Fawcett et al. BMC Genomics 2011, 12:311 http://www.biomedcentral.com/1471-2164/12/311 and the two genomes have diverged more than 6% in overall DNA sequence since that time [23]. Conse- quently, except in unusual loci such as HLA, homolo- gous basepair positions that are polymorphic SNPs in both species are highly unlikely to represent shared ancestral polymorphism that has been retained for more than 20 million years in both lineages. Rather, most shared SNP positions will reflect parallel mutational events that created variation at the same site in both species. We converted all 3,038,166 validated rhesus SNPs from this study to hg19 human coordinates, and 2,775,850 of those SNPs successfully converted using the Galaxy hosted UCSC liftOver tool. Of these con- verted SNPs, 90,086 exactly matched the location of human SNPs in dbSNP Build 132, representing a 3.2% overlap. Given that there are 29,100,846 human SNPs (chromosomes 1-22, X) in build 132, about 1% of the human genome, if the specific locations of SNPs in the human and rhesus genomes were entirely uncorrelated then we would only expect about one-third as many polymorphic basepair positions to be shared in the two species. This correlation between the locations of SNPs in rhesus monkeys and humans is almost certainly influ- enced in part by constraints on which sites within each genome can tolerate polymorphism. But other mechan- isms, such as correlations in mutation rates at homolo- gous sites across primate genomes, may also be involved. Discussion In this study, we validated 3,038,166 SNPs and identified an additional 11,382,666 potential SNPs for the Indian- origin rhesus macaque. Both Chinese and Indian-origin rhesus macaques are used in biomedical research, but Indian-origin animals are more prevalent. Furthermore, the rhesus macaque reference genome was produced using an individual of Indian-origin [23]. At the time we began this study, very few SNPs or other variants were confirmed or validated in the rhesus genome. By taking advantage of the falling cost of next-gen resequencing and utilizing all of the existing sequence data available, we were able to validate a large genome-wide series of SNPs. Different sequencing technologies exhibit distinct probabilities of different sequencing errors that will increase false positive rates in calling novel SNPs [38]. SNPs validated in a single individual using multiple sequencing technologies avoid this problem, thus increasing confidence in the SNP calls common to the two data sets. In addition to inherent sequencing chem- istry concerns, low coverage sequencing can suffer from read coverage limitations, creating false negative data because the SNP calling software will discard significant proportions of legitimate SNPs due to low coverage (Figure 4). For this reason, we chose to utilize reduced Page 8 of 13 Figure 4 Low stringency vs. high stringency SNP call validation effectiveness. Validation efficiencies cannot be determined using existing validated SNP data sets, as only 777 SNPs are currently available for rhesus macaque in dbSNP, most of which are polymorphic between subspecies (Chinese to Indian) rather than within subspecies (ie: Indian to Indian). We observed improved validation efficiency using low-stringency SNP calls rather than high- stringency. Both high and low stringency SNP calls were obtained for the reference animal (17573) mate-pair sequence data. The percentage of total SNPs validated in the low-stringency SNP set was slightly less (33.8%) than that observed in the high stringency SNP set (45.7%). In absolute numbers, however, there were 1.8X more SNPs validated from the low stringency SNP calls compared to the high stringency SNP calls. stringency SNP calls from corona_lite and AtlasSNP2. Our methodology minimizes both false positive and false negative SNP calls by using reduced-stringency SNP calling standards but requiring that each SNP loca- tion and both alleles be confirmed in data obtained from different animals and/or different chemistries. In addition, most of our comparisons utilized comparisons of SNP data sets that were called by different algo- rithms. Studies of human population variation from the 1000 Genomes Project [39] have shown that utilizing the intersection of different SNP calling methods to hone in on real SNP data reduces the false positive SNP error rate by 30-50%. This validated SNP list represents a substantial increase in the number of known rhesus SNPs, and therefore is an important resource for research involving this species. These data will facilitate the discovery of functional SNPs, the development of new disease models, and genetic linkage studies, as well as providing a valuable resource for colony management. The most interesting SNPs in any data set are those that directly alter biological function, but these are also the most challenging to identify. While functional SNPs can be in coding (non-synonymous or splice site SNPs) or non-coding regions (non-coding RNAs, regulatory regions), the simplest SNPs to evaluate for function are