BACKGROUND
ortholog prediction is an important facet of comparative genomics and is frequently used in genome annotation, gene function characterization, evolutionary genomics, and in the identification of conserved regulatory elements.
as the number of genome sequences grow, comparative genomics has become increasingly relevant.
errors in ortholog prediction can greatly affect such studies and associated downstream analyses , so there has been increasing interest in high quality ortholog prediction.
orthologs are commonly defined as genes that have diverged after a speciation event  <cit> , whereas genes that have diverged after a gene duplication event, either before a speciation event  or after a speciation event , are collectively known as paralogs.
it has been found that orthologs tend to have similar function and so their utility in comparative analyses is paramount.
classically, orthologous genes are identified by phylogenetic analysis.
a phylogenetic tree for the genes is compared against a reference species tree, with the notion that the gene tree of orthologs should be similar to the species tree.
however, sophisticated phylogenetic analysis is not easily automated, due in part to the complexity of both manual sequence alignment editing and choice of appropriate genes and species to be included in an analysis.
whole-genome analyses indicate that many gene families  were formed before the divergence of most species commonly being compared in a comparative genomics analysis .
therefore, orthologs – which diverged due to speciation – are typically more similar to each other than to other genes in the genome.
this is why sequence similarity is often used to infer gene orthology between two or more species, and is also the premise behind the most common high-throughput ortholog prediction method used today: the reciprocal-best-blast-hits  analysis  <cit> .
with the rbh method, genes from species a and species b are predicted to be orthologs if they are both the "best blast hit" of the other, when all genes from species a are compared to all genes from species b by blast analysis.
there are numerous resources and methods that use a version of rbh as part of their ortholog prediction process, including the clusters of orthologous groups  database  <cit> , the institute for genomic research 's ego database  <cit> , and inparanoid  <cit> .
however, if a gene is not present in one organism's gene dataset, perhaps due to incomplete genome sequence data or gene loss in the organism, the rbh method will incorrectly predict a paralog as an ortholog .
today, comparative genomics is often being performed using incomplete genomes, especially for large eukaryotic genome sequencing projects.
also, gene loss is a major driving force behind bacterial evolution  <cit> .
it is therefore important to recognize that many of the current ortholog databases will likely contain false-positives due to the limitations of the rbh approach.
for comparative analyses, it is also frequently desirable to identify orthologs that most likely have similar function.
in some cases, an ortholog may diverge more rapidly in sequence  in one organism/species versus another related organism/species.
in addition, a gene duplication may occur in one species, but not a second species, after species divergence.
in this case either one – or both – of the duplicated genes  are more likely to have diverged in function  <cit> .
we therefore propose to differentiate such orthologs  from those that appear to have diverged only due to a speciation event.
we also wish to identify those orthologs that have diverged to a degree that is similar to that expected for its species, since those orthologs that have undergone unusually rapid divergence in one species, relative to another, may have also diverged more in function.
we therefore propose the term ssd-orthologs  to define orthologs that appear to have diverged only due to speciation – and have diverged to the same relative degree as their species.
these ssd-orthologs are more likely to have retained similar function, and would better suit the purposes of many comparative analyses.
to avoid the confusion that may stem from the association of the term "many-to-many orthologs" with in-paralogs, we will use the term paralogs in this text to refer to out-paralogs and specify in-paralogs, when applicable.
to address these issues, we have developed a method we call ortholuge.
ortholuge is a high-throughput analysis pipeline that evaluates previously predicted orthologs  and generates predictions regarding which of these are likely ssd-orthologs and which are likely paralogs or other non-ssd-orthologs.
the pipeline requires tentative ortholog predictions  for large gene datasets from three species, two of which are the species to be compared, and one of which is an outgroup species.
all phylogenetic distances between the genes/proteins in an ortholog group are computed for each group in the input list.
ratios of these distances are used to evaluate ortholog quality.
we find that these ratios show certain consistencies over several sets of eukaryotic and bacterial orthologs, along with data sets introduced with true-negatives for comparison.
this permitted the formulation of ratio cut-offs for retaining ssd-orthologs and removing probable paralogs, which resulted in a higher quality data set of orthologs.
overall, we demonstrate that the relative evolutionary relationships may be used to support the prediction of orthologs.
in addition, noting those orthologs with prominent differences  may help refine analyses to permit the identification of those orthologs that most likely retain the same function.
RESULTS
an overview of the ortholuge approach for increasing the specificity of ortholog predictions is outlined in figure  <dig>  based on the analyses described below, the details of this approach were formulated and the approach validated using both prokaryotic and eukaryotic data sets.
ortholuge software is available  <cit>  to assist with the analysis of data sets other than those reported here.
data sets exhibited little bias due the automated sequence alignment trimming approach
we investigated the behaviour and utility of ortholuge through analysis of diverse eukaryotic and bacterial rbh-derived datasets.
for the initial test eukaryotic data set, we chose predicted mouse-rat-human orthologs from the expressed sequence tag  data in tigr's eukaryotic gene ortholog  database  <cit>  .
the majority of our subsequent analyses utilized the higher quality mgd-based dataset  and the refseq-based rbh dataset composed of these same species, as indicated.
for the bacterial data set, we chose three gamma-proteobacteria: escherichia coli, pseudomonas putida, and pseudomonas syringae .
orthologs between these three species  were predicted using a transitive rbh approach, applied to the deduced proteins from complete genome sequences  <cit> .
accurate sequence alignment is critical for phylogenetic analysis; thus, we wished to improve the automated alignment and trimming components of the ortholuge method.
we therefore performed a comprehensive examination of biases in our automated alignment editing process .
a sample of rbh-predicted ortholog sequence sets was analyzed to devise the gap-masking and sequence trimming approaches.
the sequence sets were examined to identify both gaps introduced by misalignments and gaps introduced through sequence insertions and deletions.
our observations suggested that some of the noise introduced through the misalignment may be alleviated through the removal of the gapped-segment flanking portions.
we also noted that there was no appreciable effect on the sequence distances when the flanking sequences around the sequence-variation gapped regions were removed.
we manually introduced gap-masking simulations over the sequences using various window length criteria to establish a gap-masking approach with a relatively conservative worst-case scenario.
both the trimming and gap-masking methods were evaluated for the introduction of ratio distribution biases by selected alignment characteristics.
no obvious bias was observed through the introduction of our gap masking approach or alignment trimming .
ortholuge produces ratios which form distributions
ortholuge was designed with the purpose of overcoming certain limitations of the rbh method, such as the problem illustrated in figure  <dig>  ortholuge overcomes this problem by using ratios of phylogenetic distances between genes to evaluate orthology, and using an outgroup species as a reference for two ingroup species being compared .
for these three species, the distances for the "ortholog triple" are calculated and the three possible ratios that can be generated are calculated .
with this approach, the problem illustrated in figure  <dig> would be detected because the human-cattle distance is unexpectedly larger than the human-mouse distance – impacting on ratio values.
we ran ortholuge on three mouse-rat-human datasets: two sets of rbh-predicted orthologs – one based on ego data and the other based on refseq data – and a third high-quality curated set.
for all datasets, human was the outgroup used to help predict more precise orthologs between mouse  and rat .
the resulting ortholuge phylogenetic distance ratios are shown in figure  <dig> and supplemental figure  <dig> as histograms.
for each of the three ratios, we tabulated the frequency of putative orthologous groups within certain ratio value ranges.
ratio <dig>  ratio <dig>  and ratio <dig> each form clear distributions.
ratio <dig> is generally located around a ratio value of  <dig>  which is expected if the chosen outgroup is more distant relative to the ingroups.
it is centered to the left or right of  <dig> depending on which of the two ingroups is closer to the outgroup.
the ratio <dig> and ratio <dig> distributions are generally located at a ratio much lower than  <dig>  reflecting the closer relationship between the ingroup species versus any ingroup to the outgroup.
we ran our analyses on both protein and nucleotide sequences and found that for closely related species such as these, nucleotide sequences provide a better ratio distribution resolution.
however, the overall ratio distributions are similar, even when using different methods of initial ortholog detection .
we also performed this analysis with our bacterial p. putida-p. syringae-e. coli orthologs, comparing p. putida  and p. syringae  using e. coli as the outgroup.
we observed very similar results: both the eukaryotic and prokaryotic data sets are consistent in the distributions formed, and in the approximate position of the distributions.
since we expected most ssd-orthologs  to evolve in a similar manner, we hypothesized that orthologs falling within the higher frequency ranges of the distributions are more likely to be ssd-orthologs compared to those that are outliers.
in essence, what is defining the species divergence is the divergence observed for most genes .
ortholuge ratios can also be conveniently visualized in an r <dig> × r <dig> plot
instead of histograms , an alternative way to represent ortholuge ratios is to use a 2-dimensional plot of two ortholuge ratios, where each putative ortholog group is represented by one point in the graph.
in principal, any two of the three ratios can be used for the plot, since the three ratios are related.
that is, ratio <dig> equals ratio <dig> divided by ratio <dig>  through subsequent analyses, we found that the ratio <dig> and ratio <dig> combination  was the simplest to visualize and to work with.
for the r <dig> × r <dig> plots, the eukaryotic mouse-rat-human rbh-predicted putative orthologous groups appear to occupy three types of positions .
the majority of points form a cluster  at low ratio <dig> and ratio <dig> values.
in fact, about 85% of orthologs have ratio <dig> and ratio <dig> values less than  <dig>   some points with higher ratio <dig> values are located along a curve that approaches, and then falls along, the line equation ratio <dig> =  <dig>  this is consistent with an unusually high divergence of a gene from ingroup  <dig>   conversely, some points with higher ratio <dig> values are located along a line that is roughly around line equation ratio <dig> =  <dig>  this is consistent with an unusually high divergence for a gene from ingroup  <dig>  the rbh-predicted orthologous groups for p. putida-p. syringae-e. coli species show a similar r <dig> × r <dig> plot .
consistent with the eukaryotic results, the vast majority of orthologous groups for this prokaryotic analysis also exhibit ratio <dig> and ratio <dig> values less than  <dig>
we expected most ssd-orthologs to evolve in a similar manner, and found that most orthologous groups form a cluster  in an r <dig> × r <dig> plot.
therefore, we hypothesized that orthologous groups falling within the high frequency range are more likely to contain ssd-orthologs.
conversely, those outside of this range  are more likely to contain, in an ingroup, either an ortholog that has undergone unusual divergence, or a paralog.
"higher quality" orthologous groups are found primarily in "low" ortholuge ratio ranges, in r <dig> × r <dig> plots
the data sets of tentative orthologs predicted above by an rbh approach will certainly contain genes that are being falsely identified as orthologs.
it is difficult, if not impossible, to obtain a dataset of this size that contains only true orthologs, due to the inherent nature of inference associated with evolutionary study.
however, data sets of "higher" and "lower" quality can be constructed and examined , to observe how their ortholuge ratios change in comparison to each other.
these data sets should contain a notably greater or smaller proportion of true orthologs, respectively.
we therefore examined the behaviour of ortholuge ratios for a higher quality data set of probable orthologs.
curated orthologs between human, mouse, and rat genomes were acquired from the mouse genome database .
figure 5b and 5e illustrate that this higher quality data set occupies a smaller area of the r <dig> × r <dig> plot.
this smaller area is observed, even when the number of points is normalized with the number plotted for the rbh-based data .
for this higher quality  data set there are notably fewer points along the ratio <dig> =  <dig> line equation and the ratio <dig> =  <dig> line in the plot, compared to the rbh-based data plot in figure 5a and 5d.
conversely, we examined the ratios associated with a "lower quality" data set, involving rbh-predicted orthologs for bovine, human, and mouse, from tigr's ego database .
the incomplete state of the bovine genome data at the time of this analysis should lead to more falsely predicted orthologs, since some true orthologs will be missing from the bovine dataset .
these results are shown in figure 5c and 5f.
note the higher number of points with a high ratio <dig> value, falling along the line equation ratio <dig> = 1; these points are consistent with how the ratio would behave if the bovine data contained paralogs that were notably more divergent than expected for most orthologs.
to gain a sense of the differences in plots of different quality datasets, note that below ratio <dig> and ratio <dig> values of  <dig>  there lies 97% of high quality dataset points , 86% of rbh-predicted ortholog group points , and only 73% of the low quality data set points .
these results suggest that true orthologs  tend to fall within the bulk of the highest frequency range , while orthologs with unusual divergence patterns  and paralogs have either high ratio <dig> or high ratio <dig> values.
for the prokaryotic analysis, a higher quality data set was compared to the rbh-based data set as well.
figure 6a and 6b illustrate the same trend as the eukaryotic data, with respect to how the r <dig> × r <dig> plots look for more precise and less precise ortholog data sets.
known paralogs  introduced into orthologous groups generate either high ratio <dig> or high ratio <dig> values, as shown in a gene loss/incomplete genome simulation
the above comparisons of higher quality  and lower quality  ortholog data sets support our hypothesis that orthologs and paralogs fall within different regions of the r <dig> × r <dig> plot.
however, a stronger argument can be made by examining specifically where falsely predicted orthologs  occur in such distributions.
a true-negative data set was therefore constructed by removing genes from one of the ingroup gene data sets and then identifying the next best reciprocal blast hit with the other ingroup .
therefore a true negative is essentially an ortholog triple which has been transformed into a false positive by introducing a less similar sequence for one of the species sequences.
these true negatives represent the types of ortholog predictions that would result from an rbh-method in scenarios such as figure  <dig>  since we know that rbh can make incorrect predictions when a genome is incomplete or when gene loss has occurred, this analysis simulates what would occur with the rbh method in such cases.
the benefit of this analysis is that we specifically know the true-negatives introduced, allowing us to examine how the ortholuge ratios for these true-negatives  behave.
for the e. coli-p. putida-p. syringae input ortholog groups, we constructed two true-negative data sets.
in the first, we replaced p. putida genes with their next best rbh hit to p. syringae, resulting in ingroup <dig> paralogs.
in the second, we replaced p. syringae genes with their next best rbh hit to p. putida, resulting in ingroup <dig> paralogs.
for both, we conservatively introduced all possible paralogs into the analysis, resulting in roughly 50% of the genes converted to true-negatives .
the results from these two data sets , show that these true-negatives overlap very little with the rbh-predicted orthologs  or with the high quality  orthologs .
this demonstrates that even with all possible true paralogs simulated, very few of them are falling within the higher frequency ranges of the rbh distributions.
we also constructed a third true-negative data set with all outgroup genes  replaced by their next best rbh hit to both p. syringae and p. putida.
the r <dig> × r <dig> plot  shows that these true-negative cases plot at lower ratio <dig> and ratio <dig> values and do not separate well from what would be expected for true-orthologs.
this is actually promising, since in the case of a paralog in an outgroup, the two ingroups should still be regarded as probable true orthologs and should still be falling within the main cluster of true-orthologs, as we observe.
in other words, since the goal of ortholuge is to improve ortholog identification between the two ingroups, it is beneficial that an outgroup paralog does not generally interfere with/affect the analysis.
ortholuge ratio cut-offs, to separate orthologs from paralogs, can be determined based on an iterative-true-negative analysis
after determining that the introduced true-negatives almost never fall within certain ratio ranges, it became clear that ratio cut-offs could be derived to exclude most true-negatives, and thus improve the specificity  of ortholog prediction.
to do this, another strategy was employed to simulate the introduction of paralogs  and then formulate ortholog identification cut-offs.
this second strategy, involving an iterative-true-negative analysis, allows one to view the variance in proportion of true-negatives in a particular ratio range, and is also amenable to high throughput use for the formulation of cut-offs.
for both the eukaryotic  rbh-predicted data set , and the prokaryotic rbh-predicted data set, we conservatively modeled an incomplete genome  scenario by randomly replacing 25% of the genes in the rbh-predicted data set with the "next best rbh" hit .
this randomized introduction of true-negatives was iterated at least  <dig> times, and each iteration was evaluated by ortholuge.
the proportion of true-negative orthologs was averaged over all iterations and the standard deviation determined.
we found that that once again, the ratio values of true-negative orthologs do not overlap well with those of the bulk of rbh-predicted orthologous groups .
for both the prokaryotic and eukaryotic rbh-based data sets, this iterative true-negative analysis was used to determine ratio ranges where true paralogs were very unlikely to land and ranges where they were very likely to land.
the borders of these ranges  became the ratio cut-off values.
this permitted classification of the rbh-predicted tentative orthologous groups into probable ssd-orthologs, probable paralogs, or "uncertain" categories.
it should be noted that a more accurate name for the 'probable paralog' category might be 'probable non-ssd-ortholog,' because there may be true orthologs that have undergone unusual divergence in one ingroup species within this category.
however, in such cases the non-ssd-orthologs may have functionally diverged, and therefore are cases that we would want to differentiate from our ssd-ortholog set.
regardless, for ease of comprehension, we propose to call those cases with very atypical ratios  "probable paralogs", since paralogs likely predominate in this region.
we chose a 25% true-negative introduction, since this is likely above a worst-case scenario in terms of the number of genes that may be missing in an incomplete genome, or most cases of naturally occurring gene loss.
we felt it was important to "saturate" the data set with true-negatives, because any given rbh-based dataset will likely contain some proportion of false-positives in the putative orthologous groups .
therefore, to effectively identify the ranges where true-negatives were becoming increasingly more common we needed to observe a large proportion of true-negatives.
however, we did not want to transform a data set with all possible true-negatives, as this would not provide a sense of the variation in proportion of true-negatives within a given ratio range.
note that we also chose to report the results here for a transformation of an rbh-predicted data set with the true-negatives , rather than a transforming a high quality dataset, since the refseq based analysis could be more easily fully automated .
however, transformation of a eukaryotic high quality dataset with true-negatives generated similar cut-off values .
through an iterative sampling approach we were able to generate standard deviations of the proportion of true-negatives in a given ratio range , providing a clearer picture of the likelihood of a true-negative occurring in that range.
ortholuge ratios in combination can help predict which gene in a given putative orthologous group is likely a paralog
a closer inspection of the ortholuge ratios shows that they behave in a predictable fashion when the ortholog group contains one or more false-positives .
for example, if ingroup <dig> is actually a paralog, then the distance between ingroup1-outgroup and the distance between ingroup1-ingroup <dig> would be larger than the norm for an ssd-ortholog.
this would cause ratio <dig> to increase , and ratio <dig> to increase a slighter amount .
conversely, if ingroup <dig> is actually the paralog, then ratio <dig> would be expected to increase and ratio <dig> to increase slightly.
these predictable changes do indeed occur, as illustrated by an analysis of true-negatives , an analysis of a dataset of tentative orthologs identified by rbh using an incomplete genome , and an additional manual review of selected cases .
we propose that when unusual ratio ranges are identified for a given orthologous group, the relative changes can facilitate predictions regarding which of the two ingroups may contain a paralog .
note that an outgroup paralog cannot be well predicted, however this does not affect the utility of ortholuge, since the method is focused on characterizing the orthology of the two ingroups.
it should also be noted that multiple-paralog scenarios , are more complex.
though relatively easy to predict on paper, they are more difficult to distinguish in reality, because the amount of divergence for the two paralogs may vary greatly.
in most cases they would resemble one of the first three scenarios, depending on which of the two paralogs was more diverged.
nevertheless, in the end, these rare cases  will still most frequently display atypical ratios, and will not fall within probable ortholog cut-offs.
ortholuge in action: an estimation of probable ssd-orthologs and probable paralogs in rbh-based data sets
an example of ratio cut-offs generated based on our true-negative analysis is listed in table  <dig> .
researchers are of course encouraged to choose their own cut-off to suit their needs .
however, based on our simulations, these cut-offs should effectively differentiate probable orthologs and paralogs for these data sets.
we also propose that these cut-offs can identify those orthologs most closely following species divergence  – orthologs which may be more functionally similar to each other versus those that have diverged at different evolutionary rates in each species.
using the derived ratio cut-offs, we have constructed several data sets of probable ssd-orthologs consisting of: mouse-rat comparisons , and one for a p. putida-p. syringae comparison .
these ssd-orthologs are particularly suited for comparative genomics analyses.
in addition, notations are added to all the data analysed, indicating cases of probable gene duplication after species divergence  – a scenario that can increase the likelihood of functional divergence of the genes.
these higher quality sets of orthologs can be found via the ortholuge website  <cit> .
the proportion of ssd-orthologs in the rbh-predicted data sets is summarized in table  <dig>  note that cases of in-paralogs are not counted within the counts of ssd-orthologs in table  <dig>  such cases, due to their uncertain potential to have diverged in function because of a gene duplication, are counted within the "uncertain" category.
using the cut-offs, we were also able to estimate the proportion of rbh-predicted orthologs that are likely paralogs for these eukaryotic and prokaryotic data sets .
for the prokaryotic data about 5% of rbh-based predictions are probable paralogs.
for the eukaryotic data, about 10% of the rbh-predictions are probable paralogs.
these are significant numbers that validate the need for a method like ortholuge, particularly if one is trying to use rbh-predicted orthologs for downstream analyses that require stringent ortholog prediction .
application of these cut-offs to classify the curated eukaryote and prokaryote datasets suggest that the false negative rate in is in the range of  <dig> % for prokaryote data and 3% for the eukaryote data.
to facilitate the analysis of other datasets, we have developed ortholuge software that can be used to characterize any existing dataset of orthologs.
if no pre-existing ortholog dataset is available, ortholuge can also construct such a dataset using an rbh-based approach applied to whole genome datasets .
ortholuge was developed using perl under linux  and operates in any unix environment, provided all the needed tools  are available for the user's operating system.
this freely available, open source, software is available on the ortholuge website  <cit> .
discussion
for cross-genome comparison purposes, researchers often wish to compare orthologs – in particular orthologs that have not undergone unusual divergence rates relative to one another, and have more likely retained similar function.
we propose that ortholuge is an approach, suitable for high-throughput genome-scale analysis, which aids identification of such orthologs.
the ortholuge method significantly improves the specificity/precision of high-throughput rbh-based ortholog analysis.
for example, our results indicate that roughly  <dig> in  <dig> rbh-predicted rat-mouse orthologs are very likely paralogs, and about  <dig> in  <dig> rbh-predicted orthologs for two pseudomonas species are similarly likely incorrect.
note that our rbh analysis requires transitivity between three species, rendering it more stringent than the typical rbh analysis between two species.
this suggests that the typical rbh analysis may have an even greater number of false predictions.
the resulting more specific identification of orthologs by ortholuge is an important requirement for many downstream analyses, such as identifying gene regulatory regions, or characterizing differences in microarray-measured gene expression responses across species.
an automated method such as ortholuge is of course no substitute for a more manual, comprehensive phylogenetic analysis and has some limitations as mentioned below.
however, its simplicity and utility for high-throughput analyses suggest that it is a useful complement to rbh-based identification of putative orthologs using whole-genome gene datasets.
in addition, ortholuge's higher specificity approach can complement other methods that may provide a higher sensitivity/recall approach for ortholog identification  <cit> .
ortholuge evaluates orthologs through phylogenetic distance comparisons.
to perform such comparisons, an outgroup is required to assist the prediction of orthologs between the two ingroups – this has simultaneous advantages and disadvantages.
the added sequence provides extra resolution and extra specificity; however, a distant outgroup may lessen the sensitivity of the approach.
presumably, though, as more genomes are sequenced, the number of possible outgroups available to choose from will increase and very distant outgroups will become less of a problem.
the ortholuge pipeline generates predictions by evaluating the entire genome at once .
the more data points that are representative of the genome, the more confident the ratio cut-offs will be.
it assumes that the majority of incoming predictions are true orthologs, will exhibit expected ratios, and will thus form the high frequency ranges of the distributions.
our analysis does suggest this assumption to be reasonable and, notably, both eukaryotic and bacterial orthologs display similar ratio distributions, despite marked differences regarding how such organisms evolve.
once the genome-wide predictions are made for a certain species combination, ortholuge can be used to estimate how likely it is that a specific putative orthologous group contains a true-negative within its ingroups.
in such cases, we can match these ratios with a category , to suggest which gene in the ortholog group is likely to be the paralog.
however, it should be emphasized that at this time we have not exhaustively examined all possible scenarios, and so such analysis should be taken as a guide requiring further investigation.
interestingly, this method also appears to be useful to examine, in a genome-wide scale, the relationships between species.
by examining the ratio values at the highest frequency ranges in the histograms, one can easily determine which two of any three organisms are more similar to each other, on average, and on a genome-wide scale .
the simplicity of ortholuge allows for many benefits.
for example, it can easily be re-run when genome annotations undergo significant changes.
in addition, it can easily be customized with any method of sequence alignment or phylogenetic distance calculation, depending upon the researcher's preference.
it is expected that further analysis will reveal relationships between true-negative analyses and ratio cut-off generation, negating the need to perform a full iterative-introduced-true-negative analysis for each species comparison.
of course, users can choose their own ortholuge ratio cut-offs, either using a true-negative analysis, or another approach of their choice, for identification of orthologs at their preferred level of specificity.
accepting only orthologs in a certain ratio range and discarding the rest will certainly eliminate a small fraction of true orthologs from the input set.
for example, if the probable paralog cut-offs are applied to the "high quality" curated prokaryotic and eukaryotic data sets, we eliminate  <dig> % and 3% of the prokaryotic and eukaryotic predictions, respectively.
however, if the more stringent ssd-ortholog cut-offs are applied, we eliminate  <dig> % and 8% of the predictions, respectively.
while these outliers may be false-positives in the curated data, they may also be true orthologs that have undergone unusual divergence in one ingroup species.
for example, if a gene duplication occurred in one ingroup species after the speciation divergence, the resulting duplicated gene may undergo accelerated evolution  <cit> .
such scenarios would result in skewed ratios for true orthologs.
however, we propose that such orthologs with unusual  divergence may more likely have differing function at some level.
in many genome-wide studies involving comparisons between species, researchers wish to identify those genes that are more likely to be functionally equivalent – i.e.
orthologs that did not experience unusual rates of evolution or gene transfer.
ortholuge improves the identification of such "supporting species divergence" ortholog pairs .
this is apparently an important issue, as illustrated by some confusion occurring in the literature regarding the definition of orthologs.
the definition that we, and many evolutionary biologists use, is the one initially proposed  <cit>  that describes orthologs as genes that have diverged due to speciation .
however, the term ortholog is increasingly being inferred to mean 'functionally equivalent genes in different species' – a common misconception  <cit> .
while we and others agree that orthologs tend to have similar function, this is not a requirement for orthology  <cit> .
so, it appears that while many researchers are identifying orthologs in a genetic or genomic study, what they really wish to identify is the subset of orthologs that are specifically functionally equivalent.
some methods, such as the widely used inparanoid, refer to all in-paralogs  in the one species as orthologous to the related gene in the other species.
they do not clearly distinguish between such cases of in-parology and more simple one-to-one orthologous relationships.
we believe that such cases should be differentiated because a duplication event after species divergence may have led to significant functional divergence of one or both of the duplicated genes in the one species.
in ortholuge, cases involving possible in-paralogs are flagged using a simple analysis that focuses on detecting the most clear-cut in-paralog cases.
for our analysis, we did apply ratio cutoffs derived using one-to-one ortholog rbh-based  data to classify a same species  data set that includes both one-to-one orthologs and many-to-many orthologs.
however, we recognize a need to implement more robust procedures that would consider all cases of suspected recent gene duplications in the analysis .
it would also be desirable to complement this analysis further by noting cases of relative gene rearrangement in the input set of orthologs.
ortholuge in its current form cannot detect gene rearrangements, however it could potentially complement other bioinformatics approaches that detect such rearrangements  <cit> .
ortholuge could also be adapted to contain a gene rearrangement analysis that is customized to its methodology.
these additional ortholog evolutionary scenarios, involving possible in-paralogy or gene rearrangements, should be specifically noted because they cannot be distinguished by examining ortholuge distance ratios alone.
they require further study in any comparative analysis, since functional equivalence between the orthologs is less likely.
regarding the limitations of this method, it should be emphasized that ortholuge is limited by the quality of the initial ortholog-analysis .
ortholuge is also only as good as the quality of the sequence data being analyzed.
we have tested our alignment trimming and masking of regions of lower alignment quality extensively to improve the critical sequence alignment component of our method; however, certainly this method will fail if low quality sequences, with many errors, are used in the analysis.
in addition, the top blast hit is not necessarily the nearest neighbour  <cit>  and so true orthologs may be missed when using ortholuge after initially identifying orthologs with an rbh-based approach.
ortholuge could therefore improve if the initial ortholog prediction method is improved .
regardless of any limitations, ortholuge appears to effectively improve the specificity of ortholog identification and is suitable for high-throughput, genome-wide use.
given the amount of genomics data being obtained at this time, such specific, high-throughput approaches will become increasingly necessary, as genomics research moves further toward more multi-genome comparative analyses.
CONCLUSIONS
ortholuge improves the specificity of ortholog identification and is suitable for high-throughput use.
this precise ortholog prediction method complements other ortholog prediction methods that are not focused on precision and it potentially identifies those orthologs most likely to be functionally similar.
the ortholuge method provides important data set evaluation for a variety of analyses based on comparative approaches, including gene function prediction, prediction of conserved regulatory elements, and comparative analysis of gene order or gene/protein expression data.

 ortholog prediction is an important facet of comparative genomics and is frequently used in genome annotation, gene function characterization, evolutionary genomics, and in the identification of conserved regulatory elements. as the number of genome sequences grow, comparative genomics has become increasingly relevant. errors in ortholog prediction can greatly affect such studies and associated downstream analyses, so there has been increasing interest in high quality ortholog prediction. orthologs are commonly defined as genes that have diverged after a speciation event, whereas genes that have diverged after a gene duplication event, either before a speciation event or after a speciation event, are collectively known as paralogs. it has been found that orthologs tend to have similar function and so their utility in comparative analyses is paramount. classically, orthologous genes are identified by phylogenetic analysis. a phylogenetic tree for the genes is compared against a reference species tree, with the notion that the gene tree of orthologs should be similar to the species tree. however, sophisticated phylogenetic analysis is not easily automated, due in part to the complexity of both manual sequence alignment editing and choice of appropriate genes and species to be included in an analysis. whole-genome analyses indicate that many gene families were formed before the divergence of most species commonly being compared in a comparative genomics analysis. therefore, orthologs – which diverged due to speciation – are typically more similar to each other than to other genes in the genome. this is why sequence similarity is often used to infer gene orthology between two or more species, and is also the premise behind the most common high-throughput ortholog prediction method used today: the reciprocal-best-blast-hits analysis. with the rbh method, genes from species a and species b are predicted to be orthologs if they are both the "best blast hit" of the other, when all genes from species a are compared to all genes from species b by blast analysis. there are numerous resources and methods that use a version of rbh as part of their ortholog prediction process, including the clusters of orthologous groups database, the institute for genomic research's ego database, and inparanoid. however, if a gene is not present in one organism's gene dataset, perhaps due to incomplete genome sequence data or gene loss in the organism, the rbh method will incorrectly predict a paralog as an ortholog. today, comparative genomics is often being performed using incomplete genomes, especially for large eukaryotic genome sequencing projects. also, gene loss is a major driving force behind bacterial evolution. it is therefore important to recognize that many of the current ortholog databases will likely contain false-positives due to the limitations of the rbh approach. for comparative analyses, it is also frequently desirable to identify orthologs that most likely have similar function. in some cases, an ortholog may diverge more rapidly in sequence in one organism/species versus another related organism/species. in addition, a gene duplication may occur in one species, but not a second species, after species divergence. in this case either one – or both – of the duplicated genes are more likely to have diverged in function. we therefore propose to differentiate such orthologs from those that appear to have diverged only due to a speciation event. we also wish to identify those orthologs that have diverged to a degree that is similar to that expected for its species, since those orthologs that have undergone unusually rapid divergence in one species, relative to another, may have also diverged more in function. we therefore propose the term ssd-orthologs to define orthologs that appear to have diverged only due to speciation – and have diverged to the same relative degree as their species. these ssd-orthologs are more likely to have retained similar function, and would better suit the purposes of many comparative analyses. to avoid the confusion that may stem from the association of the term "many-to-many orthologs" with in-paralogs, we will use the term paralogs in this text to refer to out-paralogs and specify in-paralogs, when applicable. to address these issues, we have developed a method we call ortholuge. ortholuge is a high-throughput analysis pipeline that evaluates previously predicted orthologs and generates predictions regarding which of these are likely ssd-orthologs and which are likely paralogs or other non-ssd-orthologs. the pipeline requires tentative ortholog predictions for large gene datasets from three species, two of which are the species to be compared, and one of which is an outgroup species. all phylogenetic distances between the genes/proteins in an ortholog group are computed for each group in the input list. ratios of these distances are used to evaluate ortholog quality. we find that these ratios show certain consistencies over several sets of eukaryotic and bacterial orthologs, along with data sets introduced with true-negatives for comparison. this permitted the formulation of ratio cut-offs for retaining ssd-orthologs and removing probable paralogs, which resulted in a higher quality data set of orthologs. overall, we demonstrate that the relative evolutionary relationships may be used to support the prediction of orthologs. in addition, noting those orthologs with prominent differences may help refine analyses to permit the identification of those orthologs that most likely retain the same function.  an overview of the ortholuge approach for increasing the specificity of ortholog predictions is outlined in figure based on the analyses described below, the details of this approach were formulated and the approach validated using both prokaryotic and eukaryotic data sets. ortholuge software is available to assist with the analysis of data sets other than those reported here. data sets exhibited little bias due the automated sequence alignment trimming approach we investigated the behaviour and utility of ortholuge through analysis of diverse eukaryotic and bacterial rbh-derived datasets. for the initial test eukaryotic data set, we chose predicted mouse-rat-human orthologs from the expressed sequence tag data in tigr's eukaryotic gene ortholog database. the majority of our subsequent analyses utilized the higher quality mgd-based dataset and the refseq-based rbh dataset composed of these same species, as indicated. for the bacterial data set, we chose three gamma-proteobacteria: escherichia coli, pseudomonas putida, and pseudomonas syringae. orthologs between these three species were predicted using a transitive rbh approach, applied to the deduced proteins from complete genome sequences. accurate sequence alignment is critical for phylogenetic analysis; thus, we wished to improve the automated alignment and trimming components of the ortholuge method. we therefore performed a comprehensive examination of biases in our automated alignment editing process. a sample of rbh-predicted ortholog sequence sets was analyzed to devise the gap-masking and sequence trimming approaches. the sequence sets were examined to identify both gaps introduced by misalignments and gaps introduced through sequence insertions and deletions. our observations suggested that some of the noise introduced through the misalignment may be alleviated through the removal of the gapped-segment flanking portions. we also noted that there was no appreciable effect on the sequence distances when the flanking sequences around the sequence-variation gapped regions were removed. we manually introduced gap-masking simulations over the sequences using various window length criteria to establish a gap-masking approach with a relatively conservative worst-case scenario. both the trimming and gap-masking methods were evaluated for the introduction of ratio distribution biases by selected alignment characteristics. no obvious bias was observed through the introduction of our gap masking approach or alignment trimming. ortholuge produces ratios which form distributions ortholuge was designed with the purpose of overcoming certain limitations of the rbh method, such as the problem illustrated in figure ortholuge overcomes this problem by using ratios of phylogenetic distances between genes to evaluate orthology, and using an outgroup species as a reference for two ingroup species being compared. for these three species, the distances for the "ortholog triple" are calculated and the three possible ratios that can be generated are calculated. with this approach, the problem illustrated in figure would be detected because the human-cattle distance is unexpectedly larger than the human-mouse distance – impacting on ratio values. we ran ortholuge on three mouse-rat-human datasets: two sets of rbh-predicted orthologs – one based on ego data and the other based on refseq data – and a third high-quality curated set. for all datasets, human was the outgroup used to help predict more precise orthologs between mouse and rat. the resulting ortholuge phylogenetic distance ratios are shown in figure and supplemental figure as histograms. for each of the three ratios, we tabulated the frequency of putative orthologous groups within certain ratio value ranges. ratio ratio and ratio each form clear distributions. ratio is generally located around a ratio value of which is expected if the chosen outgroup is more distant relative to the ingroups. it is centered to the left or right of depending on which of the two ingroups is closer to the outgroup. the ratio and ratio distributions are generally located at a ratio much lower than reflecting the closer relationship between the ingroup species versus any ingroup to the outgroup. we ran our analyses on both protein and nucleotide sequences and found that for closely related species such as these, nucleotide sequences provide a better ratio distribution resolution. however, the overall ratio distributions are similar, even when using different methods of initial ortholog detection. we also performed this analysis with our bacterial p. putida-p. syringae-e. coli orthologs, comparing p. putida and p. syringae using e. coli as the outgroup. we observed very similar results: both the eukaryotic and prokaryotic data sets are consistent in the distributions formed, and in the approximate position of the distributions. since we expected most ssd-orthologs to evolve in a similar manner, we hypothesized that orthologs falling within the higher frequency ranges of the distributions are more likely to be ssd-orthologs compared to those that are outliers. in essence, what is defining the species divergence is the divergence observed for most genes. ortholuge ratios can also be conveniently visualized in an r × r plot instead of histograms, an alternative way to represent ortholuge ratios is to use a 2-dimensional plot of two ortholuge ratios, where each putative ortholog group is represented by one point in the graph. in principal, any two of the three ratios can be used for the plot, since the three ratios are related. that is, ratio equals ratio divided by ratio through subsequent analyses, we found that the ratio and ratio combination was the simplest to visualize and to work with. for the r × r plots, the eukaryotic mouse-rat-human rbh-predicted putative orthologous groups appear to occupy three types of positions. the majority of points form a cluster at low ratio and ratio values. in fact, about 85% of orthologs have ratio and ratio values less than some points with higher ratio values are located along a curve that approaches, and then falls along, the line equation ratio = this is consistent with an unusually high divergence of a gene from ingroup conversely, some points with higher ratio values are located along a line that is roughly around line equation ratio = this is consistent with an unusually high divergence for a gene from ingroup the rbh-predicted orthologous groups for p. putida-p. syringae-e. coli species show a similar r × r plot. consistent with the eukaryotic results, the vast majority of orthologous groups for this prokaryotic analysis also exhibit ratio and ratio values less than we expected most ssd-orthologs to evolve in a similar manner, and found that most orthologous groups form a cluster in an r × r plot. therefore, we hypothesized that orthologous groups falling within the high frequency range are more likely to contain ssd-orthologs. conversely, those outside of this range are more likely to contain, in an ingroup, either an ortholog that has undergone unusual divergence, or a paralog. "higher quality" orthologous groups are found primarily in "low" ortholuge ratio ranges, in r × r plots the data sets of tentative orthologs predicted above by an rbh approach will certainly contain genes that are being falsely identified as orthologs. it is difficult, if not impossible, to obtain a dataset of this size that contains only true orthologs, due to the inherent nature of inference associated with evolutionary study. however, data sets of "higher" and "lower" quality can be constructed and examined, to observe how their ortholuge ratios change in comparison to each other. these data sets should contain a notably greater or smaller proportion of true orthologs, respectively. we therefore examined the behaviour of ortholuge ratios for a higher quality data set of probable orthologs. curated orthologs between human, mouse, and rat genomes were acquired from the mouse genome database. figure 5b and 5e illustrate that this higher quality data set occupies a smaller area of the r × r plot. this smaller area is observed, even when the number of points is normalized with the number plotted for the rbh-based data. for this higher quality data set there are notably fewer points along the ratio = line equation and the ratio = line in the plot, compared to the rbh-based data plot in figure 5a and 5d. conversely, we examined the ratios associated with a "lower quality" data set, involving rbh-predicted orthologs for bovine, human, and mouse, from tigr's ego database. the incomplete state of the bovine genome data at the time of this analysis should lead to more falsely predicted orthologs, since some true orthologs will be missing from the bovine dataset. these results are shown in figure 5c and 5f. note the higher number of points with a high ratio value, falling along the line equation ratio = 1; these points are consistent with how the ratio would behave if the bovine data contained paralogs that were notably more divergent than expected for most orthologs. to gain a sense of the differences in plots of different quality datasets, note that below ratio and ratio values of there lies 97% of high quality dataset points, 86% of rbh-predicted ortholog group points, and only 73% of the low quality data set points. these results suggest that true orthologs tend to fall within the bulk of the highest frequency range, while orthologs with unusual divergence patterns and paralogs have either high ratio or high ratio values. for the prokaryotic analysis, a higher quality data set was compared to the rbh-based data set as well. figure 6a and 6b illustrate the same trend as the eukaryotic data, with respect to how the r × r plots look for more precise and less precise ortholog data sets. known paralogs introduced into orthologous groups generate either high ratio or high ratio values, as shown in a gene loss/incomplete genome simulation the above comparisons of higher quality and lower quality ortholog data sets support our hypothesis that orthologs and paralogs fall within different regions of the r × r plot. however, a stronger argument can be made by examining specifically where falsely predicted orthologs occur in such distributions. a true-negative data set was therefore constructed by removing genes from one of the ingroup gene data sets and then identifying the next best reciprocal blast hit with the other ingroup. therefore a true negative is essentially an ortholog triple which has been transformed into a false positive by introducing a less similar sequence for one of the species sequences. these true negatives represent the types of ortholog predictions that would result from an rbh-method in scenarios such as figure since we know that rbh can make incorrect predictions when a genome is incomplete or when gene loss has occurred, this analysis simulates what would occur with the rbh method in such cases. the benefit of this analysis is that we specifically know the true-negatives introduced, allowing us to examine how the ortholuge ratios for these true-negatives behave. for the e. coli-p. putida-p. syringae input ortholog groups, we constructed two true-negative data sets. in the first, we replaced p. putida genes with their next best rbh hit to p. syringae, resulting in ingroup paralogs. in the second, we replaced p. syringae genes with their next best rbh hit to p. putida, resulting in ingroup paralogs. for both, we conservatively introduced all possible paralogs into the analysis, resulting in roughly 50% of the genes converted to true-negatives. the results from these two data sets, show that these true-negatives overlap very little with the rbh-predicted orthologs or with the high quality orthologs. this demonstrates that even with all possible true paralogs simulated, very few of them are falling within the higher frequency ranges of the rbh distributions. we also constructed a third true-negative data set with all outgroup genes replaced by their next best rbh hit to both p. syringae and p. putida. the r × r plot shows that these true-negative cases plot at lower ratio and ratio values and do not separate well from what would be expected for true-orthologs. this is actually promising, since in the case of a paralog in an outgroup, the two ingroups should still be regarded as probable true orthologs and should still be falling within the main cluster of true-orthologs, as we observe. in other words, since the goal of ortholuge is to improve ortholog identification between the two ingroups, it is beneficial that an outgroup paralog does not generally interfere with/affect the analysis. ortholuge ratio cut-offs, to separate orthologs from paralogs, can be determined based on an iterative-true-negative analysis after determining that the introduced true-negatives almost never fall within certain ratio ranges, it became clear that ratio cut-offs could be derived to exclude most true-negatives, and thus improve the specificity of ortholog prediction. to do this, another strategy was employed to simulate the introduction of paralogs and then formulate ortholog identification cut-offs. this second strategy, involving an iterative-true-negative analysis, allows one to view the variance in proportion of true-negatives in a particular ratio range, and is also amenable to high throughput use for the formulation of cut-offs. for both the eukaryotic rbh-predicted data set, and the prokaryotic rbh-predicted data set, we conservatively modeled an incomplete genome scenario by randomly replacing 25% of the genes in the rbh-predicted data set with the "next best rbh" hit. this randomized introduction of true-negatives was iterated at least times, and each iteration was evaluated by ortholuge. the proportion of true-negative orthologs was averaged over all iterations and the standard deviation determined. we found that that once again, the ratio values of true-negative orthologs do not overlap well with those of the bulk of rbh-predicted orthologous groups. for both the prokaryotic and eukaryotic rbh-based data sets, this iterative true-negative analysis was used to determine ratio ranges where true paralogs were very unlikely to land and ranges where they were very likely to land. the borders of these ranges became the ratio cut-off values. this permitted classification of the rbh-predicted tentative orthologous groups into probable ssd-orthologs, probable paralogs, or "uncertain" categories. it should be noted that a more accurate name for the'probable paralog' category might be'probable non-ssd-ortholog,' because there may be true orthologs that have undergone unusual divergence in one ingroup species within this category. however, in such cases the non-ssd-orthologs may have functionally diverged, and therefore are cases that we would want to differentiate from our ssd-ortholog set. regardless, for ease of comprehension, we propose to call those cases with very atypical ratios "probable paralogs", since paralogs likely predominate in this region. we chose a 25% true-negative introduction, since this is likely above a worst-case scenario in terms of the number of genes that may be missing in an incomplete genome, or most cases of naturally occurring gene loss. we felt it was important to "saturate" the data set with true-negatives, because any given rbh-based dataset will likely contain some proportion of false-positives in the putative orthologous groups. therefore, to effectively identify the ranges where true-negatives were becoming increasingly more common we needed to observe a large proportion of true-negatives. however, we did not want to transform a data set with all possible true-negatives, as this would not provide a sense of the variation in proportion of true-negatives within a given ratio range. note that we also chose to report the results here for a transformation of an rbh-predicted data set with the true-negatives, rather than a transforming a high quality dataset, since the refseq based analysis could be more easily fully automated. however, transformation of a eukaryotic high quality dataset with true-negatives generated similar cut-off values. through an iterative sampling approach we were able to generate standard deviations of the proportion of true-negatives in a given ratio range, providing a clearer picture of the likelihood of a true-negative occurring in that range. ortholuge ratios in combination can help predict which gene in a given putative orthologous group is likely a paralog a closer inspection of the ortholuge ratios shows that they behave in a predictable fashion when the ortholog group contains one or more false-positives. for example, if ingroup is actually a paralog, then the distance between ingroup1-outgroup and the distance between ingroup1-ingroup would be larger than the norm for an ssd-ortholog. this would cause ratio to increase, and ratio to increase a slighter amount. conversely, if ingroup is actually the paralog, then ratio would be expected to increase and ratio to increase slightly. these predictable changes do indeed occur, as illustrated by an analysis of true-negatives, an analysis of a dataset of tentative orthologs identified by rbh using an incomplete genome, and an additional manual review of selected cases. we propose that when unusual ratio ranges are identified for a given orthologous group, the relative changes can facilitate predictions regarding which of the two ingroups may contain a paralog. note that an outgroup paralog cannot be well predicted, however this does not affect the utility of ortholuge, since the method is focused on characterizing the orthology of the two ingroups. it should also be noted that multiple-paralog scenarios, are more complex. though relatively easy to predict on paper, they are more difficult to distinguish in reality, because the amount of divergence for the two paralogs may vary greatly. in most cases they would resemble one of the first three scenarios, depending on which of the two paralogs was more diverged. nevertheless, in the end, these rare cases will still most frequently display atypical ratios, and will not fall within probable ortholog cut-offs. ortholuge in action: an estimation of probable ssd-orthologs and probable paralogs in rbh-based data sets an example of ratio cut-offs generated based on our true-negative analysis is listed in table. researchers are of course encouraged to choose their own cut-off to suit their needs. however, based on our simulations, these cut-offs should effectively differentiate probable orthologs and paralogs for these data sets. we also propose that these cut-offs can identify those orthologs most closely following species divergence – orthologs which may be more functionally similar to each other versus those that have diverged at different evolutionary rates in each species. using the derived ratio cut-offs, we have constructed several data sets of probable ssd-orthologs consisting of: mouse-rat comparisons, and one for a p. putida-p. syringae comparison. these ssd-orthologs are particularly suited for comparative genomics analyses. in addition, notations are added to all the data analysed, indicating cases of probable gene duplication after species divergence – a scenario that can increase the likelihood of functional divergence of the genes. these higher quality sets of orthologs can be found via the ortholuge website. the proportion of ssd-orthologs in the rbh-predicted data sets is summarized in table note that cases of in-paralogs are not counted within the counts of ssd-orthologs in table such cases, due to their uncertain potential to have diverged in function because of a gene duplication, are counted within the "uncertain" category. using the cut-offs, we were also able to estimate the proportion of rbh-predicted orthologs that are likely paralogs for these eukaryotic and prokaryotic data sets. for the prokaryotic data about 5% of rbh-based predictions are probable paralogs. for the eukaryotic data, about 10% of the rbh-predictions are probable paralogs. these are significant numbers that validate the need for a method like ortholuge, particularly if one is trying to use rbh-predicted orthologs for downstream analyses that require stringent ortholog prediction. application of these cut-offs to classify the curated eukaryote and prokaryote datasets suggest that the false negative rate in is in the range of % for prokaryote data and 3% for the eukaryote data. to facilitate the analysis of other datasets, we have developed ortholuge software that can be used to characterize any existing dataset of orthologs. if no pre-existing ortholog dataset is available, ortholuge can also construct such a dataset using an rbh-based approach applied to whole genome datasets. ortholuge was developed using perl under linux and operates in any unix environment, provided all the needed tools are available for the user's operating system. this freely available, open source, software is available on the ortholuge website. discussion for cross-genome comparison purposes, researchers often wish to compare orthologs – in particular orthologs that have not undergone unusual divergence rates relative to one another, and have more likely retained similar function. we propose that ortholuge is an approach, suitable for high-throughput genome-scale analysis, which aids identification of such orthologs. the ortholuge method significantly improves the specificity/precision of high-throughput rbh-based ortholog analysis. for example, our results indicate that roughly in rbh-predicted rat-mouse orthologs are very likely paralogs, and about in rbh-predicted orthologs for two pseudomonas species are similarly likely incorrect. note that our rbh analysis requires transitivity between three species, rendering it more stringent than the typical rbh analysis between two species. this suggests that the typical rbh analysis may have an even greater number of false predictions. the resulting more specific identification of orthologs by ortholuge is an important requirement for many downstream analyses, such as identifying gene regulatory regions, or characterizing differences in microarray-measured gene expression responses across species. an automated method such as ortholuge is of course no substitute for a more manual, comprehensive phylogenetic analysis and has some limitations as mentioned below. however, its simplicity and utility for high-throughput analyses suggest that it is a useful complement to rbh-based identification of putative orthologs using whole-genome gene datasets. in addition, ortholuge's higher specificity approach can complement other methods that may provide a higher sensitivity/recall approach for ortholog identification. ortholuge evaluates orthologs through phylogenetic distance comparisons. to perform such comparisons, an outgroup is required to assist the prediction of orthologs between the two ingroups – this has simultaneous advantages and disadvantages. the added sequence provides extra resolution and extra specificity; however, a distant outgroup may lessen the sensitivity of the approach. presumably, though, as more genomes are sequenced, the number of possible outgroups available to choose from will increase and very distant outgroups will become less of a problem. the ortholuge pipeline generates predictions by evaluating the entire genome at once. the more data points that are representative of the genome, the more confident the ratio cut-offs will be. it assumes that the majority of incoming predictions are true orthologs, will exhibit expected ratios, and will thus form the high frequency ranges of the distributions. our analysis does suggest this assumption to be reasonable and, notably, both eukaryotic and bacterial orthologs display similar ratio distributions, despite marked differences regarding how such organisms evolve. once the genome-wide predictions are made for a certain species combination, ortholuge can be used to estimate how likely it is that a specific putative orthologous group contains a true-negative within its ingroups. in such cases, we can match these ratios with a category, to suggest which gene in the ortholog group is likely to be the paralog. however, it should be emphasized that at this time we have not exhaustively examined all possible scenarios, and so such analysis should be taken as a guide requiring further investigation. interestingly, this method also appears to be useful to examine, in a genome-wide scale, the relationships between species. by examining the ratio values at the highest frequency ranges in the histograms, one can easily determine which two of any three organisms are more similar to each other, on average, and on a genome-wide scale. the simplicity of ortholuge allows for many benefits. for example, it can easily be re-run when genome annotations undergo significant changes. in addition, it can easily be customized with any method of sequence alignment or phylogenetic distance calculation, depending upon the researcher's preference. it is expected that further analysis will reveal relationships between true-negative analyses and ratio cut-off generation, negating the need to perform a full iterative-introduced-true-negative analysis for each species comparison. of course, users can choose their own ortholuge ratio cut-offs, either using a true-negative analysis, or another approach of their choice, for identification of orthologs at their preferred level of specificity. accepting only orthologs in a certain ratio range and discarding the rest will certainly eliminate a small fraction of true orthologs from the input set. for example, if the probable paralog cut-offs are applied to the "high quality" curated prokaryotic and eukaryotic data sets, we eliminate % and 3% of the prokaryotic and eukaryotic predictions, respectively. however, if the more stringent ssd-ortholog cut-offs are applied, we eliminate % and 8% of the predictions, respectively. while these outliers may be false-positives in the curated data, they may also be true orthologs that have undergone unusual divergence in one ingroup species. for example, if a gene duplication occurred in one ingroup species after the speciation divergence, the resulting duplicated gene may undergo accelerated evolution. such scenarios would result in skewed ratios for true orthologs. however, we propose that such orthologs with unusual divergence may more likely have differing function at some level. in many genome-wide studies involving comparisons between species, researchers wish to identify those genes that are more likely to be functionally equivalent – i.e. orthologs that did not experience unusual rates of evolution or gene transfer. ortholuge improves the identification of such "supporting species divergence" ortholog pairs. this is apparently an important issue, as illustrated by some confusion occurring in the literature regarding the definition of orthologs. the definition that we, and many evolutionary biologists use, is the one initially proposed that describes orthologs as genes that have diverged due to speciation. however, the term ortholog is increasingly being inferred to mean'functionally equivalent genes in different species' – a common misconception. while we and others agree that orthologs tend to have similar function, this is not a requirement for orthology. so, it appears that while many researchers are identifying orthologs in a genetic or genomic study, what they really wish to identify is the subset of orthologs that are specifically functionally equivalent. some methods, such as the widely used inparanoid, refer to all in-paralogs in the one species as orthologous to the related gene in the other species. they do not clearly distinguish between such cases of in-parology and more simple one-to-one orthologous relationships. we believe that such cases should be differentiated because a duplication event after species divergence may have led to significant functional divergence of one or both of the duplicated genes in the one species. in ortholuge, cases involving possible in-paralogs are flagged using a simple analysis that focuses on detecting the most clear-cut in-paralog cases. for our analysis, we did apply ratio cutoffs derived using one-to-one ortholog rbh-based data to classify a same species data set that includes both one-to-one orthologs and many-to-many orthologs. however, we recognize a need to implement more robust procedures that would consider all cases of suspected recent gene duplications in the analysis. it would also be desirable to complement this analysis further by noting cases of relative gene rearrangement in the input set of orthologs. ortholuge in its current form cannot detect gene rearrangements, however it could potentially complement other bioinformatics approaches that detect such rearrangements. ortholuge could also be adapted to contain a gene rearrangement analysis that is customized to its methodology. these additional ortholog evolutionary scenarios, involving possible in-paralogy or gene rearrangements, should be specifically noted because they cannot be distinguished by examining ortholuge distance ratios alone. they require further study in any comparative analysis, since functional equivalence between the orthologs is less likely. regarding the limitations of this method, it should be emphasized that ortholuge is limited by the quality of the initial ortholog-analysis. ortholuge is also only as good as the quality of the sequence data being analyzed. we have tested our alignment trimming and masking of regions of lower alignment quality extensively to improve the critical sequence alignment component of our method; however, certainly this method will fail if low quality sequences, with many errors, are used in the analysis. in addition, the top blast hit is not necessarily the nearest neighbour and so true orthologs may be missed when using ortholuge after initially identifying orthologs with an rbh-based approach. ortholuge could therefore improve if the initial ortholog prediction method is improved. regardless of any limitations, ortholuge appears to effectively improve the specificity of ortholog identification and is suitable for high-throughput, genome-wide use. given the amount of genomics data being obtained at this time, such specific, high-throughput approaches will become increasingly necessary, as genomics research moves further toward more multi-genome comparative analyses.  ortholuge improves the specificity of ortholog identification and is suitable for high-throughput use. this precise ortholog prediction method complements other ortholog prediction methods that are not focused on precision and it potentially identifies those orthologs most likely to be functionally similar. the ortholuge method provides important data set evaluation for a variety of analyses based on comparative approaches, including gene function prediction, prediction of conserved regulatory elements, and comparative analysis of gene order or gene/protein expression data. 
one problem with rbh is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. through simulations of incomplete genome data/gene loss, we show that the vast majority of genes falsely predicted as orthologs by an rbh-based method can be identified. ortholuge analyzes phylogenetic distance ratios involving two comparison species and an outgroup species, noting cases where relative gene divergence is atypical. orthologs tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. this method, and its associated software, will aid those performing various comparative genomics-based analyses, such as the prediction of conserved regulatory elements upstream of orthologous genes. the ortholuge method reported here appears to significantly improve the specificity of high-throughput ortholog prediction for both bacterial and eukaryotic species. these datasets, as well as ortholuge software that may be used to characterize other species' datasets, are available at. to address these issues, we present here a high-throughput computational method named ortholuge that further evaluates previously predicted orthologs – identifying which orthologs most closely reflect species divergence and may more likely have similar function. it also identifies some cases of gene duplication after species divergence. the gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis; therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-blast-hits approach. in addition, there is an increasing interest in identifying orthologs most likely to have retained similar function. ortholuge was then used to estimate the number of false-positives in selected rbh-predicted ortholog datasets, identifying approximately 10% paralogs in a eukaryotic data set and 5% in a bacterial data set.
 orthologs tend to have similar function, and so their prediction has become an important component of comparative genomics and genome annotation. the gold standard phylogenetic analysis approach of comparing available organismal phylogeny to gene phylogeny is not easily automated for genome-wide analysis; therefore, ortholog prediction for large genome-scale datasets is typically performed using a reciprocal-best-blast-hits approach. one problem with rbh is that it will incorrectly predict a paralog as an ortholog when incomplete genome sequences or gene loss is involved. in addition, there is an increasing interest in identifying orthologs most likely to have retained similar function.  to address these issues, we present here a high-throughput computational method named ortholuge that further evaluates previously predicted orthologs – identifying which orthologs most closely reflect species divergence and may more likely have similar function. ortholuge analyzes phylogenetic distance ratios involving two comparison species and an outgroup species, noting cases where relative gene divergence is atypical. it also identifies some cases of gene duplication after species divergence. through simulations of incomplete genome data/gene loss, we show that the vast majority of genes falsely predicted as orthologs by an rbh-based method can be identified. ortholuge was then used to estimate the number of false-positives in selected rbh-predicted ortholog datasets, identifying approximately 10% paralogs in a eukaryotic data set and 5% in a bacterial data set. higher quality datasets of orthologs, which we term "ssd-orthologs", were also constructed. these datasets, as well as ortholuge software that may be used to characterize other species' datasets, are available at.  the ortholuge method reported here appears to significantly improve the specificity of high-throughput ortholog prediction for both bacterial and eukaryotic species. this method, and its associated software, will aid those performing various comparative genomics-based analyses, such as the prediction of conserved regulatory elements upstream of orthologous genes. 