Fawcett et al. BMC Genomics 2011, 12:311 http://www.biomedcentral.com/1471-2164/12/311 the two expected alleles in the potential SNP position. This novel method of validating potential SNPs in a new read set is restricted by the exact matching parameter that confers the high specificity. When e-genotype was used to screen human 1000 Genomes read data for known SNPs (from dbSNP) in the same individual, the error call rates were extremely low for miscalled homozy- gotes (called as wrong homozygote (0.002%)) and hetero- zygotes (called as wrong heterozygote (1.01%) or homozygote (1.52%)) (Figure S3, Additional file 4). dbSNP concordance in human data from the 1000 Gen- omes pilot study [39] using e-genotype indicates that the total miscall rate is ~2.6% (Figure S3, Additional file 4). When extremely high coverage or low coverage SNPs (delineated by dotted lines on figure) are removed from consideration (high coverage SNPs are typically non-spe- cific and low coverage SNPs are often miscalled hets where coverage was simply insufficient to detect both alleles), the miscall rate is calculated to be 1.6%. Because of the stringency of probe placement, e-genotype has much reduced effective read coverage for any SNP, but of the SNPs that are called by e-genotype, the probability of false positive calls is extremely low. E-genotype has sig- nificantly reduced power to detect clustered SNPs, espe- cially in linkage disequilibrium, when they fall within the 31 base pair probe region. E-genotype is unable to iden- tify SNPs located within 15 base pairs of gap regions in the reference sequence or chromosome ends. We developed a perl pipeline to compare chromo- some, base pair locations, and both alleles for all pair- wise comparisons of SNP lists. At least one read of suffi- cient quality (equivalent to a minimum Phred score of 20) for each allele in each data set was required for a positive result, and all instances with more than 200 reads covering an allele were removed irrespective of average read coverage for the data set, due to presumed mis-mapping or non-unique mapping. Once these com- parisons were completed, all of the validated SNPs were appended in a single file and duplicates were removed by chromosome/base-pair determination. E-genotyping (http://is04607.com/~drio/egenotype/ [29]) was performed on all the SOLiD read sets from animals 17573, r1766 and r02120. The probe sets were created from: a) the Sanger data from the original refer- ence animal (17573), b) the novel SNP calls from coro- na_lite for r1766 and r02120, or c) the reads produced using Roche 454 methods on unrelated animals in the original rhesus genome paper [23]. All the probe sets were checked for unique mapping locations by mapping to the reference genome assembly prior to running e- genotype on experimental read sets. E-genotyping results with at least one read for each allele were con- sidered to be positive when comparing data from differ- ent chemistries. Page 11 of 13 We annotated the SNPs using a Java tool that accessed Ensembl (build 57, March 2010). Annotation of SNPs included the genomic placement (intronic, inter- genic, synonymous coding, nonsynonymous coding, 5’UTR, 3’UTR, or splice site) for all applicable tran- scripts. If a SNP was determined to fall in a genic region (coding and non-coding), the following annotations were added as applicable: gene name, gene function, codon, reference amino acid, variant amino acid, protein position. All of the validated SNPs have been submitted to dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/[16]) (ss numbers available in Table S2, Additional files 5, 6, 7, 8, 9 and 10). Unvalidated potential SNPs will be avail- able at our lab Genboree site (http://genboree.org/java- bin/project.jsp?projectName=Rhesus%20SNPs%20using% 20Next-Gen%20Sequencing&isPublic=Yes) [30]. Polyphen-2 analysis and GeneGo analysis Non-synonymous SNPs (nsSNPs) from this validated list of rhesus macaque SNPs were converted to human gen- ome (hg18) co-ordinates using the UCSC liftOver tool from Genboree Galaxy and then analyzed for possible functional significance using PolyPhen-2 (http://genetics. bwh.harvard.edu/pph2/[35]) [36]. All SNPs that did not convert cleanly to unique locations in hg18, or where the human reference allele at that location matched the rhesus variant allele, were removed from further analy- sis. The resulting list of human homologues of our rhe- sus nsSNPs was submitted as a batch file for HumDiv model analysis in PolyPhen-2. Polyphen-2 utilizes both homologous sequence alignment as well as known pro- tein 3D crystal structures to predict the potential effect of any given polymorphism upon protein function. Poly- morphisms are tested for the potential damaging effect upon protein function by normalization to known human deleterious alleles in UniProt and use of a naïve Bayes classifier. All genes containing SNPs that were classified as damaging (probably or possibly) in the Poly- Phen-2 output were submitted to GeneGo (St. Joseph, MI, http://www.genego.com[37]) for network analysis. Each gene was associated with a generalized function and proposed disease network. Additional material Additional file 1: Figure S1, Additional file 1. Validation by data source. Validation efficiencies cannot be determined using existing validated SNP data sets, as only 777 SNPs are currently available in dbSNP for rhesus macaque, most of which are polymorphic between subspecies (Chinese to Indian) rather than within a subspecies (Indian to Indian). We calculated the proportions of unique SNPs validated within each pairwise comparison of all 15 data sets. On average, we validated ~35% of the potential SNPs from each of the resequencing data sets. r02120 displays much higher rates of validation (~65%) compared to all other data sets, likely due to technical issues resulting in much lower