protein sequences computing a pairwise correlation among motifs for each data set and computing the variation between the data sets to identify one or more motifs that are conserved in a given data set and thus correlate with that data set 39 s phenotype application_number US 201515128405 A description the computational methods utilized in this study allow for detection of sequence sites with high variability between two data sets of similar protein sequences but with different phenotypes in one embodiment these methods are applied to the study of hpvs previously studied sequence comparison techniques examined the phylogeny of sequences within a set but are limited in revealing variation between sequences or data sets for instance in the context of hpvs previous comparative genomics studies would either focus on one or two genes primarily the known oncogenes e6 amp e7 or investigate a few hpv types at a time commonly hpv16 hpv18 and hpv45 the bioinformatics methodology utilized herein provides a systematic comprehensive and unsupervised approach for determining regions in the hpv proteome that contribute toward carcinogenesis statistically significant motifs indicate variation between hr high risk and lr low risk types in their respective regions of the proteome these areas can then be viewed as sites that potentially contribute toward oncogenesis and can be evaluated in light of putative function of protein regions this approach also can be generalized for identifying variation between two different data sets the utilization of the methods herein has the potential to be used as a discovery tool for therapeutic targets for hpv this serves as a precursor step to designing drugs to target significant regions to prevent malignant conversion moreover these processes are a comprehensive and unbiased.