the hiv-1 virus is the causative agent of aids , a growing worldwide epidemic and also a fascinating system for studying fundamental scientific questions . for example , one major clinical problem in the treatment of aids is hiv 's ability to develop resistance to antiviral drugs rapidly , often within weeks of introduction of a new drug ( 13 ) . foremost among the factors responsible for this are the virus ' extremely high mutation rate ( 4,5 ) and replication rate ( 3,68 ) . for this reason , there is great medical interest in understanding both the specific causes of drug resistance , and predicting fast versus slow evolutionary pathways to multiple drug resistance . at the same time , hiv provides an extraordinary wealth of data about fundamental scientific questions such as the fitness landscape for protein evolution ( 9,10 ) . evolutionary biology has developed a powerful and general approach for investigating such problems : metrics of selection pressure that measure whether a particular genetic change is selected for or against during evolution . such metrics can reveal important selection forces either constraining or driving evolution of a protein , directly from raw sequence variation data ( 11,12 ) . one very widely used metric of selection pressure on amino acid mutations is known as ka / ks or dn / ds ( 13,14 ) and measures the ratio of observed amino acid mutations over observed synonymous mutations , normalized by the ratio expected under a neutral model . ordinarily ka / ks is  1 , indicating negative selection against amino acid mutations ( far fewer observed than expected under a neutral model ) . ka / ks > 1 is referred to as positive selection ( i.e. amino acid mutations increase reproductive fitness ) and is observed in rare cases where new evolutionary challenges create strong pressure for rapid evolution of a protein ( e.g. immune system genes like mhc that are involved in recognizing pathogenic antigens ) . ordinarily , a single ka / ks value is calculated for a whole gene , but with very large datasets it becomes possible to estimate distinct ka / ks values for individual codon positions or amino acid mutations . this yields a  selection pressure map of a gene , revealing its detailed functional constraints and in rare cases positive selection peaks that signal important new evolutionary pressures such as drug treatment . we used ka / ks because it provides a powerful tool for detecting positive selection . phylogenetic analysis of our hiv sequence dataset using phylip ( 15 ) shows a star - like topology ( data are available at , but will be presented in detail elsewhere ) , in agreement with previous studies ( 16,17 ) . we have assembled a large - scale database that provides researchers detailed selection pressure maps of hiv proteins involved in drug resistance . these data have many possible applications , including prediction of mutations contributing to drug resistance , distinguishing primary drug resistance mutations from accessory mutations , rate measurements of fast versus slow evolutionary pathways to multiple drug resistance , and the evolutionary dynamics of different types of mutations as the virus moves from untreated to drug - treated conditions and back . this database makes available for the first time a very large hiv sequence dataset ( sequences from 50  000 clinical aids samples ) , which makes possible high - resolution selection pressure mapping , as well as smaller datasets from other public databases . the methods and most of the data described herein have been published previously ( 12,18 ) . the primary dataset consists of sequences for hiv protease and reverse transcriptase ( rt ) for 50  000 clinical aids patient samples from the united states , collected during 19992003 ( 12 ) , and mostly under drug treatment . these data cover 1.4 kb each [ 300  000 chromatograms ; six overlapping reads per sample , including both strands ; see ( 12 ) for details ] and were generously contributed by specialty laboratories inc . owing to hiv 's high mutation rate , on average each sequence contains 32 mutations / kb [ with respect to the los alamos reference sequence ( 12 ) ] , for a total of more than 2 million mutation observations in the dataset ( 12 ) . over 5000 distinct codon mutations were observed , each with an average count of 364 samples ( 12 ) . for comparison , this very large dataset , made available publicly for the first time , has made detailed selection pressure mapping possible . of the samples , 99.3% are subtype b ; non - subtype - b samples were excluded from the analysis ( 12 ) . the dataset is fully hipaa - compliant ; all information concerning the source patients was removed by specialty . the stanford - treated dataset consists of 1797 subtype b samples with known drug treatments . this dataset provides a useful comparison with the specialty results , for validating whether a specific mutation is reproducibly selected by drug treatment . the stanford - untreated dataset consists of 2628 subtype b samples not under drug treatment . by comparing results from this dataset with specialty and stanford - treated , users can assess whether a specific mutation is more likely to be associated with drug resistance or other types of phenotypic fitness effects ( e.g. interactions with the immune system ) . the first aspect of the database is mapping of ka / ks selection pressure at each codon position in hiv protease and the first 381 codon positions of rt ( figure 1 ) . codon - specific selection pressure ( 12 ) was calculated using the following formula : 

 where na and ns are the number of amino acid mutations and synonymous mutations observed at the codon , na , t is the number of possible transition mutations in the codon that would change the resulting amino acid , ns , t is the number of possible transition mutations that are synonymous , na , v and ns , v are the equivalent numbers for transversions , and ft and fv are the transition and transversion frequencies , respectively . we calculated an lod confidence score for a codon to be under positive selection pressure according to the following formula : 
 lod=log10p(inan , q , kaks=1)=log10i = nan(ni)qi(1q)ni 
 where n is the total number of mutations observed in the codon and q is calculated as follows : 
 q = na , tft+na , vfv3ft+6fv . this analysis includes ka / ks values for 2946 individual amino acid mutations ( 12 ) at 399 codon positions with lod scores > 2 . for example , strong positive selection ( ka / ks > 1 ) indicates drug - resistance mutations or important fitness effects . experimental validation data in hiv protease ( where causes of drug resistance are well characterized ) showed that 19 of 23 known drug resistance codons were correctly predicted by our database , which also accurately predicts the mutant enzyme 's activity phenotype ( 12,20 ) . of the 47 positively selected sites found in the specialty dataset , 28 were also found in the stanford - untreated dataset , possibly indicating that those sites can harbor fitness mutations ( 18 ) . the database has a simple graphical interface ( figure 1 ) : users can peruse the codon - position selection pressure map directly , click on a position , and inspect detailed tabular results grouped either by codon position , individual amino acid mutations or individual nucleotide mutations ( e.g. to see whether two different nucleotide mutations producing the same amino acid replacement show the same ka / ks value ) . clicking on any codon position performs a query and returns the results in an easy - to - read format . the massive size of the specialty dataset makes it possible to measure how selection pressure for one amino acid mutation y is affected by amino acid mutations at other sites x. specifically , the database computes ka / ks for mutation y conditioned on the presence of amino acid mutations at site x versus the absence of any mutation at site x. this  conditional ka / ks ( 18 ) calculation is performed as follows : 
 ( kaks)yxa = nyaxa / nysxa(na , tft+na , vfv)/(ns , tft+ns , vfv ) , 
 where nyaxa and nysxa are the numbers of amino acid mutations and synonymous mutations at site y observed in the presence of amino acid mutations at site x and all other variables retain their previous definitions . dividing this result by the one obtained in the absence of any mutation at site x to arrive at the  conditional selection ratio ( 18 ) results in the following expression : 
 ( kaks)yx=(ka / ks)yxa(ka / ks)yxo = nyaxa / nysxanyaxo / nysxo , 
 where nyaxo and nysxo are the numbers of samples containing either an amino acid mutation or synonymous mutation at y and no mutation at x. the lod score by which we evaluated the significance of apparent positive conditional selection was calculated using the following : 

 where n = nyaxa+nysxa and q as defined above . for experimental validation , this database correctly predicted 80 of 92 known mutation positive interaction pairs identified in hiv protease by independent experimental studies ( p - value = 10 ) ( 11,18 ) . the database again provides a graphical interface ( figure 2 ) as a 2d heatmap showing all pairwise interactions , which users can click at any position to inspect detailed tabular results . selection pressure interaction map . the degree to which a mutation at one site x ( horizontal axis ) affects the selection pressure at another site y ( vertical axis ) is shown as the condtional selection ratio for all amino acid mutations at site y conditioned on any amino acid mutation at site x. the color coding scale indicates increasing values of positive conditional selection ratio . interactions showing conditional selection ratios > 1 ( positive conditional selection ) with lod scores > 3 are shown , with blue indicating stronger interactions and yellow indicating weaker ones . for example , the data show a significant interaction between protease site 90 ( a known drug resistance mutation site ) and site 10 ( figure 3 ) . amino acid mutations at 90 displayed strong , unconditional positive selection , indicating that they directly cause drug resistance . in contrast , mutations at 10 are negatively selected in the absence of the 90 mutation , but become positively selected in the presence of the 90 mutation ( figure 3 ) . these results closely match previous experimental studies showing that mutations at 90 cause drug resistance , while mutations at 10 have an accessory effect of compensating for the destabilizing effect of mutations at 90 ( 21 ) . thus , our database can help users by providing information that can distinguish primary drug - resistance mutations from accessory mutations ( 18 ) . users can navigate through links on every result page , to see mutations that strongly select for a given mutation , mutations that are strongly selected for by this mutation , or links to the stanford ( 22 ) and los alamos hiv databases ( 23 ) giving further information about mutations at this site . for the two possible pathways from wild - type protease to the 10/90 double mutant , we computed the conditional ka / ks values for each mutation conditioned on the presence or absence of the other mutation ( shown as numbers next to each edge in the figure ) . for example , in the absence of the 10 mutation , the 90 mutation shows strong positive selection in both the specialty and stanford - treated datasets , but was negatively selected in the stanford - untreated dataset . since the steady - state speed of a multistep path is determined by its slowest step , we highlighted the rate - limiting step in each path ( boldface ) . for example , in the specialty dataset , the steady - state rate of the upper pathway appears to be 10-fold faster than that of the lower pathway . ( a ) specialty dataset , ( b ) stanford - treated dataset and ( c ) stanford - untreated dataset . comparison between the independent datasets in the database can shed additional light on such questions . for example , users can assess whether positively selected mutations in the specialty dataset are really due to drug resistance , by comparing with the stanford - treated and stanford - untreated datasets . as shown in figure 3b and c , the stanford - treated data strongly corroborate the specialty result , while the stanford - untreated data show that 90 is indeed involved in drug resistance ; it becomes strongly negatively selected in the absence of drug treatment . these data can help users distinguish genuine drug - treatment mutations from those that affect phenotype in other ways , e.g. interactions with the host immune system . detailed analysis of these datasets demonstrates that the ka / ks results are highly reproducible : independent datasets from different sets of patients show strong quantitative agreement ( 18 ) . the primary dataset consists of sequences for hiv protease and reverse transcriptase ( rt ) for 50  000 clinical aids patient samples from the united states , collected during 19992003 ( 12 ) , and mostly under drug treatment . these data cover 1.4 kb each [ 300  000 chromatograms ; six overlapping reads per sample , including both strands ; see ( 12 ) for details ] and were generously contributed by specialty laboratories inc . owing to hiv 's high mutation rate , on average each sequence contains 32 mutations / kb [ with respect to the los alamos reference sequence ( 12 ) ] , for a total of more than 2 million mutation observations in the dataset ( 12 ) . over 5000 distinct codon mutations were observed , each with an average count of 364 samples ( 12 ) . for comparison , this very large dataset , made available publicly for the first time , has made detailed selection pressure mapping possible . of the samples , 99.3% are subtype b ; non - subtype - b samples were excluded from the analysis ( 12 ) . the dataset is fully hipaa - compliant ; all information concerning the source patients was removed by specialty . the stanford - treated dataset consists of 1797 subtype b samples with known drug treatments . this dataset provides a useful comparison with the specialty results , for validating whether a specific mutation is reproducibly selected by drug treatment . the stanford - untreated dataset consists of 2628 subtype b samples not under drug treatment . by comparing results from this dataset with specialty and stanford - treated , users can assess whether a specific mutation is more likely to be associated with drug resistance or other types of phenotypic fitness effects ( e.g. interactions with the immune system ) . the first aspect of the database is mapping of ka / ks selection pressure at each codon position in hiv protease and the first 381 codon positions of rt ( figure 1 ) . further positions in rt were not sequenced in this dataset . codon - specific selection pressure ( 12 ) was calculated using the following formula : 

 where na and ns are the number of amino acid mutations and synonymous mutations observed at the codon , na , t is the number of possible transition mutations in the codon that would change the resulting amino acid , ns , t is the number of possible transition mutations that are synonymous , na , v and ns , v are the equivalent numbers for transversions , and ft and fv are the transition and transversion frequencies , respectively . we calculated an lod confidence score for a codon to be under positive selection pressure according to the following formula : 
 lod=log10p(inan , q , kaks=1)=log10i = nan(ni)qi(1q)ni 
 where n is the total number of mutations observed in the codon and q is calculated as follows : 
 q = na , tft+na , vfv3ft+6fv . this analysis includes ka / ks values for 2946 individual amino acid mutations ( 12 ) at 399 codon positions with lod scores > 2 . for example , strong positive selection ( ka / ks > 1 ) indicates drug - resistance mutations or important fitness effects . experimental validation data in hiv protease ( where causes of drug resistance are well characterized ) showed that 19 of 23 known drug resistance codons were correctly predicted by our database , which also accurately predicts the mutant enzyme 's activity phenotype ( 12,20 ) . of the 47 positively selected sites found in the specialty dataset , 28 were also found in the stanford - untreated dataset , possibly indicating that those sites can harbor fitness mutations ( 18 ) . the database has a simple graphical interface ( figure 1 ) : users can peruse the codon - position selection pressure map directly , click on a position , and inspect detailed tabular results grouped either by codon position , individual amino acid mutations or individual nucleotide mutations ( e.g. to see whether two different nucleotide mutations producing the same amino acid replacement show the same ka / ks value ) . the interface to the positive selection mutation database is a clickable imagemap . clicking on any codon position performs a query and returns the results in an easy - to - read format . the massive size of the specialty dataset makes it possible to measure how selection pressure for one amino acid mutation y is affected by amino acid mutations at other sites x. specifically , the database computes ka / ks for mutation y conditioned on the presence of amino acid mutations at site x versus the absence of any mutation at site x. this  conditional ka / ks ( 18 ) calculation is performed as follows : 
 ( kaks)yxa = nyaxa / nysxa(na , tft+na , vfv)/(ns , tft+ns , vfv ) , 
 where nyaxa and nysxa are the numbers of amino acid mutations and synonymous mutations at site y observed in the presence of amino acid mutations at site x and all other variables retain their previous definitions . dividing this result by the one obtained in the absence of any mutation at site x to arrive at the  conditional selection ratio ( 18 ) results in the following expression : 
 ( kaks)yx=(ka / ks)yxa(ka / ks)yxo = nyaxa / nysxanyaxo / nysxo , 
 where nyaxo and nysxo are the numbers of samples containing either an amino acid mutation or synonymous mutation at y and no mutation at x. the lod score by which we evaluated the significance of apparent positive conditional selection was calculated using the following : 

 where n = nyaxa+nysxa and q as defined above . for experimental validation , this database correctly predicted 80 of 92 known mutation positive interaction pairs identified in hiv protease by independent experimental studies ( p - value = 10 ) ( 11,18 ) . the database again provides a graphical interface ( figure 2 ) as a 2d heatmap showing all pairwise interactions , which users can click at any position to inspect detailed tabular results . selection pressure interaction map . the degree to which a mutation at one site x ( horizontal axis ) affects the selection pressure at another site y ( vertical axis ) is shown as the condtional selection ratio for all amino acid mutations at site y conditioned on any amino acid mutation at site x. the color coding scale indicates increasing values of positive conditional selection ratio . interactions showing conditional selection ratios > 1 ( positive conditional selection ) with lod scores > 3 are shown , with blue indicating stronger interactions and yellow indicating weaker ones . for example , the data show a significant interaction between protease site 90 ( a known drug resistance mutation site ) and site 10 ( figure 3 ) . amino acid mutations at 90 displayed strong , unconditional positive selection , indicating that they directly cause drug resistance . in contrast , mutations at 10 are negatively selected in the absence of the 90 mutation , but become positively selected in the presence of the 90 mutation ( figure 3 ) . these results closely match previous experimental studies showing that mutations at 90 cause drug resistance , while mutations at 10 have an accessory effect of compensating for the destabilizing effect of mutations at 90 ( 21 ) . thus , our database can help users by providing information that can distinguish primary drug - resistance mutations from accessory mutations ( 18 ) . users can navigate through links on every result page , to see mutations that strongly select for a given mutation , mutations that are strongly selected for by this mutation , or links to the stanford ( 22 ) and los alamos hiv databases ( 23 ) giving further information about mutations at this site . for the two possible pathways from wild - type protease to the 10/90 double mutant , we computed the conditional ka / ks values for each mutation conditioned on the presence or absence of the other mutation ( shown as numbers next to each edge in the figure ) . for example , in the absence of the 10 mutation , the 90 mutation shows strong positive selection in both the specialty and stanford - treated datasets , but was negatively selected in the stanford - untreated dataset . since the steady - state speed of a multistep path is determined by its slowest step , we highlighted the rate - limiting step in each path ( boldface ) . for example , in the specialty dataset , the steady - state rate of the upper pathway appears to be 10-fold faster than that of the lower pathway . ( a ) specialty dataset , ( b ) stanford - treated dataset and ( c ) stanford - untreated dataset . comparison between the independent datasets in the database can shed additional light on such questions . for example , users can assess whether positively selected mutations in the specialty dataset are really due to drug resistance , by comparing with the stanford - treated and stanford - untreated datasets . as shown in figure 3b and c , the stanford - treated data strongly corroborate the specialty result , while the stanford - untreated data show that 90 is indeed involved in drug resistance ; it becomes strongly negatively selected in the absence of drug treatment . these data can help users distinguish genuine drug - treatment mutations from those that affect phenotype in other ways , e.g. interactions with the host immune system . detailed analysis of these datasets demonstrates that the ka / ks results are highly reproducible : independent datasets from different sets of patients show strong quantitative agreement ( 18 ) . first , we will add data for additional hiv genes , such as the env gene , which is important for hiv immune evasion ( 24 ) ; although these datasets have smaller numbers of sequences , our analysis has shown that useful ka / ks mapping information can be obtained from such counts . second , we will analyze mutation data from patients under specific drug - treatment to compare selection pressures caused by different drugs . third , we will add datasets for other hiv subtypes ( e.g. subtype c ) to reveal , where selection pressure patterns appear to be consistent with those seen in subtype b ( allowing diagnostic criteria from subtype b to be applied to other subtypes ) versus where there are important differences . fourth , we will add a new very large dataset for the hepatitis c core gene , consisting of approximately 60  000 samples , generously donated by specialty laboratories . lastly , we will add new analyses and graphical interfaces to the database , including phylogenetic analysis and clickable pathway diagrams .
the hiv positive selection mutation database is a large - scale database available at   that provides detailed selection pressure maps of hiv protease and reverse transcriptase , both of which are molecular targets of antiretroviral therapy . 
 this database makes available for the first time a very large hiv sequence dataset ( sequences from 50  000 clinical aids samples , generously contributed by specialty laboratories , inc . ) , which makes possible high - resolution selection pressure mapping . 
 it provides information about not only the selection pressure on individual sites but also how selection pressure at one site is affected by mutations on other sites . 
 it also includes datasets from other public databases , namely the stanford hiv database [ s. y. rhee , m. j. gonzales , r. kantor , b. j. betts , j. ravela and r. w. shafer ( 2003 ) nucleic acids res . , 31 , 298303 ] . 
 comparison between these datasets in the database enables cross - validation with independent datasets and also specific evaluation of the effect of drug treatment .