
==== Front
Nucleic Acids ResNucleic Acids ResnarnarNucleic Acids Research0305-10481362-4962Oxford University Press 1758682210.1093/nar/gkm382ArticlesSelecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach Stern Adi 1Doron-Faigenboim Adi 1Erez Elana 1Martz Eric 2Bacharach Eran 1Pupko Tal 1*1Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel and 2Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA*To whom correspondence should be addressed. 972-3-640-7693972-3-642-2046talp@post.tau.ac.ilThe authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors

7 2007 22 6 2007 22 6 2007 35 Web Server issue W506 W511 31 1 2007 15 4 2007 30 4 2007 © 2007 The Author(s)2007This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.Biologically significant sites in a protein may be identified by contrasting the rates of synonymous (Ks) and non-synonymous (Ka) substitutions. This enables the inference of site-specific positive Darwinian selection and purifying selection. We present here Selecton version 2.2 (http://selecton.bioinfo.tau.ac.il), a web server which automatically calculates the ratio between Ka and Ks (ω) at each site of the protein. This ratio is graphically displayed on each site using a color-coding scheme, indicating either positive selection, purifying selection or lack of selection. Selecton implements an assembly of different evolutionary models, which allow for statistical testing of the hypothesis that a protein has undergone positive selection. Specifically, the recently developed mechanistic-empirical model is introduced, which takes into account the physicochemical properties of amino acids. Advanced options were introduced to allow maximal fine tuning of the server to the user's specific needs, including calculation of statistical support of the ω values, an advanced graphic display of the protein's 3-dimensional structure, use of different genetic codes and inputting of a pre-built phylogenetic tree. Selecton version 2.2 is an effective, user-friendly and freely available web server which implements up-to-date methods for computing site-specific selection forces, and the visualization of these forces on the protein's sequence and structure.
==== Body
INTRODUCTION
Current protein sequences have been shaped by a prolonged and extensive evolutionary process. Thus, studying a protein's evolutionary history may contribute to the inference of the proteins’ properties. Evolutionary conserved sites may be indicative of active sites or of protein–protein interaction domains, while highly variable sites may represent sites subjected to positive Darwinian selection (1,2). Such positively selected sites may be interpreted as being a consequence of molecular adaptation, which confers an evolutionary advantage to the organism. Detecting the level of selection operating on a given protein is enabled by computing ω, the ratio between non-synonymous (Ka) and synonymous (Ks) substitutions. Sites showing ω values significantly higher than one are indicative of positive Darwinian selection, while sites showing ω values significantly lower than one are indicative of purifying selection (2,3).

Selecton was first introduced in 2005 (4), and implemented an evolutionary codon model (5) which enabled calculating ω at each codon site using a maximum-likelihood (ML) approach. With the advent of sequenced genomes, the comparison of DNA sequences in order to infer meaningful information has become a basic procedure for many researchers. The study of the selection forces operating on a protein, and specifically the attempt to identify positive selection in proteins has been on the rise, and with this rise the number of users of Selecton has increased dramatically. This has motivated us to upgrade the server to include most recent state-of-the-art methods for calculating positive selection. We have thus implemented five evolutionary models (6–8) that enable studying the selection forces operating on a protein. Specifically, these models enable testing whether positive selection has operated on the gene under study. This is achieved by comparing between a null model assuming no positive selection, and a model which allows positive selection. We note that positive selection is not a common phenomenon and is not apparent in most examined datasets (9,10). In these cases, Selecton can be used to accurately infer sites undergoing purifying selection. Both positive and purifying selections are calculated by inferring ω at each codon site. All calculations explicitly take into account the phylogenetic relations among the sequences and the underlying stochastic process of evolution. The value of ω at each site is then translated to a discrete color scale, and projected onto one of the homologous sequences specified by the user. If a 3-dimensional (3D) structure of the protein is available, the scores will also be projected onto the Van-der-Waals surface of the protein.

Various other web servers are available for analyzing the evolutionary forces operating on a gene, including servers which perform analyses only on an amino-acid alignment (e.g. 11,12–14), and servers which perform analyses of positive selection in codon-based alignments (15–18). For instance, The SNAP server (15) is based on counting methods which estimate the number of synonymous and non-synonymous substitutions between all pairs of sequences (19,20), and the Data-Monkey server (16) is based on a combination of model hypothesis testing and codon ancestral sequence reconstruction. The advantage of Selecton over these alternative web servers is that it combines the implementation of state-of-the-art methods for detecting positive selection, in a highly user-friendly interface which requires no previous expert knowledge. This enables the vast community of molecular biology researchers to study positive and purifying selection operating on a protein. Furthermore, expert users may use the advanced options to fine-tune the server to their exact requirements.

We provide here a brief review of Selecton, emphasizing the novel features added in version 2.2 of the server. We further present the results of the server when analyzed on the TRIM5α protein, a viral host-defense factor that has recently reached the spotlight of positive selection studies (3,21). We delineate how the use of the Selecton server enables the effortless detection of the primate species-specific retroviral restriction domain of TRIM5α found previously (21,22).

SELECTON VERSION 2 MAJOR INNOVATIONS
Evolutionary models implemented
Several different evolutionary models were implemented in Selecton version 2.2, each emphasizing different biological phenomena:

M7 and M8: These two models were first introduced by Yang et al. (6). In brief, the M8 model assumes that ω values come from a mixture of a discrete beta distribution, and an additional category ωs ⩾ 1 which allows for positive selection. The M8 is the default model for Selecton runs. The M7 model, nested within M8, does not include the additional ωs category. Since the beta distribution is defined only on the interval [0,1], it thus follows that M7 does not allow for positive selection in the protein.

M8a: This model is a variation on the M8 model. In M8a, the additional category ωs is set to 1. Thus, this model allows only for purifying and neutral selection (7).

M5: This model assumes a gamma distribution over ω (6).

MEC: This model (8) is the only model here which takes into account the differences between amino-acid replacement rates. In brief, this model expands a 20 by 20 amino-acid replacement rate matrix [such as the commonly used JTT matrix (23)] into a 61 by 61 sense-codon rate matrix. Hence, when the non-synonymous ratio Ka is inferred, the different replacement probabilities between amino acids with distinct properties are taken into account. For instance, all other models in Selecton assume that the evolutionary rates of leucine (UUG) being replaced by either tryptophan (UGG) or phenylalanine (UUU) are equal, since both require one transversion. However, according to the JTT matrix the latter is five times more likely than the former. Thus, under the MEC model, a position with radical replacements will obtain a higher Ka value than a position with more moderate replacements. It should be noted that in the MEC model, ω values are not directly equivalent to the Ka/Ks ratios. ω values are used to calculate these ratios, which are later color-coded onto the results For a more elaborate explanation, refer to the Selecton FAQ section (http://selecton.bioinfo.tau.ac.il/faq.html).

Comparison of these models allows for statistical testing of the hypothesis that there is positive selection operating on the protein (H1), by contrasting this hypothesis to a null model (H0). Part of the Selecton output is the likelihood of each model, allowing for comparison using either a likelihood ratio test (LRT) if the models are nested, or by comparing the second order Akaike Information Criterion (AICC) (24) scores if they are not. In brief, an LRT consists of comparing twice the log-likelihood difference of both models to a χ2 table. An alternative approach is to compare the AICC scores defined by − 2 · log L + 2p · (N/N − p − 1), where L represents the likelihood of the model given the data, p represents the number of free parameters and N represents the sequence length. The lower the AICC score, the better the fit of the model to the data, and hence the model is considered more justified.

We recommend performing one of the following comparisons:
M8 against M8a (nested models with one degree of freedom). This is the default comparison performed by Selecton.

M8 against M7 (nested models with two degrees of freedom).

MEC against M8a (non-nested models: five free parameters and four free parameters, respectively)



For a comparison on the advantages and disadvantages of each model, please refer to the Selecton FAQ section (http://selecton.bioinfo.tau.ac.il/faq.html).

To enable easier use of Selecton, at the end of a run with a model which allows for positive selection, the user may click on a button labeled ‘Test Statistical Significance’. This will run Selecton with the appropriate null model, perform the likelihood comparison (LRT or AICc) and output the significance level of this comparison. 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. Nevertheless, it has been previously shown that using a  to approximate the distribution of the LRT when comparing the M8 versus M8a leads to a conservative approach (25). This approach was adopted by Selecton.

Parameter estimation
Parameters common to all models used in Selecton are codon equilibrium frequencies πi, the transition transversion ratio κ and the phylogenetic tree branch lengths. πi are calculated as in (6,26) using the products of the observed nucleotide frequencies (also known as F3X4). κ and branch lengths are all ML estimates. We use an expectation maximization approach (27) to solve the problem of multivariate optimization in the case of branch lengths [a similar approach is described in detail in (28,29)].

An empirical Bayesian method for calculating ω values
The heart of the Selecton server is the calculation of the ω values at each codon position. In the previous version of the server, the ML method (4) was implemented as the sole method of calculation. Recently, we have shown that an empirical Bayesian method can significantly improve the accuracy of inference of conservation scores (30). While the ML method was found to have a relatively high level of false positives, the Bayesian method showed an improved specificity that reduced the level of false positives. The empirical Bayesian method is particularly superior to the ML method when the number of homologous sequences analyzed is small (30). Thus, an empirical Bayesian method of calculating ω values was implemented in the server (6). Following Yang (31), the distributions are approximated using eight discrete categories (the user may define a different number if desired) and the ω values are computed by calculating the expectation of the posterior ω distribution. It should be noted that although more reliable than the ML method, the empirical Bayesian method also suffers from inaccuracy in small data sets, mostly due to sampling errors in the estimation of parameters (such as the distribution shape parameters). Recently two alternatives have been proposed: the full Bayesian estimation (32) and the hierarchical Bayesian estimation (33). However, both alternatives are much more computationally intensive, and hence were not implemented in Selecton.

Reliability of the ω inferences
The reliability of the ω values estimates depends on several factors, including the number of gaps in the MSA, the number of homologous sequences used and their divergence. Thus, an essential improvement implemented in version 2.2 of Selecton is the inclusion of a measure of confidence of the inference. A confidence interval around each ω estimate is defined by the 5th and 95th percentiles of the posterior distribution inferred for each position (see FAQ section of Selecton, http://selecton.bioinfo.tau.ac.il/faq.html). For positions with an inferred ω > 1, if the lower bound of the confidence interval is >1, the inference of positive selection at this position is considered reliable. Selecton furthermore outputs the distribution of the posterior probabilities of ω at each site of the protein.

Visualization of results
The new version of Selecton enables the projection of the ω inferences also onto the primary sequence of the protein. Selecton uses a seven-color scale for representing the different types of selection. Shades of yellow (colors 1 and 2) indicate ω > 1, with dark-yellow standing for sites where reliable positive selection was inferred, and light-yellow standing for positive selection that is not statistically significant. Shades of white through magenta (colors 3 through 7) indicate various level of ω ⩽ 1.

A powerful new 3D visualization tool, FirstGlance in Jmol (FGiJ, http://firstglance.jmol.org), was also implemented in Selecton. FGiJ displays the ω values on the 3D structure of the protein. Its power lies in its easy handling and it needs no installation. It allows preparing presentations with Selecton results snapshots, saving a PDB file containing the Selecton color-coding scheme and allows easy manipulation of different properties of the colored 3D molecule. FGiJ works in all popular web browsers and computer platforms.

Genetic code
Eleven different genetic codes were implemented, including four different nuclear code variants and seven different mitochondrial code variants. This allows the analysis of genes from organisms and organelles which use non-standard genetic codes.

Phylogenetic tree
By default Selecton runs are carried out using phylogenetic trees that the server computes using the neighbor-joining algorithm (34). As input for the neighbor-joining algorithm, pairwise distances are computed applying the ML criterion under a codon model (35) which assumes no selection (ω = 1 for all sites and κ = 2). We note that in general, more accurate tree topologies lead to better estimate of parameters (36,37). However, it was previously shown that the detection of positive selection is in general robust to tree topology inaccuracies (6,38,39). Hence, the strategy we adopted in Selecton was to avoid the computationally intense search for the precise tree topology, yet to allow the user to provide a pre-computed phylogenetic-tree as an additional input. This new feature enables users to supply a more accurate tree, if available. Additionally, users can supply the tree phylogeny and have the server optimize its branch lengths.

Precision level
The precision level of the computations is defined by setting the cutoff (ε), which defines when two likelihood values have converged. Selecton allows the user to choose between three levels of precision, which also directly affect the speed of calculation: low (ε = 1), intermediate (ε = 0.1) and high (ε = 0.01). The default level of precision for Selecton runs is intermediate.

BIOLOGICAL EXAMPLE
We illustrate the power of Selecton to detect site-specific selection forces by analyzing the evolution of the TRIM5α protein, a protein that has recently been shown to have undergone extensive positive selection during the course of primate evolution (21,22). Furthermore, positively selected regions were found to correlate with the species-specificity determinants of the protein. Here, we wish to exemplify the ease with which Selecton enables detecting the species-specific viral restriction domains of TRIM5α.

Study of TRIM5α
TRIM5 is a member of the large tripartite motif family in primate genomes, characterized by having RING finger, B-box and coiled-coil domains, as well as an additional SPRY domain found in the α isoform (40). TRIM5α was found to account for HIV-1 resistance observed in rhesus cells (41,42). It is not yet known how TRIM5α mediates viral restriction, although a shorter, alternate transcript of the TRIM5 gene has been shown to be a ubiquitin ligase (43). TRIM5α restriction probably acts on the viral capsid (44), although direct physical interaction between TRIM5α and the capsid proteins has not yet been demonstrated.

TRIM5α variants from humans, rhesus monkeys and African green monkeys (AGM) display different but overlapping restriction specificities, which all have the following common property: each TRIM5α is unable to restrict retroviruses isolated from the same species, yet is able to restrict most retroviruses from other species (41). This indicates that TRIM5α is an important natural barrier to cross-species retrovirus transmission.

This type of interaction between a host protein and a parasite protein leads to genetic conflict between the two proteins. Such a conflict may lead to rapid fixation of mutations that alter amino acids at the protein–protein interface, which is the hallmark of positive selection (6). Thus, it has been hypothesized that TRIM5α is in an antagonistic conflict with the retroviral capsid proteins. Sawyer et al. (21) analyzed the selection forces acting on TRIM5α and identified a patch of positively selected residues in the SPRY domain. This patch was identified as the species-specific determinant, which is sufficient and necessary for HIV restriction in rhesus monkey cells. Substitution of this patch from the human TRIM5α with the rhesus patch, and vice versa, conferred or abolished HIV-1 restriction, respectively (21). In fact, the region determining the species-specificity of the HIV-1 restriction was eventually mapped to two alternative positions in the rhesus SPRY domains (21). A single arginine to proline replacement at residue 332 of the human TRIM5α, or conversely the exchange of the six residues at positions 335–340 for the eight residues of the rhesus sequence, conferred the human TRIM5α an enhanced ability to restrict HIV-1 (22).

To test the use of Selecton, 20 primate TRIM5α sequences (21) were used as input for the Selecton server. The server was run with the MEC model (log-likelihood = –6716; AICC score = 13 442) and compared with the M8a null model (log-likelihood = –6779; AICC score = 13 564). Since the AICC score of the MEC model is lower, we assume that the MEC model which allows for positives selection indeed fits the TRIM5α data better than a model which does not. The results of the MEC analysis were projected by the server onto the primary sequence of the human TRIM5α (Figure 1). The full results of the run are available in the Gallery section of Selecton (http://selecton.bioinfo.tau.ac.il/gallery.html). The results show an abundance of yellow-colored sites, indicating that TRIM5α has undergone extensive positive selection. Specifically, the two specific determinants conferring HIV-1 species-specific restriction showed exceptionally high levels of positive selection (Figure 1; positions boxed in black), indicating that these sites have undergone excess amino-acid fixations during the course of primate evolution. In fact, the entire SPRY domain (sites 281–493) displays extensive positive selection, as opposed to the RING finger domain (sites 15–59), the B-box domain (sites 90–132) and the coiled-coil domains (sites 130–241), which display mostly purifying selection with some dispersed positively selected sites.
Figure 1. Selecton results for TRIM5α run on 20 primate sequences (21) with the MEC model (8). Positive selection is colored in shades of yellow, and purifying selection is colored in shades of magenta. The two species-specific restriction determinants are indicated in boxes. Replacement of these positions with their rhesus equivalent positions leads to a reversal of restriction characteristics. Both determinants show a significantly high level of positive selection.



CONCLUSIONS
We describe here Selecton version 2.2, a web-based bioinformatics tool for the identification of site-specific positive selection and purifying selection in a protein. The minimal input for the server consists of a file of homologous coding sequences. The server performs a codon-based alignment of the sequences, calculates ω values at each site, translates these ratios into selection scores and projects them onto the primary or tertiary sequence of the protein, allowing visual identification of blocks or patches of sites with similar ω values. Advanced options of the server include choosing the method of calculation, inputting a phylogenetic tree of the homologous sequences and choosing from amongst a number of evolutionary models implemented in the server. To demonstrate the effectiveness of Selecton, the server was run on a dataset of homologous TRIM5α primate sequences. Selecton correctly identified the species-specific restriction determinants of the protein. Thus, this analysis emphasizes the power of Selecton to accurately identify sites undergoing positive selection, and to present these results in a clear and user-friendly way.

ACKNOWLEDGEMENTS
We would like to thank Clemens Lakner, Fredrik Ronquist and two anonymous referees for their helpful comments. We would also like to thank Nimrod Rubinstein, Itay Mayrose and Eyal Privman for their assistance in proofreading. A.S. is a fellow of the Complexity Science Scholarship program and supported by an Israeli Ministry of Science fellowship. A.D.F. is an Israeli Ministry of Science Eshkol fellow. E.M. was supported by a grant from the US National Science Foundation, Division of Undergraduate Education. T.P. is supported by a grant from the Israel Science Foundation, number 1208/04 and a young GIF grant. T.P. and E.B. are supported by a grant from the Israeli Ministry of Science and Technology. Funding to pay the Open Access publication charges for this article was provided by the Israeli Ministry of Science and Technology.

Conflict of interest statement. None declared.
==== Refs
REFERENCES
1 Graur D  Li WH   Fundamentals of molecular evolution 2000 2nd Sunderland, MA Sinauer Press 
2 Miyata T  Yasunaga T   Molecular evolution of mRNA: a method for estimating evolutionary rates of synonymous and amino acid substitutions from homologous nucleotide sequences and its application J. Mol. Evol 1980 16 23 36 6449605 
3 Yang Z   The power of phylogenetic comparison in revealing protein function Proc. Natl Acad. Sci. USA 2005 102 3179 3180 15728394 
4 Doron-Faigenboim A  Stern A  Mayrose I  Bacharach E  Pupko T   Selecton: a server for detecting evolutionary forces at a single amino-acid site Bioinformatics 2005 21 2101 2103 15647294 
5 Goldman N  Yang Z   A codon-based model of nucleotide substitution for protein-coding DNA sequences Mol. Biol. Evol 1994 11 725 736 7968486 
6 Yang Z  Nielsen R  Goldman N  Pedersen AM   Codon-substitution models for heterogeneous selection pressure at amino acid sites Genetics 2000 155 431 449 10790415 
7 Swanson WJ  Nielsen R  Yang Q   Pervasive adaptive evolution in mammalian fertilization proteins Mol. Biol. Evol 2003 20 18 20 12519901 
8 Doron-Faigenboim A  Pupko T   A combined empirical and mechanistic codon model Mol. Biol. Evol 2006 24 388 397 17110464 
9 The Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome Nature 2005 437 69 87 16136131 
10 Bustamante CD  Fledel-Alon A  Williamson S  Nielsen R  Hubisz MT  Glanowski S  Tanenbaum DM  White TJ  Sninsky JJ    Natural selection on protein-coding genes in the human genome Nature 2005 437 1153 1157 16237444 
11 Landau M  Mayrose I  Rosenberg Y  Glaser F  Martz E  Pupko T  Ben-Tal N   ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures Nucleic Acids Res 2005 33 W299 W302 15980475 
12 Edwards RJ  Shields DC   BADASP: predicting functional specificity in protein families using ancestral sequences Bioinformatics 2005 21 4190 4191 16159912 
13 Afonnikov DA  Kolchanov NA   CRASP: a program for analysis of coordinated substitutions in multiple alignments of protein sequences Nucleic Acids Res 2004 32 W64 W68 15215352 
14 Gu X  Vander Velden K   DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family Bioinformatics 2002 18 500 501 11934757 
15 Korber B   HIV Signature and Sequence Variation Analysis. Computational Analysis of HIV Molecular Sequences 2000 Dordrecht, The Netherlands Kluwer Academic Publishers 
16 Pond SL  Frost SD   Datamonkey: rapid detection of selective pressure on individual sites of codon alignments Bioinformatics 2005 21 2531 2533 15713735 
17 Liang H  Zhou W  Landweber LF   SWAKK: a web server for detecting positive selection in proteins using a sliding window substitution rate analysis Nucleic Acids Res 2006 34 W382 W384 16845032 
18 Phylemon: a suite of web-tools for molecular evolution, phylogenetics and phylogenomics Last accessed date April 10, 2007 http://phylemon.bioinfo.cipf.es/cgi-bin/home.cgi  
19 Nei M  Gojobori T   Simple methods for estimating the numbers of synonymous and non-synonymous nucleotide substitutions Mol. Biol. Evol 1986 3 418 426 3444411 
20 Ota T  Nei M   Variance and covariances of the numbers of synonymous and non-synonymous substitutions per site Mol. Biol. Evol 1994 11 613 619 8078400 
21 Sawyer SL  Wu LI  Emerman M  Malik HS   Positive selection of primate TRIM5alpha identifies a critical species-specific retroviral restriction domain Proc. Natl Acad. Sci. USA 2005 102 2832 2837 15689398 
22 Yap MW  Nisole S  Stoye JP   A single amino acid change in the SPRY domain of human Trim5alpha leads to HIV-1 restriction Curr. Biol 2005 15 73 78 15649369 
23 Jones DT  Taylor WR  Thornton JM   The rapid generation of mutation data matrices from protein sequences Comput. Appl. Biosci 1992 8 275 282 1633570 
24 Akaike H   A new look at the statistical model identification IEEE Trans. Automatic Control 1974 119 716 723 
25 Anisimova M  Bielawski JP  Yang Z   Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution Mol. Biol. Evol 2001 18 1585 1592 11470850 
26 Yang Z   PAML: a program package for phylogenetic analysis by maximum likelihood Comput. Appl. Biosci 1997 13 555 556 9367129 
27 Dempster AP  Laird NM  Rubin DB   Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion) J. Roy. Stat. Soc. Ser. B 1977 39 1 38 
28 Mayrose I  Friedman N  Pupko T   A Gamma mixture model better accounts for among site rate heterogeneity Bioinformatics 2005 21 Suppl. 2 ii151 ii158 16204095 
29 Friedman N  Ninio M  Pe'er I  Pupko T   A structural EM algorithm for phylogenetic inference J. Comput. Biol 2002 9 331 353 12015885 
30 Mayrose I  Graur D  Ben-Tal N  Pupko T   Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior Mol. Biol. Evol 2004 21 1781 1791 15201400 
31 Yang Z   Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods J. Mol. Evol 1994 39 306 314 7932792 
32 Huelsenbeck JP  Dyer KA   Bayesian estimation of positively selected sites J. Mol. Evol 2004 58 661 672 15461423 
33 Yang Z  Wong WS  Nielsen R   Bayes empirical bayes inference of amino acid sites under positive selection Mol. Biol. Evol 2005 22 1107 1118 15689528 
34 Saitou N  Nei M   The neighbor-joining method: a new method for reconstructing phylogenetic trees Mol. Biol. Evol 1987 4 406 425 3447015 
35 Nielsen R  Yang Z   Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene Genetics 1998 148 929 936 9539414 
36 Pupko T  Bell RE  Mayrose I  Glaser F  Ben-Tal N   Rate4Site: an algorithmic tool for the identification of functional regions in proteins by surface mapping of evolutionary determinants within their homologues Bioinformatics 2002 18 Suppl. 1 S71 S77 12169533 
37 Mayrose I  Mitchell A  Pupko T   Site-specific evolutionary rate inference: taking phylogenetic uncertainty into account J. Mol. Evol 2005 60 345 353 15871045 
38 Pie MR   The influence of phylogenetic uncertainty on the detection of positive Darwinian selection Mol. Biol. Evol 2006 23 2274 2278 16971694 
39 Kosakovsky Pond SL  Frost SD   Not so different after all: a comparison of methods for detecting amino acid sites under selection Mol. Biol. Evol 2005 22 1208 1222 15703242 
40 Reymond A  Meroni G  Fantozzi A  Merla G  Cairo S  Luzi L  Riganelli D  Zanaria E  Messali S    The tripartite motif family identifies cell compartments EMBO J 2001 20 2140 2151 11331580 
41 Hatziioannou T  Perez-Caballero D  Yang A  Cowan S  Bieniasz PD   Retrovirus resistance factors Ref1 and Lv1 are species-specific variants of TRIM5alpha Proc. Natl Acad. Sci. USA 2004 101 10774 10779 15249685 
42 Hatziioannou T  Cowan S  Goff SP  Bieniasz PD  Towers GJ   Restriction of multiple divergent retroviruses by Lv1 and Ref1 EMBO J 2003 22 385 394 12554640 
43 Xu L  Yang L  Moitra PK  Hashimoto K  Rallabhandi P  Kaul S  Meroni G  Jensen JP  Weissman AM    BTBD1 and BTBD2 colocalize to cytoplasmic bodies with the RBCC/tripartite motif protein, TRIM5delta Exp. Cell Res 2003 288 84 93 12878161 
44 Stremlau M  Owens CM  Perron MJ  Kiessling M  Autissier P  Sodroski J   The cytoplasmic body component TRIM5alpha restricts HIV-1 infection in Old World monkeys Nature 2004 427 848 853 14985764

