuracy of the LRT in nonrea neighbor-joining (NJ) tree using PAUP* (Swofford 2000).combinant data sets (Anisimova et al. 2001).”
Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach (2007, methodology): “It should be noted that in the first two nested comparisons, nesting is achieved by fixing one of the parameters on the boundary of the parameter space (6,25), requiring caution when comparing the models with an LRT.”