EasyGene -- a prokaryotic gene finder that ranks ORFs by statistical significance Background Contrary to other areas of sequence analysis , a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of random Open Reading Frames -LRB- ORFs -RRB- in prokaryotic genomes . Therefore , many genomes have too many short ORFs annotated as genes . Background As of February 2003 , 106 microbial genomes have been sequenced and made publicly available and the race is now on to mine genomes such as these for interesting and/or valuable genes and motifs . It has been estimated -LSB- 1 -RSB- that 60 -- 80 % of the genes in newly sequenced organisms have known homologues in other species . This percentage will grow as genomic annotations progress and perhaps there will be a time when virtually all genes can be found by homology matches to known proteins . That day , however , is not around the corner and even if it were , the occasional odd genes which would nevertheless escape detection by homology may very well be the truely novel and most wanted ones . It is a common misconception that identification of genes in prokaryotes is almost trivial . Any random sequence , as well as non-coding regions in real genomes , contain a large number of open reading frames -LRB- ORFs -RRB- . Most of these are too short to be possible protein coding genes , but in many genomes there are many ` random ' ORFs longer than e.g. 100 amino acids , a cut-off that is often used for considering an ORF a real gene . The large number of short ` random ' ORFs makes is difficult to discriminate real genes from random ORFs below a certain length , which depends on the genome and in particular its GC content . Therefore many genomes are over-annotated -LSB- 2 -RSB- . In one genome , that of A. pernix -LSB- 3 -RSB- , all ORFs longer than 100 amino acids are annotated as genes , but probably only around half the annotated genes are real -LSB- 2 -RSB- . The most severe problem today is to disciminate between short genes and random ORFs , and here the meaning of ` short ' is quite organism dependent . One of the most important contributions of this paper is a way to deal with this problem by introducing a statistical significance for an ORF being a gene . Computational gene finding exploits the statistical differences in codon usage between coding and non-coding regions of DNA -LSB- 4-6 -RSB- . The search for a mathematical frame work to efficiently capture these differences in codon usage led to Markov chain models and the GeneMark algorithm -LSB- 7 -RSB- . In order to facilitate the combination of various Markov chain scores , the application of Hidden Markov Models -LRB- HMMs -RRB- to gene finding was introduced in a gene finder for E. coli , Ecoparse -LSB- 8 -RSB- . These methods relied on a set of known genes for estimating parameters . More recently methods have been developed which start from a raw genome and automatically extract data for estimation . One of these , Glimmer -LSB- 9 -RSB- , employs interpolated Markov models in order to use the maximum Markov chain order which can reliably be estimated for every oligomer . Another one , Orpheus -LSB- 1 -RSB- , appeared the same year and calculates coding potentials of ORFs based on codon frequency of similarity-derived genes . Most of these gene finders also extract Shine-Dalgarno sequences in order to improve prediction of start codons . Due to their modular structure , HMMs are a suitable frame work for gene finding , and they are now used in GeneMark.hmm -LSB- 10 -RSB- , GeneMarkS -LSB- 11,12 -RSB- and Frame-by-Frame -LSB- 13 -RSB- . GeneMarkS uses a mixture of Markov chains of order 0 , 1 and 2 in combination with features of already annotated genomes to bootstrap an initial estimation of coding statistics , which is then iteratively improved by the GeneMark.hmm2 .1 algorithm . Gibbs sampling is also used to detect Ribosome Binding Sites -LRB- RBS -RRB- . The Frame-by-Frame method was conceived to improve the accuracy of GeneMark.hmm and it employs standard Viterbi parsing of all six reading frames independently and subsequently combines these into a global parse . In this paper , we describe a fully automated method for making an organism specific gene finder . It starts from a raw genome and searches for protein matches to get a good training set . Then an HMM with states for coding regions as well as RBS is estimated from the data set . This HMM is used to score all the ORFs in the genome and finally the score is converted to a measure of significance -- R -- which is the expected number of ORFs that would be predicted in one megabase of random DNA . The main advantage of this significance measure is that it takes the length distribution of random ORFs properly into account . The method is shown to match or exceed other gene finders currently available . Methods Automatic extraction of training set In order to fully automate the construction of the gene finder , a data set of reliable genes was obtained through the following procedure , which is essentially the same as that used in -LSB- 1 -RSB- . All ORFs with a significant match to a protein from a different organism are assumed to be real genes . A subset of those have only one possible start codon , because there is only one start between the most upstream protein match and the nearest upstream in-frame stop codon . The details of the method are : 1 . Extract the maximal ORFs longer than 120 bases from the query-genome . For every stop codon , extract the region from the first downstream -LRB- in frame -RRB- start codon to the next downstream -LRB- in frame -RRB- stop codon . 2 . Translate the ORFs to proteins and search for significant protein matches in Swiss-Prot -LSB- 14 -RSB- using BLASTP without gaps and a threshold of 10-5 -LSB- 15 -RSB- excluding proteins from the query-genome and proteins listed with one or more of the keywords putative , unknown , possible , hypothetical , probable , bacteriophage , transposon , insertion , reverse transcriptase . 3 . For each ORF with at least one significant protein match , identify the most upstream position of these matches -LRB- to compensate for random matches , we actually remove 6 bases from the most upstream match before proceeding -RRB- . If there is no alternative start codon between this position and the start of the ORF , put it in set A ' of genes with certain starts . The remaining ORFs are put in set B ' of certain genes with uncertain starts . 4 . Reduce similarity in set A ' by removing genes with similarity within the set . All pairs of genes are compared using BLASTN with a significance cut-off of 0.001 . Two genes that match are called neighbours . Genes are removed starting with the one having the largest number of neighbours . This continues until no gene has neighbours left in the set -LSB- 16 -RSB- . The reduced set is henceforth referred to as A. 5 . Unite set A ' and B ' into set T ' and reduce similarity of T ' to obtain set T . 6 . Add 50 bases of upstream flank to genes of all sets and 10 bases downstream flank . This procedure is a means to identify ORFs in a genome which are almost certain to be -LRB- protein coding -RRB- genes . The ORFs in set A and T make out reliable and balanced sets of positive examples which may be used to estimate the model parameters . HMM architecture We use a hidden Markov model -LRB- HMM -RRB- to model the gene structure . An HMM is a probabilistic model in which it is possible to model the various types of signals in a gene in one integrated model . For introductions to HMMs , see e.g. -LSB- 17-19 -RSB- . We model standard ` text book genes ' with an unbroken open reading frame , i.e. genes with no programmed frame shifts , no sequencing errors nor any other special feature obstructing the reading frame . The general architecture of the EasyGene HMM is shown in figure 1 . There is a begin and end state marked B and E respectively . Then , at each side of the gene model , there are null models to model everything that is not part of a gene nor lies in the immediate vicinity of a gene . The next submodel is the RBS model which models the RBS as well as the nucleotides between the RBS and the start codon . After the start codon we model 3 bases explicitly since it appears that the codon usage immediately downstream of start codons differs from the rest of the gene -LSB- 13 -RSB- . Similarly we model the last codon before the stop codon explicitly and 6 bases after the stop in order to capture information present around the stop codon . Figure 1 The overall HMM architecture . Each box corresponds to a submodel with more than one state . The number above the boxes indicates the number of bases modelled by the submodel . An ` X ' indicates a variable number . We employ 3 looped codon submodels of the interior of the gene as depicted on the right hand side of figure 2 . The reason for using several codon models is to embed a realistic length distribution in the HMM instead of the geometric distribution which would be implicit in having only one looped codon submodel -LSB- -LSB- 17 -RSB- , Section 3.4 -RSB- . We chose 3 because it results in a good fit to the empirical length distribution -LRB- see Results -RRB- . The states in the codon model are of 4th order -LSB- 20 -RSB- in order to capture the inter-codon dependencies . The three looped codon models are identical , i.e. they have the same emission and transition probabilities -LRB- the states are tied -RRB- . Figure 2 Enlargement of null model and internal looped codons . LEFT : The state structure of the NULL model . The background state is of third order and models the general composition of the genome . The three shadow states model coding regions on the complementary strand . There are transitions from the background state to the first RBS state and to the first state modelling the start codon . RIGHT : Details of model of internal codons . A codon is modelled by three states with a transition from the last state back to the first and one out of the codon model . By putting several codon models in series , the length distribution of coding regions can be captured . From the last state there is a transition to the first state of the ` BSTOP ' model , which models the last codon before the stop codon . The RBS model in figure 3 models the ribosome binding site as well as the nucleotides between the RBS and the start codon . It has 7 states to capture any ribosome binding patterns and 12 tied spacer states for modelling the region between the RBS and the start codon . From the first spacer state there are transitions to all but the last of the following states , so the length distribution -LRB- with a minimum of 3 and maximum of 12 -RRB- can be modelled exactly without imposing Gaussian assumptions as is done in e.g. -LSB- 21 -RSB- . These spacer states are of order zero . Figure 3 The state structure of the RBS model . The RBS model consists of seven states for modelling the ribosome binding site followed by a set of tied states for the variable region between the RBS and the start codon . From the last state there is a transition to the first of the three states modelling the start codon . The null model depicted on the left side of figure 2 has a third order state for capturing intergenic regions and a reverse codon model for modelling reverse genes -LRB- ` shadows ' -RRB- with states of second order . Note that transitions are allowed directly from the null model to a start codon ; this facilitates detection of genes inside operons which may not have a clear RBS . We found that the inclusion of two more branches of internal codons improved performance . This is presumably because it allows the HMM to keep separate statistics for atypical genes , some of which may be horizontally transfered . Adding a fourth branch did not improve performance further , so we stopped at three lending some support to the hypothesis that there are essentially three classes of genes in prokaryotic genomes -LSB- 22 -RSB- . Model estimation The HMM parameters -LRB- transition and emission probabilities -RRB- are estimated with the Baum-Welch algorithm , which is a maximum likelihood approach that finds the parameters maximizing the probability of the training set , see e.g. -LSB- 17 -RSB- . The estimation is done in these stages : 1 . The emission probabilities of the background state are estimated from both strands of the complete genome . 2 . The genes from start to stop codons are extracted from the training set and reverse complemented . The shadow model -LRB- consisting of three states -RRB- is estimated from these sequences . The parameters of this model are fixed . 3 . The RBS , start and astart submodels -LRB- cf. figure 1 -RRB- are estimated using set A exclusively since this set has reliable gene starts and therefore aligned upstream regions . Regions of 50 bases upstream from the start codon are extracted from all genes in set A . A null model -LRB- with fixed parameters -RRB- is included before the RBS model . The RBS model is initially primed with a high probability for the consensus GG -LRB- a dinucleotide common to most RBS -RRB- , but the precise pattern is found by the estimation method . During estimation , a type of simulated annealing is used , where noise of a decreasing amount is added to the parameters in each iteration of the Baum-Welch algorithm -LSB- 23 -RSB- . After training on set A , the parameters of the RBS , start and astart models are fixed . 4 . The null model , RBS , start and astart models are now combined with the internal codons , bstop , stop and astop models to make up the complete model . The non-fixed parameters of this model are then trained on the -LRB- larger -RRB- set T . The whole procedure can be completely automated . Note that no experimentally mapped RBSs are used for estimating the RBS model , the RBS is discovered during the estimation procedure . When estimating the complete model -LRB- stage 4 above -RRB- we use labelled estimation -LSB- 17,20 -RSB- , where each base of the sequence is labelled as coding or non-coding . For the part of set T where the exact start is not known , we leave the part of the sequence from the most upstream start codon until the first significant protein match unlabelled . The weight with which each base in the unlabelled region contributes to the estimation of the parameters in the coding states is automatically determined during the iterative estimation procedure . Decoding By decoding we mean the process of finding an optimal parse of the DNA string into coding and non-coding regions . The commonly used Viterbi decoding returns the most probable path of the sequence given the model -LSB- 17 -RSB- , but this is not appropriate when the length is modelled by duplicating codon states , since this length modelling is realized only as a sum over many HMM paths . Therefore we use posterior decoding where one calculates , for each nucleotide i , the probability that it was emitted by a given state S . The calculation is done by adding the probabilities of all paths compatible with having state S emit nucleotide i -LSB- 24,17 -RSB- . We use this to calculate the posterior probability of the first state of the start codon model , and thus obtain the probability that a gene starts at any position in the sequence . Given our assumption of perfect ` textbook-genes ' with no errors or frame shifts , there is exactly one stop codon for each start , and thus the probability of a gene start is equal to the probability of the whole gene . The independent scoring of start codons makes it trivial to report several possible start codons for a gene in cases where there is no clear `` winner '' . Note that the HMM architecture in figure 1 is non-looped -- ie . it would find only one gene if we were using Viterbi decoding , which only gives the single most likely parse . This architecture however , is the correct one for scoring ORFs with posterior decoding . It has the further advantage that overlapping genes are easily handled since each gene start will be scored independently whether or not it overlaps other genes . In contrast , using a looped model and Viterbi decoding would not facilitate detection of overlapping genes unless the model contains explicit states for overlapping genes as described in e.g. -LSB- 8 -RSB- . The state posterior probability itself is not a useful score , because it is a probability of the whole sequence , not just a single gene , and it therefore depends on the length of the sequence it is part of . By dividing the posterior probability by the probability of the whole sequence -LRB- the genome -RRB- according to the null model , the contribution to the state posterior probability of the sequences flanking a gene will cancel and effectively make the ratio independent of the flanking sequences -LRB- except the parts very close to the gene -RRB- , see the Appendix . The log of this ratio is called the log-odds score , and that is our basic score for a gene . Significance As mentioned above , it is important to take into account the chance that a random ORF of the same length scores as high as a given gene . This is implicitly taken into account by our HMM because it models the length distribution of genes , but it turns out that one can calculate a significance score , which works slightly better -LRB- se results -RRB- and has a more intuitive interpretation . The probability of finding high-scoring ORFs in a random sequence is highly length dependent ; the number of ORFs decays exponentially with the length so there will be a lot more short ORFs than long ones . For a random sequence of given length -LRB- e.g. 1 Mb -RRB- the expected number of ORFs of length l' can be written as N -LRB- l' -RRB- = exp -LRB- A - Bl ' -RRB- , -LRB- 1 -RRB- where A and B are constants that can be found from linear regression of the log of the number of ORFs against the length . All lengths are measured in codons and we count both start and stop codon . For convenience we introduce the variable length l' , which is the length of the ORF modelled by the looped codon submodels . A number of codons l0 are modelled explicitly in the beginning and end of the ORF -LRB- l0 = 4 in our model -RRB- , so l' = l - l0 . If the log-odds score is denoted β , we show in the appendix that β ' = β - -LRB- n - 1 -RRB- log -LRB- l' - n/2 -RRB- is approximately normally distributed with a mean αμ + γμ l' and variance ασ + γσ l' , which are both linear in l' -LRB- the α 's and γ 's are constants -RRB- . Here n is the number of looped codon submodels -LRB- 3 in our model -RRB- . The coefficients of the linear mean and variance are estimated by linear regression on ORFs from random sequences . Then we define the standard score which is normally distributed with mean 0 and variance 1 . For any given length l' and standard score Γ , one can now estimate the expected number C -LRB- l' , Γ -RRB- of ORFs of the same length scoring higher than Γ in a fixed -LRB- long -RRB- random sequence using equation -LRB- 1 -RRB- , C -LRB- l' , Γ -RRB- = exp -LRB- A - Bl ' -RRB- -LSB- 1 - Φ -LRB- Γ -RRB- -RSB- , -LRB- 3 -RRB- where Φ is the cumulative normal distribution . This number can be used directly to judge the significance of a gene predicted with length l and a standard score Γ . However , it would be preferable to know the total number of expected genes -LRB- of all lengths -RRB- predicted in a random sequence , rather than the expected number with a certain length . Therefore , instead of using C -LRB- l' , Γ -RRB- to judge significance , we use the total number of expected predictions in a random sequence . Suppose a gene of length l1 is predicted with a standard score Γ1 . Then the expected number of genes of that length predicted in a random sequence is C = C -LRB- , Γ1 -RRB- . Now we want to calculate the total number of genes of all lengths predicted in a random sequence at the threshold C. For any length above a certain lC , the expected number of ORFs will fall below C due to the exponential decay of the length distribution . Therefore the total number of predicted genes -LRB- regardless of length -RRB- is roughly lC C + the sum of predicted genes above lC -LRB- a sum of a geometric series -RRB- . We end up with the following expression for the total number of predicted genes in a random sequence : The number 16 arises from the fact that the minimum ORF length considered is 20 codons corresponding to a variable length of 20 - 4 = 16 . See Appendix for details . This is the expected number of genes predicted in a random sequence for a given value of C , and that number is the one we quote for each predicted gene . The constants A and B characterize the random sequence . R depends on the standard score Γ through C . This dependence is illustated in Figure 4 . It clearly shows that a short ORF needs to have a much higher standard score than a long ORF in order to be significant -LRB- in the sense of having a low R-value -RRB- . Figure 4 Relationship between R , Γ and variable length in codons l' . The numbers are taken from the E. coli runs described in Results and Discussion , but the qualitative behavior is independent of the genome We have chosen to normalize R to a random sequence of 1 Mb rather than a random sequence the length of the genome , because then significance can be compared across genomes . The precise recipe we use for calculation of statistical significance is : • For the genome in question , generate 40 Mb of random sequence using the 3rd order background model -LRB- estimated from the genome -RRB- . Extract all ORFs and estimate the parameters A and B by linear regression of the log of the number of ORFs against the length and normalize to 1 Mb . To avoid distortion of regression lines due to noisy statistics of long ORFs , restrict the variable ORF length used to lie below 70 codons . This makes the range of variable length 16 -- 70 codons , which corresponds to a total length range of 20 -- 74 codons . • Score all the ORFs in the random sequence with the model and calculate β ' for all ORFs . For each length , calculate the average and variance and estimate the parameters αμ , γμ , ασ , and γσ by linear regression , again using ORFs in the range 20 -- 74 codons . • To calculate the significance of an ORF in the genome , first calculate the standard score Γ from equation 2 , then C from equation 3 , and finally the significance value R using equation 4 . There are of course other possible choices of significance measure , but we believe that this is a simple and intuitively clear one and we have prefered it to the more traditional significance measure . By reporting the number R of expected false positives in one megabase of random sequence , it is easy for the end-user to estimate the number of false positives in a random sequence the length of the entire query genome -- one simply has to multiply R with the size of the genome measured in megabases . Using other gene finders In order to benchmark EasyGene we compare it with some of the existing gene finders . GeneMark 2.4 , Frame-by-Frame , GeneMark.hmm 2.1 and GeneMark.hmm / S all belong to the GeneMark suit of programs and are accessible via the web interfaces listed in the references . For GeneMark.hmm2 .1 , GeneMark.hmm / S and Frame-by-Frame the output is a coordinate listing -LRB- start and stop positions -RRB- of all predicted genes . GeneMark2 .4 outputs a list of stop codons and corresponding high-scoring start codons . Each start/stop is listed with scores for coding potential and RBS . We collect all starts for a given stop and output the `` Avg Prob '' of the start with the highest RBS score . Whenever a threshold was needed for comparison purposes , we used 0.5 which is the default set on the web page . Glimmer2 .02 and Orpheus2 were installed locally . We changed the minimum ORF length predicted by these gene finders to 60 bp which seems to be the minimum used by the other gene finders . Orpheus and Glimmer provide two kinds of output : a verbose coordinate list of starts , stops and ORF scores and a simpler , post-processed list of coordinates for ORFs regarded as genes . In order to test their ORF scoring we had to parse the scored output . We had some difficulties interpreting the scored Orpheus output since some ORFs were scored several times with identical results -LRB- several identical `` Start chosen '' -RRB- . In cases where multiple copies were found , we simply chose one of them and used the corresponding `` Coding potential '' -LRB- with the recommended threshold of -1 -RRB- for further analysis . For Glimmer2 .02 the scored output was parsed simply by selecting the Gene Score attributed to every scored ORF and using the recommended threshold of 90 . Finally , we used RBSfinder -LSB- 25 -RSB- for an alternative post-processing of Glimmer2 .02 output . RBSfinder is designed to look for RBS sequences upstream of genes predicted by Glimmer2 .02 . If there are no RBS patterns in this region , RBSfinder searches for a start codon having a RBS pattern in the same reading frame upstream or downstream and relocates the start codon accordingly . The program may be iterated several times using revised predictions as new inputs . We found that running it twice was better than once but running it three times did not improve things further , so we chose to run it twice -LRB- with default options -RRB- . Results and Discussion A number of tests were conducted in order to optimize the model architecture . We tested the number of codon models in series and found that three models yield a very good fit to the observed length distribution , see figure 5 for an example . Figure 5 Gene length distribution imposed by HMM architecture . The model length distribution given by a negative binomial -LRB- equation 8 with n = 3 -RRB- compared to the length histogram of set A genes for H. pylori J99 . The results of our experiments with the number of branches in the coding model and the order of the coding states are shown in figure 6 and 7 respectively , which show -LRB- cross-validated -RRB- performance curves -LRB- ROC curves -RRB- for varying numbers of branches and various orders . The performance curves are made by plotting the average true positive rates for a range of average false positive rates -LRB- the fraction of false positives made on average by the 10 different cross-validation models in 1 Mb of random sequence -RRB- . Figure 6 Assessing the optimal number of HMM coding branches . Performance curves for 1,2,3 and 4 Markov branches of looped codon submodels for E. coli . The performance curves are made by the following procedure : First we sort the positive R-values in ascending order for each of the 10 subsets of set T -LRB- test sets -RRB- . Then for each ascending R-value we calculate the fraction of genes in set T scoring below R -LRB- true positive rate -RRB- and the fraction of ORFs -LRB- with lengths greater than or equal to 20 codons -RRB- in one megabase double-stranded sequence scoring below R -LRB- false positive rate -RRB- . The resulting 10 files with true and false positive rates are concatenated and 30 false positive cutoffs are selected -LRB- from 0 to 0.15 with steps of 0.005 -RRB- . The false positive entries in the 10 files which fall between these cutoffs are found and the corresponding true positive entries are averaged . Hence for each average false positive rate -LRB- halfway between two consecutive false positive cutoffs -RRB- we associate an average true positive rate and these tuples are then plotted . Figure 7 Assessing the optimal order of looped codon states . Performance curves for 3rd , 4th and 5th order Markov states of looped codon submodels for E. coli . For explanation of the construction of performance curves please confer the caption of figure 6 . Figure 6 indicates that 3 branches is the best choice , while figure 7 suggests that the optimal order for these branches is 4 . Note that the Y-axes of these figures have been zoomed in order to allow visual inspection of the performance differences . Note also that all architectures and orders in fact yield a relatively high true positive rate even at false positive rates below 0.02 . Similar figures were also made for other organisms -LRB- not shown -RRB- and although the results were not always as clear as for E. coli , a choice of 3 branches of 4th order states as default models of the internal parts of genes works well for the organisms we tested . Similarly , figure 8 shows performance curves to compare log-odds and significance scores and the significance scoring is seen to be slightly better in that it allows detection of more true positives for a given false positive rate . The significance scoring has the additional advantage of being genome independent and has an intuitively appealing interpretation . Figure 8 Comparing significance and log-odds . Performance curves comparing significance and log-odds scores for E. coli . For explanation of the construction of performance curves please confer the caption of figure 6 . Table 1 shows the average true and false positive rates for selected R-values in the case of E. coli models with three branches and fourth order branch states . This table gives an impression of the approximate R values corresponding to the graphs in figures 6 , 7 and 8 . Table 1 True and false positive rates for selected R-values . R-value TP rate FP rate 0.1 0.971 0 2 0.980 2.3e-6 10 0.984 3.0e-5 50 0.987 3.1e-4 150 0.991 8.7e-4 500 0.995 2.8e-3 10000 0.999 0.059 True and false positive rates averaged over 10 cross-validations for selected R-values in the case of the three branches , fourth order E. coli model . The FP rate is measured as the average fraction of ORFs -LRB- with lengths greater than or equal to 20 codons -RRB- in one megabase of double-stranded random sequence scoring lower than the given R-value . The TP rate is measured as the average fraction of ORFs in data set T scoring lower than the given R-value In order to test the validity of the approximations used in the derivation of the significance measure , we generated 1 Mb of random sequence from the Markov chain corresponding to the background state of the E. coli model . The plots in Figure 9 show the mean and variance of the length-corrected log-odds score , β ' , for each length . They are very close to being linear as we assumed . The length distribution of the ORFs in the same random sequence is also shown , which confirm the geometric length distribution . The predicted number of significant ORFs in 1 Mb random sequence is compared with the theoretical significance value R in Figure 10 and the agreement is seen to be rather good . We also compared the distribution of standard -LRB- 3 -RRB- scores for ORFs in the same random sequences to a normal distribution of unit variance and zero mean and the agreement also turned out to be good in this case as seen in Figure 11 . We conclude that the assumptions and approximations used in the calculation of R are quite accurate . Figure 9 Statistical characteristics of random sequences . The top two panels show the mean and variance of log-odds scores versus variable ORF length in random sequences -LRB- E. coli model -RRB- . Lowest subplot shows a logarithmic plot of the length distribution of random ORFs . The linear regression lines are shown in all three plots . Figure 10 Comparing predicted and found number of false positives . Empirical and theoretical number of false positives per Mb double-stranded random sequence according to the E. coli model . Figure 11 Probability density functions for the standard score . Empirical -LRB- dots -RRB- and theoretical -LRB- line -RRB- probability density functions for the standard scores -LRB- Γ -RRB- in random sequences -LRB- E. coli model -RRB- . The lower plot is an enlargement of the distribution tail . Table 2 shows the percentage of genes found for eight different gene finders and some sets of high-confidence genes from E. coli as well as the number of genes found in the whole genome and in random sequences . The eight gene finders are : EasyGene , Glimmer2 .02 , Glimmer2 .02 with RBSfinder post-processing , Orpheus , GeneMark2 .4 , GeneMark.hmm2 .1 / GeneMarkS hybrid , `` pure '' GeneMark.hmm2 .1 and Frame-by-Frame . Table 2 Specificity , sensitivity and precision estimates for different gene finders in E. coli . Data set EasyGene Glim rbs-Glim Orpheus Gm24 GmS Gmhmm Frame A ' - % found 98.4 98.9 / 98.9 98.9 98.0 / 95.3 91.5 97.2 98.1 97.0 A ' - % exact 93.8 98.9 / 95.3 84.1 95.1 / 92.4 41.6 88.0 85.7 93.2 B ' - % found 98.4 98.5 / 98.6 98.6 95.9 / 96.5 90.2 96.6 97.2 96.4 T - % found 98.1 -LRB- 98.0 -RRB- 98.3 / 98.4 98.4 96.5 / 95.6 89.8 96.3 97.1 96.1 Genome 4145 6827/5756 5756 9333/7543 3552 4064 4230 4064 zero order 7 169/211 211 6761/5430 6 153 1459 0 first order 7 545/723 723 6836/4804 13 241 830 0 third order 1 2423/2694 2694 6582/4817 43 659 866 1 shadows 0 19/21 21 22/9 1 0 2 0 Upper part shows the percentage of genes found exactly -LRB- both 5 ' and 3 ' end -RRB- and partially -LRB- only 3 ' end exact -RRB- for different gene finders and sets of high confidence genes in E. coli . For Glimmer and Orpheus , the numbers before the '' / '' are based exclusively on their ORF scores and recommended threshold whereas the numbers after the '' / '' are based on their post-processing procedures . The number of genes predicted in the whole genome is also shown . This should be compared to the 4288 annotated genes in E. coli . The lower part of the table shows the number of false positives predicted in random sequences generated by Markov chains of order 0 , 1 and 3 and the very last row shows the number of false predictions in the shadows of the high-confidence genes in data set A. All values listed for EasyGene are based on an R-value threshold of R = 2 . Sequence set A ' consist of the 1136 genes with high-confidence starts extracted from the E. coli genome as described in Methods . The '' % exact '' row indicates the percentage of predictions where both start and stop codon are correctly predicted whereas the '' % found '' row indicates that only the stop codon is correct . Note that all genes in set A have the most upstream start -LRB- they are Longest Possible Open Reading Frames -- LPORFs -RRB- by construction , and hence performance on this set favours gene finders which are biased towards LPORFs -LRB- such as Glimmer -RRB- . Set B ' consists of 1690 high-confidence genes with uncertain starts extracted as described in Methods . Due to the uncertainty of start codon placement , one can not evaluate the exact start prediction performance for this set . The same is true for set T which is a similarity reduced union of A ' and B ' -LRB- 2042 sequences -RRB- . The Genome row shows the number of genes predicted in the E. coli genome using default parameters and thresholds for the various gene finders . For Glimmer and Orpheus the minimum length of predicted genes was lowered to 60 in order to make their performance comparable to the others ' . The next three rows show the number of false positives found in both strands of 1 Mb random sequences generated by zero , first and third order Markov chains estimated from the entire E. coli genome . The last row of the table shows the number of predictions wholly within the shadows of set A ' -- i.e. wholly within regions complementary to the genes in set A ' where , ideally , no genes -LRB- or at least very few -RRB- should be predicted . The test sets overlap with the training sets for EasyGene . Therefore , for set T the 10-fold cross validation sensitivity is shown in parenthesis for EasyGene and it is seen to be reassuringly close to the non-crossvalidated sensitivity suggesting that EasyGene employs an appropriate model complexity and steers free of overfitting . Note also that the other gene finders have also been estimated from sets that overlap -LRB- or even contain -RRB- set T . For Orpheus and Glimmer we show two numbers N1/N2 for each entry corresponding to before and after post-processing . For Orpheus , N1 is the number of unique ORFs having a Coding Potential above the recommended minimum of -1 and N2 is the number of entries in the post-processed orfnuc file . The post-processing removes some overlaps but also appears to employ a less restrictive cutoff than the recommended -1 . For Glimmer , N1 is the number of ORFs with Gene Scores greater than or equal to the recommended threshold of 90 and N2 is the number of entries in the post-processed list of putative genes . The post-processing elects -LRB- `` votes '' -RRB- some ORFs as gene candidates despite a low score . On the other hand the post-procesing removes some same-strand overlaps in different reading frames so the N2 may be greater or less than N1 depending on the relative extent of overlaps and `` voting '' . It is always difficult to asses the specificity of a gene finder , because it is difficult to find genomic regions that are certain to contain no genes . We have therefore assessed specificity in three different ways . First , by counting the number of predicted genes in a genome . If this number is much higher than the number of annotated genes , it is likely that there are many false positive predictions , i.e. poor specificity . Our second test is based on random sequence . Clearly , a high number of predicted genes in a random sequence of bases indicates a poor specificity . However , it is probably not possible to find an exact quantitative correspondence between predictions in random sequences and real genomes . Also , it is not clear what sort of random sequence to use for such a test . By 0 ` th order we mean a sequence with bases generated randomly and independently with the base frequencies of the genome . Bases are quite correlated in DNA sequences , so we have also tested on sequences that are generated by Markov chains of orders 1 and 3 . These Markov chains are estimated from the genome , so the sequences will have the same distribution of dinucleotides and 4-nucleotides , respectively , as the genome . Finally , the third test is the number of genes predicted on the opposite strand of genes -LRB- shadows -RRB- ; these shadow regions should contain very few genes if any . All gene finders except EasyGene , Frame and GeneMark2 .4 predict a rather large number of false positives in random sequences , but for GeneMark.hmm and GeneMarkS we do not see large over-prediction in the genome or in shadows . Evidently , Glimmer and Orpheus predict a lot more genes in the genome than the other gene finders , suggesting that these gene finders have very high false positive levels . This is supported by the high numbers of genes predicted in random sequences , and -LRB- to a much lesser extent -RRB- in shadows . Orpheus and Glimmer actually predict more genes pr . nucleotide in the third order random sequence than they do in the genome , suggesting that the coding potential calculated in these gene finders is far from optimal . The HMM used by Frame assumes a minimum gene length of 69 bases which could make its false positive level seem somewhat better -LRB- lower -RRB- than it is , but there was no convenient way to lower the minimum length so we simply left it . It should also be noted that Frame relies on pre-existing annotations for training and is therefore not a self-training gene finder like Glimmer , Orpheus , GeneMarkS and Easygene . The sensitivity of the gene finders is tested on sets of high-confidence genes . Glimmer has the highest sensitivity for all sets , but this is largely due to heavy over-prediction . One ought always to bear in mind that it is not difficult to achieve high sensitivity if high levels of false predictions are tolerated at the same time -- sensitivity is 100 % if all ORFs are predicted as genes ! Although there are some very close competitors , EasyGene comes out as the second best in sensitivity for all sets . The exact prediction of start codons is tested on set A ' and on an experimentally ver-ified set . As mentioned above , set A is biased , because all genes of this set are LPORFs . Glimmer always predicts the most upstream start and consequently achieves a high performance on this set . When Glimmer 's output is post-processed by RBSfinder the performance drops considerably . The prediction of start codons was evaluated further on a set of 195 E. coli genes with experimentally verified starts -LSB- 26 -RSB- with results shown in table 3 . Set LiC is the subset of 133 genes which coincide with the longest possible open reading frame -LRB- LPORF -RRB- while set LiD is the remaining 62 genes whose starts are downstream of the LPORF start . The goal is to find the starts of the challenging LiD set without loosing too many of the more trivial starts of set LiC . Table 3 shows that while most gene finders partially locate all genes in set LiC and LiD , there are large variations in their exact localization ability . Selecting for the highest combined performance on set LiC and LiD , one sees that EasyGene , GeneMarkS and Frame-by-Frame are best . Their performances also exceed that of -LSB- 27 -RSB- in which a cross-validated performance of 84.9 % + / - 4 % is reported on a subset of 184 genes out of the 195 . In the low end we have Glimmer finding 0 % of set LiD exact -LRB- 1.6 % with post-processing -RRB- and GeneMark2 .4 finding 49.6 % of set LiC exact . Using the RBSfinder post-processing -LSB- 25 -RSB- on the Glimmer predictions improves performance on set LiD to 75.8 % , but at the cost of a substantial dip in set LiC performance to 88.7 % . Table 3 Sensitivity and precision estimates for experimentally verified E. coli genes . Data set EasyGene Glim rbs-Glim Orpheus Gm24 GmS Gmhmm Frame LiC - % found 100 100/100 100 97.7 / 91.7 97.7 100 100 100 LiC - % exact 94.0 100/97 .0 88.7 96.2 / 90.2 49.6 94.0 90.2 98.5 LiD - % found 100 100/100 100 96.8 / 98.4 100 100 100 100 LiD - % exact 96.8 0/1 .6 75.8 51.5 / 51.6 67.7 95.2 80.6 87.0 Percentage of genes found exactly and partially in two subsets of the 195 experimentally verified genes published by -LSB- 26 -RSB- . All values listed for EasyGene are based on an R-value threshold of R = 2 . Many gene finders are first developed for E. coli and then later adapted to other organisms . It is therefore important also to test gene finders on other organisms . Based on table 2 and 3 we chose EasyGene , GeneMarkS and Frame-by-Frame as the gene finders with the best overall performances and then conducted further comparisons between these for M. tuberculosis -LSB- 28 -RSB- , H. pylori -LSB- 29 -RSB- and B. subtilis -LSB- 30 -RSB- . M. tuberculosis presents a challenge due to GC richness , H. pylori due to small genome size and B. subtilis was chosen on account of its reputation of being well annotated -LSB- 11 -RSB- . The results are presented in table 4 with the same rows as table 2 . Table 4 Specificity , sensitivity and precision estimates for M. tuberculosis , H. pylori J99 and B. subtilis . M. tuberculosis Data set EasyGene GmS Frame A ' - % found 96.7 97.2 96.0 A ' - % exact 89.1 80.9 87.9 B ' - % found 96.8 97.1 96.3 T - % found 96.9 -LRB- 96.6 -RRB- 97.3 96.4 Genome 3749 3983 4341 zero order 0 - 8 first order 3 - 2 third order 2 - 12 shadows 1 0 1 H. pylori J99 Data set EasyGene GmS Frame A ' - % found 99.2 99.2 99.2 A ' - % exact 97.5 95.7 96.7 B ' - % found 100 99.6 98.9 T - % found 99.7 -LRB- 98.8 -RRB- 99.5 99.1 Genome 1491 1518 1479 zero order 2 1479 2 first order 1 336 2 third order 0 403 0 shadows 2 0 0 B. subtilis Data set EasyGene GmS Frame A ' - % found 99.3 98.1 98.8 A ' - % exacts 94.8 94.1 93.3 B ' - % found 99.2 99.0 98.2 T - % found 99.3 -LRB- 99.2 -RRB- 99.0 98.4 Genome 4083 4221 4006 zero order 1 675 0 first order 2 457 0 third order 1 813 2 shadows 0 0 0 Genes found exactly and partially for different gene finders and sets of high confidence genes in M. tuberculosis , H. pylori J99 and B. subtilis , where the number of annotated genes is 3918 , 1491 and 4100 respectively . There are no pre-trained GeneMarkS models for M. tuberculosis , so it was not possible to obtain a false positive estimate for this organism . All values listed for EasyGene are based on an R-value threshold of R = 2 . For M. tuberculosis GeneMarkS and EasyGene are comparable , although GeneMarkS seems to over-predict slightly -LRB- assuming that the 3918 annotated genes are close to being correct -RRB- , and Easygene might under-predict . We believe that Frame predicts too many genes in this organism and at the same time it has lower sensitivity than the two others , suggesting a worse performance overall . This indicates that Frame is not very robust with respect to high GC content . The small dataset from H. pylori might give a slight over-fitting in EasyGene , where there is one percent difference between cross-validated results and non-crossvalidated . For this organism the three gene finders seem to have very similar performances . Finally , for B. subtilis EasyGene comes closest to the number of annotated genes and have higher sensitivity than the other methods . Conclusions The emerging overall picture is that the sensitivity of EasyGene tends to be comparable to GeneMarkS and higher than Frame . With regards to specificity , EasyGene and Frame tend to be comparable and both higher than GeneMarkS . Hence , EasyGene comes out with the best combined sensitivity/specificity performance . When it comes to exact starts , EasyGene also generally performs best . Glimmer and Orpheus have good sensitivities but at the cost of very low specificities in this comparison . We have lowered the ORF length cutoff from their default values in these methods to make the results comparable . This may be unfair , but since the challenge is to find the short genes , we believe that any gene finder should be able to score them . At present it is not possible to automatically find all genes in a prokaryotic genome . We believe the aim of a gene finding system is to help expert annotators as much as possible , and we consider the statistical significance of a gene an important help in classifying the predictions into almost certain genes and border-line genes needing more attention . Contrary to most other gene finders discussed here , it is up to the user which significance cut-off to use . EasyGene also predicts sub-optimal start codons if need be , so it will be easy to see if e.g. two alternative starts have almost the same score . A shortcoming of the significance value as calculated here is that long ORFs score well simply on account of their length , since very long ORFs occur rarely in random sequences . For this reason , EasyGene also provides a log-odds score in the output which may be held up against the R-value for ORFs longer than say 500 bp . Genes of this length ought to have high log-odds values . If they do not -- i.e. if they score below 0 -- then they are probably not real genes despite their length -LRB- sometimes very long non-coding ORFs occur in regions of repetitive DNA -RRB- . If the amount of available genomic DNA is very small -LRB- as it may be in partially sequenced genomes -RRB- the 3 branches of 4th order coding models may have too many parameters to be reliably estimated . In such cases , one could reduce the parameter space simply by using fewer branches and/or lower orders . More generally , one could develop a method for automatic fine-tuning of HMM architecture for every new organism . Other lines of future research could focus on modelling of genes with errors and frame shifts . Finally , it may be noted that a prototype of EasyGene has already been used in the annotation of S. typhi -LSB- 31 -RSB- . Appendix : The length dependent score distribution The log-odds score distribution has several components . The probability of a sequence z containing and ORF given a model M may be written as where c1 , ... , cl denotes the codons in the ORF and flanks denotes all the rest of the sequence . P -LRB- l | M -RRB- is the length distribution of the HMM . In logarithmic form we have Similarly , the denominator of the log-odds score , log P -LRB- z | N -RRB- , may be rewritten in exactly the same way , so the entire log-odds score becomes where We will now look at the distribution of each of these terms . The null model consists of a state with a loop and three reverse codon states with a loop . For a long sequence one of the loops will usually dominate the probability , so the length distribution is well approximated by a geometric distribution P -LRB- l | N -RRB- k1 exp -LRB- - k1l -RRB- where k1 is a constant greater than zero . The length distribution from the looped codon states is a negative binomial -LSB- 17 -RSB- , where p is loop probability and n is the number of looped codon submodels -LRB- in casu , n = 3 -RRB- . Some of the ORF -LRB- start and end -RRB- is modelled by non-looped states . The number of nucleotides modelled in the beginning and end of the ORF is denoted l0 and the variable length is denoted l' , so the total ORF length is l = l0 + l' . Using that log -LRB- n - 1 -RRB- log -LRB- l' - n/2 -RRB- - log -LRB- n-1 -RRB- ! for l' >> n , we obtain The next term , Q , in equation 6 is a sum of random variables since we are considering random sequences . The sum Q will therefore -LRB- according to the central limit theorem -RRB- converge to a normal distribution N -LRB- μl , σ -RRB- , for large l . The submodels flanking the gene model are identical to the null model . Therefore p -LRB- flanks | M -RRB- and p -LRB- flanks | N -RRB- will almost cancel in the last term of equation 6 , except from the contribution from the RBS model and the states after the stop codon . This contribution will be independent of the ORF length . Since it is again a sum of random terms it is well approximated by a normal distribution . Apart from the non-linear term from the negative binomial we have now shown that all terms in equation 6 are constant or scale linearly with l. Therefore is normally distributed for fixed length with an average and variance that are linear functions of l : average = αμ + γμl ' -LRB- 11 -RRB- The parameters of these linear functions can readily be estimated from random sequences by doing linear regression between the variable length and the mean and variance of log-odds scores as shown in the two upper plots of figure 9 . -LRB- Note that one can switch from the variable length l' to the total length l in the formulas by changing the constants . -RRB- Finally it is convenient to introduce the standard score Γ , defined in equation 2 , which is normally distributed with average 0 and variation 1 . Since random ORF lengths are geometrically distributed -LRB- cf. equation 1 -RRB- , the expected number of ORFs of length l scoring more than Γ in a sequence is C -LRB- l' , Γ -RRB- = exp -LRB- A - Bl ' -RRB- -LSB- 1 - Φ -LRB- Γ -RRB- -RSB- , -LRB- 13 -RRB- where the exponential term is the expected number of ORFs of variable length l' and Φ is the cumulative normal distribution . Thus , for a given score we can calculate the number of expected predictions in a random sequence . For lengths l ≥ lC this expectation is always less than C -LRB- due to the exponential factor -RRB- . So if we require that no more than C predictions be made at any length , the total number of predictions in a random genome is where ls is the shortest ORF length considered -LRB- we use 20 -RRB- . lC is given by Strictly speaking , we should take the smallest integer larger than , but using the real expression introduces only an insignificant error . Inserting into equation 14 finally yields equation 4 , R = -LCB- -LRB- A - log C -RRB- / B - -LRB- ls - l0 -RRB- -RCB- C + C / -LRB- 1 - exp -LRB- - B -RRB- -RRB- . -LRB- 16 -RRB- Web sites used Frame-by-Frame : GeneMark.hmm 2.1 : GeneMark.hmm 2.1 using GeneMarkS models : GeneMark 2.4 :

O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

computational gene finding
statistical significance
hidden markov model

short open reading frames
automated genome annotation


Database of neurodegenerative disorders A neurological disorder is a disorder caused by the deterioration of certain nerve cells called neurons . Changes in these cells cause them to function abnormally , eventually bringing about their death . In this paper we present a comprehensive database for neurodegenerative diseases , a first-of-its kind covering all known or suspected genes , proteins , pathways related to neurodegenerative diseases . This dynamically compiled database allows researchers to link neurological disorders to the candidate genes & proteins . It serves as a tool to navigate potential gene-protein-pathway relationships in the context of neurodegenerative diseases . The neurodegenerative disorder database covers more then 100 disease concepts including synonyms and research topics . The current version of the database provides links to 728 abstracts and over 203 unique genes/proteins with 137 drugs . Also it is integrated well with other related databases . The aim of this database is to provide the researcher with a quick overview of potential links between genes and proteins with related neurodegenerative diseases . Thus DND providing a user-friendly interface is designed as a source to enhance research on neurodegenerative disorders . Background Neurodegenerative disease leads to neurodegeneration and disabilities as a result of deterioration of neurons . -LSB- 1 -RSB- Progressive loss of motor , sensory neurons and the ability of the mind to refer sensory information to an external object is affected in different kinds of neurological disorders . -LSB- 2 -RSB- Mutations in the genes identified for these disorders leads to accumulation of misfolded protein resulting in protein aggregation and intracellular inclusions . -LSB- 3 -RSB- The knowledge gained from the genetic studies of many neurodegenerative disorders explains the disease mechanism that includes few pathways leading to neuronal death of cells . -LSB- 4 -RSB- Amyloid precursor proteins -LRB- APP -RRB- , SNCA , Parkin , UCHL1 , NR4A2 , DJ1 , PINK1 and LRRK2 are the genetic causes of some of the primary neurodegenerative disorders like Alzheimer 's disease , Parkinson 's disease , Huntington 's disease , amyotrophic lateral sclerosis and prion diseases . -LSB- 5 -RSB- The treatment strategies for these disorders include therapies and drugs . Because of the complexity involved in the neurodegenerative disease mechanism and treatment strategies , it is of interest to collate different Neurodegenerative Disorders in the form of a database . Hence we developed DND -LRB- Database of Neurodegenerative Disorders -RRB- , an on-line web based database that contains more than 100 neuro related disease concepts and provides with a covering of all related genes , proteins , pathways and drug information . Methodology Database model and content Database of Neurodegenerative Disorders -LRB- DND -RRB- is developed as an open source software system using Mysql - 5.0.18 - Win32 -LSB- 6 -RSB- and PHP - 5.2.0 . -LSB- 7 -RSB- DND uses the relational data model . A schema diagram describing the DND is shown in the -LSB- figure 1 -RSB- . MySQL tables are shown as rectangles . Mandatory attributes are in bold , optional are in italics . The relationships between tables in the database are shown as connecting lines . The public databases NCBI , SwissProt , Kegg and DrugBank are used for populating the database . The gene sequences related to various neurological disorders are obtained from NCBI . Both gene sequence and coding sequence can be obtained in Fasta format . Also HGNC links for each entry is provided . All protein sequences are extracted from UniProt , and fields that mapped to PDB , Pfam , Interpro , Prints and Smart are parsed and stored . Links from DND to these source databases are also provided for each entry . Fasta format of protein sequences too can be obtained . Pathway information for Neurodegenerative diseases as well as known neuro related drug compounds in KEGG is searched and the results obtained are incorporated in to the database . The articles are chosen to represent a diverse selection of reports on major Neurodegenerative diseases and corresponding Pubmed ID is provided for all the entries . Neurological Associations and Organizations interested in neuroscience-related fields are tabulated with links to their corresponding web pages . Searching DND There are three ways by which the user can query the database . The first one is the keyword search in home page that can be performed by giving keywords like Disease name , Gene name and Drug name to retrieve the required data . The second one is the advanced search option for specific requirements with two main entry points namely gene/protein and drug/drug properties . Gene/protein include search options like Gene name , Gene id , Omim , Hgnc , Pubmed , Swissprot id , Pdb , Interpro , Pfam , Smart and Prints . The second entry point Drug/drug properties include Drug name , Kegg id , DrugBank id , Pubchem , and IUPAC name and Molecular formula . Alternatively , the users can also browse the database via the seven entry points namely browse by Diseases , Genes , Proteins , Drugs , Pathways , Related Articles and Organizations . Utility The current version of the DND is comprehensive with enormous data related to every aspect of neurological disorders , providing public access to sequence , genetics , structural , and bibliographic information . Structures of drugs can be viewed using the browser plug in Chime -LSB- 8 -RSB- that allows chemical structures to rotate , reformat , and save in various file formats . A glossary describing the terms used in database is also provided to help the end users . We believe that DND will assist the intended neurological researchers in understanding of fundamental molecular and genetic processes that control various neurodegenerative diseases .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

neurodegenerative disorder
neurological disorders


Single-day therapy : an expert opinion on a recent development for the episodic treatment of recurrent genital herpes One common method for treating recurrent genital herpes outbreaks is 3 -- 5 day episodic therapy with nucleoside analogues . However , since maximum viral replication occurs within 24 h after the onset of symptoms , short-term patient-initiated episodic therapy started at prodromal onset or at the first appearance of lesions in patients without a prodrome may represent an important option . In a recent randomized trial , single-day famciclovir treatment decreased lesion healing time and the duration of pain and other symptoms by approximately 2 days compared to placebo , and prevented progression to a full outbreak in almost one in four patients . Because single-day treatment is more convenient than traditional therapies , it may lead to improved patient compliance and better overall management of recurrent genital herpes outbreaks . Introduction Recurrent genital herpes outbreaks are predominantly caused by herpes simplex virus type 2 -LRB- HSV-2 -RRB- , although the incidence of genital herpes caused by herpes simplex virus type 1 -LRB- HSV-1 -RRB- is rising due to changing attitudes about oral-genital sexual behavior -LSB- 10 -RSB- . Approximately one in four adults in the United States is seropositive for HSV-2 -LSB- 7 -RSB- , an incurable infection that initially enters the body through mucous membranes or abraded skin . Once primary infection has occurred , the virus remains dormant in the dorsal root ganglia , where it can reactivate to cause uncomfortable , recurrent outbreaks -LSB- 20 -RSB- . HSV-2 infection is somewhat more prevalent in women than in men -LSB- 7 -RSB- , and can have a profound psychological impact as well as physical repercussions , resulting in a decrease in measures of quality of life . The fear of spreading the infection can result in lowered self-esteem or sexual desire and , less commonly , social phobia and depression -LSB- 6 -RSB- . In addition , patients with genital herpes have a significantly increased risk of acquiring human immunodeficiency virus -LRB- HIV -RRB- -LSB- 8 -RSB- . Episodic treatment for genital herpes Although there is no cure for HSV-2 infection , patients have two treatment options : treating outbreaks as they occur -LRB- episodic therapy -RRB- , or attempting to prevent future outbreaks -LRB- suppressive therapy -RRB- . Suppression with daily oral antivirals may be most appropriate for patients with frequent or uncomfortable recurrences and to reduce genital herpes transmission to sexual partners -LSB- 4 -RSB- . Although suppressive therapy is effective , one quarter to one half of patients on suppressive therapy have at least one recurrence per year -LSB- 5 , 14 -RSB- . Although episodic treatment has not been proven to reduce the risk of transmission , it still remains an important option for patients who do not want to take daily suppressive therapy , are not concerned about the frequency of recurrences , or are not sexually active . The best method for administering episodic therapy is to make medication readily available to patients to self-administer at the first onset of symptoms or lesions -LSB- 16 -RSB- . This type of patient-initiated episodic therapy has been successfully used in previous clinical trials of recurrent genital herpes -LSB- 13 , 15 -RSB- . The ability to treat recurrences sooner may also help decrease the duration of an outbreak or halt progression to a full outbreak -LSB- 9 , 13 , 15 , 19 -RSB- . This expert opinion will examine the results from a recently published clinical trial about patient-initiated , single-day oral antiviral therapy -LRB- famciclovir -RRB- for recurrent genital herpes -LSB- 2 -RSB- and compare its efficacy with data from currently available therapies . Treatment of recurrent genital herpes with oral antivirals Patients with recurrent episodes of genital herpes often experience such prodromal symptoms as itching , tingling , burning , and pain , with papules and vesicles typically forming 12 -- 24 h after onset of these symptoms -LSB- 3 -RSB- . Maximum HSV-2 concentration and lesion pain usually occur within the first 24 h of onset of these prodromal symptoms -LSB- 3 -RSB- . Under the assumption that one can extrapolate from the pathogenesis of herpes labialis -LSB- 17 -RSB- , it is thought that the most effective way to treat recurrent genital herpes with antiviral therapy would be within the first 24 h after the onset of clinical symptoms , when viral replication is highest -LSB- 16 -RSB- . Oral antivirals , acting as nucleoside analogues enter infected cells and bind to viral thymidine kinase , resulting in their phosphorylation . After the antivirals are further phosphorylated by cellular enzymes , they compete with nucleosides to bind to the viral DNA polymerase , resulting in polymerase inactivation and a decrease in viral replication -LSB- 16 -RSB- . Although topical acyclovir was initially used to treat recurrent genital herpes , it has since been proven ineffective and is no longer recommended -LSB- 1 , 12 -RSB- . Today , the most commonly prescribed drugs are the oral antivirals acyclovir , valacyclovir , and famciclovir . Acyclovir is approved for 5-day episodic treatment , although a 2-day regimen has been shown to be effective -LSB- 13 , 19 -RSB- . Acyclovir is effective , but its poor bioavailability requires more frequent dosing . Valacyclovir , a prodrug of acyclovir , has a higher bioavailability than acyclovir and has been approved for 3-day episodic treatment -LSB- 9 , 14 -RSB- . Famciclovir , a prodrug of penciclovir , is currently indicated for 5-day treatment -LSB- 11 , 15 -RSB- . The high bioavailability of famciclovir -LRB- 77 % -RRB- and the rapid onset of viral replication in recurrent genital herpes suggested that this medication would be efficacious in an even shorter single-day treatment regimen . Data from single-day famciclovir clinical trial A clinical trial was performed to assess whether single-day famciclovir was effective in treating recurrent genital herpes in immunocompetent individuals -LSB- 2 -RSB- . The 329 study participants experiencing a recurrence of genital herpes were predominately female -LRB- 71 % -RRB- and 48 % had had more than six recurrences in the preceding year . Participants were given a single day of famciclovir 1,000 mg bid or matching placebo and were instructed to take the medication within 6 h of the onset of prodromal symptoms and/or genital herpes lesions during their next recurrence . The trial results showed that a single-day regimen of famciclovir significantly decreased lesion-healing time and significantly reduced time to resolution of all symptoms by 2 days , as compared to placebo . Famciclovir also halted the progression to a full genital herpes outbreak in almost one in four patients . Adverse events were of mild-to-moderate intensity and were similar to the adverse events experienced by the placebo group . Although no head-to-head studies have been conducted between single-day famciclovir and other oral antivirals , patients who received single-day famciclovir experienced effects similar to those seen in previous clinical trials of traditional longer-term therapies -LRB- Table 1 -RRB- -LSB- 2 , 9 , 13 , 15 , 18 , 19 -RSB- . Single-day therapy appears to inhibit viral replication enough to significantly reduce both symptoms and the tissue damage characteristic of a full outbreak , preventing progression to a full recurrence in some cases . Table 1Effectiveness of oral antivirals in clinical trials of episodic treatment for genital herpesDrugTreatment regimenMedian time -LRB- days -RRB- to lesion healing -LRB- treatment vs control -RRB- Percent of aborted episodes -LRB- treatment vs control -RRB- Acyclovir -LSB- 13 , 19 -RSB- 800 mg tid × 2 days versus placebo200 mg 5 times daily × 5 days versus placebo4 .0 versus 6.0 -LRB- P = .001 -RRB- 5.7 versus 7.2 a -LRB- P ≤ .001 -RRB- 27.0 versus 10.6 % -LRB- P = .029 -RRB- Was not performedValacyclovir -LSB- 9 , 18 -RSB- 500 mg bid × 3 days versus 500 mg bid × 5 days1 ,000 mg bid × 5 days versus 200 mg acyclovir 5 times daily × 5 days versus placebo4 .4 versus 4.7 -LRB- P = NS -RRB- 4.8 versus 4.8 versus 6.0 -LRB- P < .001 -RRB- b25 .4 versus 26.6 % -LRB- P = NS -RRB- 25.9 versus 24.8 versus 19.8 % -LRB- P = NS -RRB- Famciclovir -LSB- 2 , 15 -RSB- 1,000 mg bid × 1 day versus placebo125 mg bid × 5 days versus placebo4 .3 versus 6.1 -LRB- P < .001 -RRB- 3.8 versus 4.8 -LRB- P < .001 -RRB- 23.3 versus 12.7 % -LRB- P = .003 -RRB- Was not performedaMean time to healingbP values correspond to both valacyclovir versus placebo and acyclovir versus placeboNS = Not significant Conclusion The rapid onset of viral replication in recurrent genital herpes suggests that a shorter course of therapy would be as efficacious as traditional treatments . Patient-initiated single-day therapy could prove helpful for obstetricians/gynecologists who treat patients with genital herpes , as it may provide a more convenient option for treating the disease , and could help to promote patient compliance . High bioavailability , ease of use , and the added benefit of preventing progression to a full genital herpes outbreak in some patients make famciclovir an excellent candidate for single-day patient-initiated episodic therapy .

O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O

single-day famciclovir
patient-initiated episodic therapy