Single nucleotide polymorphisms (SNPs) are intra-species sequence variation. Conversely, cross-species genomic conservation reflects longer term inter-species evolution. SNPs mark genomic locations where intra-species variability is permissible; fewer SNPs should occur in regions encoding sequence-dependent functions. Similarly, insofar that many functional molecular processes are sequence dependent and behave similarly across species, higher genome sequence conservation should occur in functional regions. Therefore, SNP occurrence rates and genomic conservation should be anti-correlated but similarly delineate functional regions. Indeed, there is an emerging field to actively integrate intra- and inter-genomic variation, with applications ranging from disease to phenotype to functional molecular biology to evolution to crop breeding to conservation [1], [2], [3], [4], [5], [6].

Each step in mRNA-processing relies on specific sequences. After transcription, the spliceosome binds RNA to splice exons into a mature transcript; sequence at the transcript is recognized and polyadenylated. The ribosome scans the mature transcript, starting protein translation at a start codon and ending translation at a stop codon. One pathway for mRNA degradation involves miRNA recognition of sequences in the mRNA 3′ untranslated region (3′ UTR).

The human and mouse genomes were assembled in 2001 [7] [8] and 2002 [9], respectively. The University of California Santa Cruz genome databases [10] provide access to many genome-wide resources, including the genomic locations of mouse and human SNPs and a nucleotide-by-nucleotide cross-species conservation score. The databases also lists the locations of predicted miRNA target locations and human transcription factor binding sites and polyadenylation sites, along with genomic alignments of protein-coding RefSeq (NM) transcripts, including start codons, splice sites, and stop codons.

Here, I integrate these genomic coordinates to determine SNP rates and conservation scores surrounding mRNA processing elements. I expected that, for example, SNPs would occur more frequently in codon position three, the degenerate position, and that conservation would be higher at this position. The results indeed confirm expectations and clearly show that SNP rates and conservation are anti-correlated, that both mark functional elements, and demonstrate the single-nucleotide, sequence-level genetic constraints imposed by protein-coding, by splicing, and by transcript processing.

Using mouse and human data available in the UCSC genome databases, I calculate SNP rates and conservation scores at single nucleotide resolution across six mRNA processing regions, for both species (Methods). The SNP rate is the percentage of nucleotides at a given relative position that overlap a SNP. The conservation score at a given relative position is the average inter-species genome conservation score, where the score is taken from the Vertebrate Multiz Alignment & PhastCons Conservation (28 Species) table [11]. Summary diagrams for mouse start, stop, 3′, and 5′ splice sites are shown in Figure 1.

Here, I used mouse and human genomic resources to examine SNP rates and interspecies genomic sequence conservation in RNA-relevant regions. The results confirm expectations that the SNP rate and conservation score show anti-correlation: fewer SNPs occur in conserved regions. This is in agreement with our understanding that both processes mark regions under evolutionary conservation: SNPs reflect allowed intra-species genome variation and cross-species conservation demarcates long-term evolutionary stability.

Results from human and mouse datasets are very similar. One difference is that when all human SNPs are considered, the SNP rate in introns is surprisingly lower than in exons (Figure 6, top), However, examination of only high quality SNPs shows SNP rates lower in exons, as expected (Figure 6, middle and bottom). SNPs discovery experiments are often biased based on genomic location: many previous and upcoming studies concentrate on variations (and mutations) in known coding exonic regions [17], further biasing our knowledge of SNPs to those occurring in mRNAs. Thus the appearance of higher SNP rates in exons relative to introns may be a simple sampling bias.

Additional biases may result from the genome of the laboratory mouse genome. Inbred laboratory mouse strains have a genome that is mosaic of two sub-species [18]. The polymorphisms contained in dbSNP are a broad and diverse collection of variations submitted from many contributors. Thus a given SNP may represent either an inter-species variation (e.g., within Mus musculus domesticus) or a variation between sub-species (e.g. between M. m. domesticus and M. m. musculus). The latter represent variation somewhat between a true intra-species polymorphism and cross-species variation.

Furthermore, changes in mutation rate variation could introduce biases in these observations (e.g., [19]). The data could be skewed if, for example, the genome of one species experienced an extreme mutation rate, biasing the genomic conservation; by the erroneous submission to dbSNP of somatic mutations from cancerous cell lines, impacting SNP rates; or by variation in evolution across a genome, such as from variable GC content [20], [21]. The biases likely exist; however, the aggregated results presented here are likely robust to these biases.

Predicted miRNA binding sites [15] incorporate cross-species conservation, along with location relative to transcripts (e.g., in 3′ UTRs) and the underlying nucleic acid sequence, and thus genomic conservation is automatically high. However, as the prediction algorithms do not take SNPs into account, it is satisfying to observe that the SNP rate falls within the predicted mouse binding locations, as has been previously observed for human miRNA binding sites [22], [23]. Indeed, the low SNP rate in these conserved locations further demonstrates that SNPs and interspecies genomic sequence conservation are not independent processes; rather, SNPs occur in less conserved regions. Similarly, SNP rates and conservation around predicted human transcription factor binding sites (TFBSs) shows similar results: SNP rates are low in predicted TFBSs.

The profiles of conservation scores and composite SNP rates delineate established functional elements. Conservation can be used to identify individual elements whereas SNP rates can identify elements when consolidated across the genome. The sharp peak in conservation and the SNP rate trough occurring 20 nt before the polyadenylation site identifies a region highly enriched for the polyA signal AA[U/A]AAA. Motif enrichment, conservation, and the low SNP rate all occur over a narrow range from 30 to 17 nt preceding the polyadenylation site. Variations near 5′ and 3′ splice sites may impact pre-mRNA splicing and can cause disease [24]; correspondingly, high conservation and low SNP rates occur here. A lower SNP rate and higher conservation occurs from +/− 6 nt adjacent the splice sites, similar to previous findings that suggest that this is due to the occurrence of splicing signals [25].

Low genomic variability exists in protein coding regions: SNP rates are lower and conservation higher in all protein coding regions examined. Near the protein start site, conservation is sharply higher precisely at the start site and maintains a high level into the protein coding region. Similar profiles, but in reverse, occur at the protein stop site. However, while the stop codon shows a low SNP rate, conservation at the stop site codon itself is not higher than at previous positions, unlike the spike at the start codon. This may reflect either the stop codon degeneracy or that a mutated stop codon will likely be followed by a second stop codon. Furthermore, compared to the conservation in the internal protein coding exons (e.g., Figure 3), the conservation is lower in the coding regions adjacent the start and the stop codons. Speculatively, this may be due either to a relative decrease in functional importance at the protein N- and C-terminus (unlikely) or simply that our knowledge of the start and stop sites is more uncertain than the coordinates of internal exons, which are clearly defined by bounding AG-GT splice sites.

Additionally, a 15 nt trough in conservation and corresponding high in the SNP rate occur from 5 to 20 nt after the stop site in both mouse and human results. That it occurs in both SNPs and conservation suggests that it is real and, taken at face value, shows that more changes have occurred in the region between the stop codon and 20 nt into the 3′ UTR. Previous work has shown that miRNA binding sites at positioned within the 3′UTR occur at least 15 nt from the stop codon [26], suggesting that the increase in sequence variability in this zone is a result of fewer miRNA binding sites.

Inter-species (conservation), intra-species (SNPs), and intra-genome (webLogos) data all demonstrate the low variation at splice sites. The SNP rate increases almost 3-fold from within the coding exons to the rate at 100-nt into introns. However, the low variability occurs not only in protein-coding regions but also into the adjacent intron, accentuating the need to study variation beyond non-synonymous changes (e.g., [21]). Indeed, the highest conservation and lowest SNP rate occur outside of the protein-coding exons, at the splice sites. This is in line with expectations that while polymorphisms in protein-coding regions could impact amino-acid selection or codon usage, polymorphism and mutations in splicing motifs could deregulate or cause the skipping of entire exons.

Finally, all protein coding regions display a phase three periodicity with increased variability at position three. The periodicity is not observed outside of the protein coding regions, such as adjacent polyadenylation sites. That the SNP rates are highest at codon position three is undoubtedly a result of the degeneracy for amino acid coding at the third position (19 of 22 amino acids are degenerate at codon position 3). Moreover, codon degeneracy is slightly higher at codon position one relative to position two: I correspondingly find a lower SNP rate at the second position. Thus, more SNPs occur in degenerate positions for amino acid encoding, and are thus less likely to have a functional impact [6].

Together, these findings reinforce our understanding of evolution and functional elements in the genome. These include that SNPs occur more frequently in less conserved regions; that both genomic conservation and aggregated SNPs rates can be used to identify functional elements; and that codon position three in protein coding regions is more degenerate.

Genomic coordinates were downloaded from UCSC databases [10], including human dbSNP build 130 and mouse dbSNP build 128 [13], human predicted transcription factor binding sites (TFBS) from the HMR Conserved Transcription Factor Binding Sites table [27], mouse and human predicted miRNA target sites from the TargetScan table [15], reported human polyadenylation sites [16], alternative transcriptional element alignments (derived in the UCSC databases from the unpublished txgAnalyse and txGraph programs written by Jim Kent at UCSC), and RefSeq NM transcript and EST sequence alignments. I accessed the nucleotide-specific cross-species conservation scores from the Vertebrate Multiz Alignment & PhastCons Conservation (28 Species) table [11]. As mentioned, the predictions of both TFBS and miRNA binding sites explicitly use cross-species genome conservation as criteria. Polyadenylation site predictions incorporate the coordinates of 3′ ESTs, including the presence of polyA tails. Validated human SNPs are those containing the word “by” in the description (e.g., “by-hapmap”) and “1000 Genome” SNPs are those listing “by-1000genomes”. The SNPs identified in the 1000 Genomes Project are not error-free; however, I expect no systematic error that would impact the presented aggregated SNP rates. Transcript alignments were performed by UCSC using BLAT.

For the human predicted polyadenylation sites, I selected only those polyadenylation sites falling within 100 nucleotides of the 3′ terminus of a RefSeq transcript. This excluded alternative polyadenylation sites occurring in the interior of 3′ UTRs and also allowed a strand assignment to each polyadenylation site, based on the RefSeq transcript orientation.

I used RefSeq “NM” transcript alignments which include exon boundaries, transcription start and stop coordinates, coding start and stop coordinates, and intron phase. I required at least 60 nt of coding sequence after and before start and stop codons, respectively. For the 5′ and 3′ splice sites, I included only internal exons longer than 60 nt surrounded by introns greater than 300 nt long.

I examined several mRNA processing regions (Figure 1): protein start codons, internal 3′ splice sites, internal 5′ splice sites, protein stop codons, predicted miRNA binding sites, predicted polyadenylation sites (human), transcript 3′ termini (mouse), and predicted transcription factor binding sites (human), For each region, the algorithm looped over each individual site, counting SNPs and the level of conservation at each position between −500 and +500 nt around the site. Reported values reflect the percentage (SNP rate) and average (genomic conservation) at each position relative to the specific site location. The UCSC genome database also includes the underlying nucleotide sequence, which was used for the WebLogos.