Detecting the presence and location of selection in proteins.

Methods to detect the action of selection on proteins can now make strong predictions about its strength and location, but are becoming increasingly technical. The complexity of the methods makes it difficult to determine and interpret the significance of any selection detected. With more information being extracted from the data, the quality of the protein alignment and phylogeny used becomes increasingly important in assessing whether or not a prediction is merely a statistical artifact. Both data quality issues and statistical assessment of the results are considered.

[1]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

[2]  J. Felsenstein CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP , 1985, Evolution; international journal of organic evolution.

[3]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[4]  M. Kreitman,et al.  Adaptive protein evolution at the Adh locus in Drosophila , 1991, Nature.

[5]  Nick Goldman,et al.  Accuracy and Power of Statistical Methods for Detecting Adaptive Evolution in Protein Coding Sequences and for Identifying Positively Selected Sites , 2004, Genetics.

[6]  J. Kalbfleisch,et al.  A modified likelihood ratio test for homogeneity in finite mixture models , 2001 .

[7]  Sean R. Eddy,et al.  Profile hidden Markov models , 1998, Bioinform..

[8]  T. Massingham,et al.  Detecting Amino Acid Sites Under Positive Selection and Purifying Selection , 2005, Genetics.

[9]  R. Simes,et al.  An improved Bonferroni procedure for multiple tests of significance , 1986 .

[10]  P. Sharp,et al.  The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. , 1987, Nucleic acids research.

[11]  Simon Whelan,et al.  Estimating the Frequency of Events That Cause Multiple-Nucleotide Changes , 2004, Genetics.

[12]  R. Nielsen,et al.  Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. , 1998, Genetics.

[13]  Sergei L. Kosakovsky Pond,et al.  Not so different after all: a comparison of methods for detecting amino acid sites under selection. , 2005, Molecular biology and evolution.

[14]  N. Goldman,et al.  Codon-substitution models for heterogeneous selection pressure at amino acid sites. , 2000, Genetics.

[15]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[16]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[17]  Z. Yang,et al.  Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. , 2001, Molecular biology and evolution.

[18]  F. Wright The 'effective number of codons' used in a gene. , 1990, Gene.

[19]  W. Wong,et al.  Bayes empirical bayes inference of amino acid sites under positive selection. , 2005, Molecular biology and evolution.

[20]  J. Hsu Multiple Comparisons: Theory and Methods , 1996 .

[21]  Yoshiyuki Suzuki,et al.  New Methods for Detecting Positive Selection at Single Amino Acid Sites , 2004, Journal of Molecular Evolution.

[22]  J. Felsenstein Evolutionary trees from DNA sequences: A maximum likelihood approach , 2005, Journal of Molecular Evolution.

[23]  K. Liang,et al.  Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions , 1987 .

[24]  R. Nielsen,et al.  Pervasive adaptive evolution in mammalian fertilization proteins. , 2003, Molecular biology and evolution.

[25]  Nick Goldman,et al.  Statistical tests of models of DNA substitution , 1993, Journal of Molecular Evolution.

[26]  Y. Hochberg A sharper Bonferroni procedure for multiple tests of significance , 1988 .