BACKGROUND
an important problem in genome annotation is the identification and characterization of functional elements.
these elements include transcription factor binding sites , which are short, degenerate sequences that appear frequently in the genome.
the interactions between transcription factors  and their respective binding sites are critical for regulating gene expression.
to characterize binding sequences for a tf, computational methods search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest .
for many motif-finding methods, it is necessary to input upstream sequences from a set of genes , with the assumption that a common motif is shared by the sequences .
however, upstream sequences of genes included in this set may not have an occurrence of the same motif, or genes that have the occurrence of the motif in their upstream sequence may not be identified in the co-expressed set.
to address these weaknesses, correlation-based motif finding methods  <cit>  have been developed that do not rely on a pre-determined set of genes either based on co-expression  or over-representation of motifs as in  <cit> .
using all genes from a single experiment, oligos in a specified length range are enumerated in their upstream sequence and tested for significant correlation with expression values or genome-wide location measurements for a particular tf.
the correlation-based motif finding approach was introduced in the "regulatory element detection using correlation with expression"  software  <cit>  using a linear regression framework and has since been adapted in several ways including the use of scores to motifs instead of oligo counts  <cit> , probabilistic representations of motifs  <cit> , binary indicators for word occurrences  <cit>  and flexible non-linear regression functions  <cit> .
an alternative motif-finding strategy, relying on the availability of complete genomes from related species, has made it possible to search for putative tfbs in evolutionarily conserved sequences.
it has been shown that for closely related species, where reasonable alignment of the orthologous promoter sequences can be achieved, the binding sites for many tfs are evolutionarily conserved.
different computational methods have been developed that vary in the number and diversity of species investigated, in search strategies, i.e.
genome-wide  versus gene sets , in whether they use known transcription factors motifs  or predict motifs de novo , in how they integrate inter-species conservation with intra-species conservation , in whether the alignment of the motif occurrences across species is required  and in whether global alignments in orthologous sequences are necessary  <cit> .
in summary, there are numerous motif finding methods that fall into several different classes, including those reviewed that are correlation or sequence-conservation based.
because of their successes individually, in this work, we describe a new method for predicting motifs that combines these two strategies.
due to the variability in tf-dna interactions, tfbs are characterized by motifs containing degenerate positions.
for example, the second position in the consensus tfbs for the yeast transcription factor opi <dig>  can be a or g, which is denoted by the iupac symbol r. at a functional tfbs, the possible substitutions at a position may be observed in aligned sequences from multiple species.
for example, an opi <dig> functional site may be fully conserved across species  or exhibit a or g at the second position for different species.
to search for degenerate motifs, we have developed an adaptation of the correlation-based algorithm reduce  <cit>  called conservation-reduce .
in c-reduce, a multiple species alignment is generated and then translated into a consensus pattern using degenerate nucleotide symbols that capture the variation at each position across species.
all oligos, including those with degenerate symbols, are then evaluated for significant correlation.
by using multiple species data, we can identify motifs that may be missed by reduce, which only examines sequences from a single species and requires exactly the same oligo in different sequences.
an alternative method for identifying degenerate motifs is fast-reduce   <cit> , which was developed for single species data and identifies degenerate motifs through an enumerative approach.
however, enumeration of degenerate motifs can become very costly as the length of the motif and number of degenerate positions increases.
in contrast, c-reduce reduces the search space of degenerate motifs by taking into account the variability at a position inferred from evolutionary information.
in summary, c-reduce benefits from the use of conservation in two ways.
first, it predicts degenerate motifs, but reduces the search space by only focusing on naturally occurring degeneracies that appear across multiple species.
second, by examining sequences from multiple species, it will discount chance matches of a motif in a single species if it the match has a highly degenerate consensus sequence in the multiple species alignment.
the degeneracy of the consensus, reflecting random mutations in other species, makes a functional tfbs at that position less likely.
to predict transcription factor binding site motifs, our method is evaluated on chip-chip  data in yeast and gene expression data in drosophila.
we find that the conservation and correlation-based approaches perform better in combination than they do individually.
RESULTS
c-reduce applied to yeast data
c-reduce was first applied to the  <dig> genome-wide location data sets of  <dig> tfs where six other methods failed to identify the motif specified in the literature for that tf  <cit> .
these six alternative methods were applied to sets of sequences that were determined to be significantly enriched for tf binding.
two of the six methods also incorporated sequence conservation.
in comparison, c-reduce uses upstream sequences from the entire set of genes and incorporates conservation information.
the results for both c-reduce and f-reduce  are displayed in tables  <dig> and  <dig>  for  <dig> of the  <dig> transcription factors, c-reduce identified the specified motif in at least one of the conditions, while f-reduce discovered the correct motif for  <dig> transcription factors.
in many cases, both programs were successful, but f-reduce often discovered a shorter or more degenerate motif than c-reduce.
both programs are not suitable for finding long motifs with dimer patterns.
therefore, some of the missed cases were for tfs such as gal <dig>  with the motif cggnccg.
the  <dig> transcription factor  motifs not discovered by the methods applied in  <cit>  are listed in tables  <dig> and  <dig>  only exact matches to the motifs  are considered.
the first and second columns list the transcription factor and known motif given in the supplementary table  <dig> file from  <cit> .
the third column lists the environmental conditions examined .
the fourth and fifth columns list the results for c-reduce and f-reduce respectively.
for example, "1: attggy  <dig> " for hap <dig>  indicates that the oligo attggy was the first predicted oligo with a -log <dig> of  <dig>  under the ypd condition.
the degenerate symbols are r = , y = , m = , k = , s = , w = , b = , d = , h = , v =  and n = .
see table  <dig> for details.
we also ran c-reduce on the complete set of  <dig> tfs given in  <cit> .
we separated the complete set into the  <dig> tfs with motifs that were recovered by one of the programs applied by  <cit>   and the  <dig> from above that were not recovered by any of their programs  .
while we were unable to find some of the recovered motifs, c-reduce, using exact matches, performed much better on the not-recovered set for a total correct prediction rate of ~65% compared to ~54% in harbison et al.
<cit>  .
we also relaxed our matching criteria to allow for one mismatch or one shifted match .
for example, in harbison et al.
<cit> , although there was one mismatch, the predicted motif "cacatgc" was considered a successful prediction for the known ino <dig> motif "atttcacatc".
we discovered the motif "tcacatg", very similar to their predicted "cacatgc", but because of the last position "g" it was not considered an exact match.
therefore, relaxing our criteria to allow for one mismatch or one shifted position, we were able to improve our correct prediction rate to 79% .
for each transcription factor motif, c-reduce results are listed for the complete harbison et al.
<cit>  data using either exact matches or at most one mismatch or shifted match " <dig> mm/s" .
comparisons with other programs
we compared c-reduce with four other programs that also use multiple species data for motif prediction on the more difficult "not-recovered" set from harbison et al.
<cit> .
all methods we evaluated, phylocon  <cit> , converge  <cit> , phylogibbs  <cit>  and tree gibbs sampler  <cit> , are designed to be applied to orthologous sequences from a subset of all genes.
phylogibbs and tree gibbs sampler also incorporate the phylogenetic relationships among the species into their search.
although sequence conservation is incorporated into these methods, they differ from c-reduce in that they use a sequence set approach, where only sequences with the most significant tf binding enrichment are used rather than all sequences.
in the phylogibbs program  <cit> , there were a total of  <dig> tf data sets in the "not-recovered" list where we could compare our results and in those cases, both programs made  <dig> correct predictions, although not always for the same tf motif .
in comparison to the method of tree gibbs sampler  <cit> , which was applied to a subset of the  <dig> "not-recovered" tfs, c-reduce with  <dig> mm/s made more correct predictions and less false positive predictions .
finally, for converge and phylocon  <cit>  results for almost all of the "not-recovered" cases were reported in their "additional file 2".
c-reduce was able to correctly predict ~65% of the motifs, while phylocon and converge predicted ~26% and ~41% respectively .
only when their predictions were combined did these two programs have more similar accuracy to c-reduce.
the sub-tables list comparisons between c-reduce and several other methods.
the total number of transcription factor datasets evaluated  is not the same in each sub-table because results are not always reported for the complete  <dig> "not-recovered" set.
for tree gibbs sampler, the authors report all motif predictions and the false positive rates can be compared with c-reduce.
for that sub-table, "true positives" indicates the number of correct predictions and "false positives" indicates the number of incorrect predictions out of all predictions.
there are some caveats regarding the comparisons between methods.
we use reported successes by the authors since motif predictions were not always provided and the evaluation criteria, typically for predicted position weight matrices, were usually quite different from those for the c-reduce oligo predictions.
for tree gibbs sampler  <cit> , we could make a more direct comparison because the authors provided all consensus motifs for their predictions and their reported evaluation criteria could be used to compare the c-reduce oligos with the known motif.
however, the tree gibbs sampler evaluation may be at a disadvantage to c-reduce because degenerate symbols were not used to construct their consensus motifs.
because of this, the tree gibbs sampler incorrect predictions were manually checked with a more relaxed criteria allowing for more mismatches, but the results in table  <dig> did not change.
application to insect data
to investigate the performance of c-reduce on more diverse species, we applied our method to insect data.
to predict the binding site motif for the drosophila  transcription factor dorsal, we applied c-reduce to data from a microarray study on dorsal targets  <cit>  and  <dig> kilobase upstream sequences from four insect species .
dorsal is important for the initiation of tissue differentiation in the early embryo and many of its target genes are sequence-specific transcription factors.
the experiment in  <cit>  determined genome-wide expression levels comparing mutant embryos that contain no dorsal protein  or uniformly high levels  or low levels  of dorsal throughout the embryo.
known dorsal binding sites are represented by the consensus sequences gggwwwwccm or gggwdwwwccm  <cit> .
table  <dig> summarizes the results of running c-reduce on the dorsal data.
for all three comparisons  the top predicted oligo matched the consensus dorsal binding sites using exact matches.
reduce, without the conservation information, was unable to predict this binding site, although it did predict the motif acccc for high vs. none  and high vs. none  but it is not an exact match to the known motif.
the first column shows the pair-wise mutant comparisons .
columns 2– <dig> list predicted oligos that match the dorsal motif, their rank and -log <dig> respectively.
positions in bold indicate matches to the flanking ggg/ccc or kgg/ccm part of the consensus dorsal motifs .
discussion
c-reduce is a straightforward adaptation of the reduce software.
it uses the same algorithm but requires consensus sequences from multi-species alignments and an initial enumeration of oligos to remove those that have more than two degenerate symbols from the search process.
because it takes advantage of the speed in reduce, it is well suited for searching long sequences.
in contrast, programs such as tree gibbs sampler have been limited to testing at most  <dig> kilobase regions.
recently, sequence conservation data was used to enhance matrixreduce  <cit> , an adaptation of the reduce algorithm, but not for the purpose of de novo motif finding, which is the focus here.
c-reduce in its current form has some limitations.
some transcription factors motifs were not predicted by c-reduce, possibly due to dimer patterns that are not well characterized by this method, weak evidence for this motif in the literature, or low quality of the genome-wide location data.
c-reduce is not well suited for finding very degenerate motifs; we only allow  <dig> degenerate symbols total.
it is also not well suited for searching very long motifs  because of the small sample size associated with matches to the longer motifs.
c-reduce also has some limitations in particular for higher eukaryotes.
it requires global alignments of the sequences, expects aligned binding sites within the global alignment, and does not explicitly model cis-regulatory modules.
despite these limitations, it was successfully applied to a difficult insect data set.
the yeast data set is a common benchmark test set and consists of saccharomyces sensu stricto species which are only separated by ~ <dig> mya  <cit> .
for a more challenging prediction problem, we applied c-reduce to insect data with species separated by ~ <dig> mya   <cit> .
in higher eukaryotes, tfbs are often dispersed in long intergenic regions so it is important to consider a longer upstream sequence region.
however, tfbs are typically less than  <dig> base pairs, so increasing the sequence search space decreases the signal to noise ratio making the search problem more difficult.
despite these challenges, c-reduce correctly predicted the dorsal motif in all three gene expression comparison experiments.
CONCLUSIONS
c-reduce, which relies on sequence conservation and a correlation-based strategy across all gene upstream sequences, shows improved performance on a comprehensive genome-wide location dataset for yeast.
our comparisons to f-reduce, which does not use sequence conservation, and to several other programs that use conservation but only on sequences from gene subsets, indicate that the combination of these two approaches yields more predictive power.
c-reduce can be used to find degenerate motifs but instead of relying on exhaustive searches with degenerate symbols, which can quickly become intractable, it limits the search by taking advantage of evolutionary information and discounts false occurrences of motifs that are not evolutionarily conserved.

 an important problem in genome annotation is the identification and characterization of functional elements. these elements include transcription factor binding sites, which are short, degenerate sequences that appear frequently in the genome. the interactions between transcription factors and their respective binding sites are critical for regulating gene expression. to characterize binding sequences for a tf, computational methods search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. for many motif-finding methods, it is necessary to input upstream sequences from a set of genes, with the assumption that a common motif is shared by the sequences. however, upstream sequences of genes included in this set may not have an occurrence of the same motif, or genes that have the occurrence of the motif in their upstream sequence may not be identified in the co-expressed set. to address these weaknesses, correlation-based motif finding methods have been developed that do not rely on a pre-determined set of genes either based on co-expression or over-representation of motifs as in. using all genes from a single experiment, oligos in a specified length range are enumerated in their upstream sequence and tested for significant correlation with expression values or genome-wide location measurements for a particular tf. the correlation-based motif finding approach was introduced in the "regulatory element detection using correlation with expression" software using a linear regression framework and has since been adapted in several ways including the use of scores to motifs instead of oligo counts, probabilistic representations of motifs, binary indicators for word occurrences and flexible non-linear regression functions. an alternative motif-finding strategy, relying on the availability of complete genomes from related species, has made it possible to search for putative tfbs in evolutionarily conserved sequences. it has been shown that for closely related species, where reasonable alignment of the orthologous promoter sequences can be achieved, the binding sites for many tfs are evolutionarily conserved. different computational methods have been developed that vary in the number and diversity of species investigated, in search strategies, i.e. genome-wide versus gene sets, in whether they use known transcription factors motifs or predict motifs de novo, in how they integrate inter-species conservation with intra-species conservation, in whether the alignment of the motif occurrences across species is required and in whether global alignments in orthologous sequences are necessary. in summary, there are numerous motif finding methods that fall into several different classes, including those reviewed that are correlation or sequence-conservation based. because of their successes individually, in this work, we describe a new method for predicting motifs that combines these two strategies. due to the variability in tf-dna interactions, tfbs are characterized by motifs containing degenerate positions. for example, the second position in the consensus tfbs for the yeast transcription factor opi can be a or g, which is denoted by the iupac symbol r. at a functional tfbs, the possible substitutions at a position may be observed in aligned sequences from multiple species. for example, an opi functional site may be fully conserved across species or exhibit a or g at the second position for different species. to search for degenerate motifs, we have developed an adaptation of the correlation-based algorithm reduce called conservation-reduce. in c-reduce, a multiple species alignment is generated and then translated into a consensus pattern using degenerate nucleotide symbols that capture the variation at each position across species. all oligos, including those with degenerate symbols, are then evaluated for significant correlation. by using multiple species data, we can identify motifs that may be missed by reduce, which only examines sequences from a single species and requires exactly the same oligo in different sequences. an alternative method for identifying degenerate motifs is fast-reduce, which was developed for single species data and identifies degenerate motifs through an enumerative approach. however, enumeration of degenerate motifs can become very costly as the length of the motif and number of degenerate positions increases. in contrast, c-reduce reduces the search space of degenerate motifs by taking into account the variability at a position inferred from evolutionary information. in summary, c-reduce benefits from the use of conservation in two ways. first, it predicts degenerate motifs, but reduces the search space by only focusing on naturally occurring degeneracies that appear across multiple species. second, by examining sequences from multiple species, it will discount chance matches of a motif in a single species if it the match has a highly degenerate consensus sequence in the multiple species alignment. the degeneracy of the consensus, reflecting random mutations in other species, makes a functional tfbs at that position less likely. to predict transcription factor binding site motifs, our method is evaluated on chip-chip data in yeast and gene expression data in drosophila. we find that the conservation and correlation-based approaches perform better in combination than they do individually.  c-reduce applied to yeast data c-reduce was first applied to the genome-wide location data sets of tfs where six other methods failed to identify the motif specified in the literature for that tf. these six alternative methods were applied to sets of sequences that were determined to be significantly enriched for tf binding. two of the six methods also incorporated sequence conservation. in comparison, c-reduce uses upstream sequences from the entire set of genes and incorporates conservation information. the results for both c-reduce and f-reduce are displayed in tables and for of the transcription factors, c-reduce identified the specified motif in at least one of the conditions, while f-reduce discovered the correct motif for transcription factors. in many cases, both programs were successful, but f-reduce often discovered a shorter or more degenerate motif than c-reduce. both programs are not suitable for finding long motifs with dimer patterns. therefore, some of the missed cases were for tfs such as gal with the motif cggnccg. the transcription factor motifs not discovered by the methods applied in are listed in tables and only exact matches to the motifs are considered. the first and second columns list the transcription factor and known motif given in the supplementary table file from. the third column lists the environmental conditions examined. the fourth and fifth columns list the results for c-reduce and f-reduce respectively. for example, "1: attggy " for hap indicates that the oligo attggy was the first predicted oligo with a -log of under the ypd condition. the degenerate symbols are r =, y =, m =, k =, s =, w =, b =, d =, h =, v = and n =. see table for details. we also ran c-reduce on the complete set of tfs given in. we separated the complete set into the tfs with motifs that were recovered by one of the programs applied by and the from above that were not recovered by any of their programs. while we were unable to find some of the recovered motifs, c-reduce, using exact matches, performed much better on the not-recovered set for a total correct prediction rate of ~65% compared to ~54% in harbison et al.. we also relaxed our matching criteria to allow for one mismatch or one shifted match. for example, in harbison et al., although there was one mismatch, the predicted motif "cacatgc" was considered a successful prediction for the known ino motif "atttcacatc". we discovered the motif "tcacatg", very similar to their predicted "cacatgc", but because of the last position "g" it was not considered an exact match. therefore, relaxing our criteria to allow for one mismatch or one shifted position, we were able to improve our correct prediction rate to 79%. for each transcription factor motif, c-reduce results are listed for the complete harbison et al. data using either exact matches or at most one mismatch or shifted match " mm/s". comparisons with other programs we compared c-reduce with four other programs that also use multiple species data for motif prediction on the more difficult "not-recovered" set from harbison et al.. all methods we evaluated, phylocon, converge, phylogibbs and tree gibbs sampler, are designed to be applied to orthologous sequences from a subset of all genes. phylogibbs and tree gibbs sampler also incorporate the phylogenetic relationships among the species into their search. although sequence conservation is incorporated into these methods, they differ from c-reduce in that they use a sequence set approach, where only sequences with the most significant tf binding enrichment are used rather than all sequences. in the phylogibbs program, there were a total of tf data sets in the "not-recovered" list where we could compare our results and in those cases, both programs made correct predictions, although not always for the same tf motif. in comparison to the method of tree gibbs sampler, which was applied to a subset of the "not-recovered" tfs, c-reduce with mm/s made more correct predictions and less false positive predictions. finally, for converge and phylocon results for almost all of the "not-recovered" cases were reported in their "additional file 2". c-reduce was able to correctly predict ~65% of the motifs, while phylocon and converge predicted ~26% and ~41% respectively. only when their predictions were combined did these two programs have more similar accuracy to c-reduce. the sub-tables list comparisons between c-reduce and several other methods. the total number of transcription factor datasets evaluated is not the same in each sub-table because results are not always reported for the complete "not-recovered" set. for tree gibbs sampler, the authors report all motif predictions and the false positive rates can be compared with c-reduce. for that sub-table, "true positives" indicates the number of correct predictions and "false positives" indicates the number of incorrect predictions out of all predictions. there are some caveats regarding the comparisons between methods. we use reported successes by the authors since motif predictions were not always provided and the evaluation criteria, typically for predicted position weight matrices, were usually quite different from those for the c-reduce oligo predictions. for tree gibbs sampler, we could make a more direct comparison because the authors provided all consensus motifs for their predictions and their reported evaluation criteria could be used to compare the c-reduce oligos with the known motif. however, the tree gibbs sampler evaluation may be at a disadvantage to c-reduce because degenerate symbols were not used to construct their consensus motifs. because of this, the tree gibbs sampler incorrect predictions were manually checked with a more relaxed criteria allowing for more mismatches, but the results in table did not change. application to insect data to investigate the performance of c-reduce on more diverse species, we applied our method to insect data. to predict the binding site motif for the drosophila transcription factor dorsal, we applied c-reduce to data from a microarray study on dorsal targets and kilobase upstream sequences from four insect species. dorsal is important for the initiation of tissue differentiation in the early embryo and many of its target genes are sequence-specific transcription factors. the experiment in determined genome-wide expression levels comparing mutant embryos that contain no dorsal protein or uniformly high levels or low levels of dorsal throughout the embryo. known dorsal binding sites are represented by the consensus sequences gggwwwwccm or gggwdwwwccm. table summarizes the results of running c-reduce on the dorsal data. for all three comparisons the top predicted oligo matched the consensus dorsal binding sites using exact matches. reduce, without the conservation information, was unable to predict this binding site, although it did predict the motif acccc for high vs. none and high vs. none but it is not an exact match to the known motif. the first column shows the pair-wise mutant comparisons. columns 2– list predicted oligos that match the dorsal motif, their rank and -log respectively. positions in bold indicate matches to the flanking ggg/ccc or kgg/ccm part of the consensus dorsal motifs. discussion c-reduce is a straightforward adaptation of the reduce software. it uses the same algorithm but requires consensus sequences from multi-species alignments and an initial enumeration of oligos to remove those that have more than two degenerate symbols from the search process. because it takes advantage of the speed in reduce, it is well suited for searching long sequences. in contrast, programs such as tree gibbs sampler have been limited to testing at most kilobase regions. recently, sequence conservation data was used to enhance matrixreduce, an adaptation of the reduce algorithm, but not for the purpose of de novo motif finding, which is the focus here. c-reduce in its current form has some limitations. some transcription factors motifs were not predicted by c-reduce, possibly due to dimer patterns that are not well characterized by this method, weak evidence for this motif in the literature, or low quality of the genome-wide location data. c-reduce is not well suited for finding very degenerate motifs; we only allow degenerate symbols total. it is also not well suited for searching very long motifs because of the small sample size associated with matches to the longer motifs. c-reduce also has some limitations in particular for higher eukaryotes. it requires global alignments of the sequences, expects aligned binding sites within the global alignment, and does not explicitly model cis-regulatory modules. despite these limitations, it was successfully applied to a difficult insect data set. the yeast data set is a common benchmark test set and consists of saccharomyces sensu stricto species which are only separated by ~ mya. for a more challenging prediction problem, we applied c-reduce to insect data with species separated by ~ mya. in higher eukaryotes, tfbs are often dispersed in long intergenic regions so it is important to consider a longer upstream sequence region. however, tfbs are typically less than base pairs, so increasing the sequence search space decreases the signal to noise ratio making the search problem more difficult. despite these challenges, c-reduce correctly predicted the dorsal motif in all three gene expression comparison experiments.  c-reduce, which relies on sequence conservation and a correlation-based strategy across all gene upstream sequences, shows improved performance on a comprehensive genome-wide location dataset for yeast. our comparisons to f-reduce, which does not use sequence conservation, and to several other programs that use conservation but only on sequences from gene subsets, indicate that the combination of these two approaches yields more predictive power. c-reduce can be used to find degenerate motifs but instead of relying on exhaustive searches with degenerate symbols, which can quickly become intractable, it limits the search by taking advantage of evolutionary information and discounts false occurrences of motifs that are not evolutionarily conserved. 
computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. in this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes. our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. c-reduce has been tested on chip-chip data for yeast transcription factors and on gene expression data in drosophila. our method, c-reduce, can account for variability at a motif position inferred from evolutionary information.
 computational methods for characterizing novel transcription factor binding sites search for sequence patterns or "motifs" that appear repeatedly in genomic regions of interest. correlation-based motif finding strategies are used to identify motifs that correlate with expression data and do not rely on promoter sequences from a pre-determined set of genes.  in this work, we describe a method for predicting motifs that combines the correlation-based strategy with phylogenetic footprinting, where motifs are identified by evaluating orthologous sequence regions from multiple species. our method, c-reduce, can account for variability at a motif position inferred from evolutionary information. c-reduce has been tested on chip-chip data for yeast transcription factors and on gene expression data in drosophila.  our results indicate that utilizing sequence conservation information in addition to correlation-based methods improves the identification of known motifs. 