Origin of Split Gene Structure Figure 4. Direct correlation of the number of exons per gene, length of coding sequence, and length of gene. The number of exons, length of coding sequence, and length of gene for each gene in the human genome were tabulated from the EuSplice database [41]. The number of genes with specified number of exons was recorded, and the average lengths of the coding sequence and the gene for this dataset were computed. The average lengths of the coding sequence and the gene were plotted as a function of increasing number of exons per gene up to 80 exons per gene. The figure shows (A) length of coding sequence and (B) length of gene as a function of increasing number of exons per gene. doi:10.1371/journal.pone.0003456.g004 showed that all of the exons were small enough to be confined to ORFs ,750 bases and most (99%) were shorter than 300 bases. The plot from the spliced gene sequences segregated into two distinct sets of ORFs: a curve exhibiting NED approximating random DNA (#750 bases), and a separate NED curve of non- conforming ORFs ranging from 2000 bases to several thousand bases (Figure 5A). A statistical analysis of the frequencies of ORFs ,750 bases showed that this set was highly correlated to that from a random sequence (R = 0.9987). The ARF frequency of the unspliced and spliced genes showed virtually no difference in their overall patterns (Figure 5A), and there was no second ARF NED curve, indicating that the ORF lengths were increased specifically. Thus, the only effect of splicing was the generation of a lengthened non-conforming ORF in the spliced sequence relative to the un- spliced sequence in each gene. The origin of prokaryotic genes from spliced eukaryotic genes ORF analysis findings from hundreds of prokaryotic genes were strikingly similar to those of spliced eukaryotic genes (Figure S1). When the ORF length frequency of E. coli K12 genes with coding- sequence lengths .2000 bases were plotted (Figure 5B), two distinct clusters of ORF length distribution emerged: the NED pattern with an ORF maximum of ,750 bases, similar to that in random DNA; and a second cluster of non-random, non- conforming ORFs ranging from 2000 to several thousand bases. This remarkably similar pattern to that of the spliced human genes (Figure 5A) supports the possibility that the long non-conforming ORFs in prokaryotic genes were perhaps derived from the prior splicing of exons from preexisting split genes. Moreover, the ARF pattern from these genes had only one cluster of ARFs (shorter than ,750 bases), indicating that the longer non-conforming ORFs must have been derived due to a specific biological process during evolution. Thus, while our analysis indicates that the long contiguous protein-coding sequences of extant prokaryotes statistically could not have existed in any length of random DNA (see Figure 1), it suggests the possibility that they could be formed only by the linking of originally separate short exons within random DNA. It is possible that the reverse-transcription of the spliced mRNAs of original split-genes could create long contigu- ously coding genes, which would then precisely exhibit the ORF length characteristics observed in modern prokaryotic genes. Genome-wide analysis of non-random ORFs Frequency plots of the lengths of ORFs, exons and the total coding sequence of genes (TCS) in each genome were plotted (Figure 6). Most of the ORFs in the human genome conformed to the random model and were shorter than the respective lengths of the TCS. Note that the exon curves were contained well within the ORF curves. The frequency of non-conforming ORFs and exons increased gradually as one went from the human to the prokaryotes. As expected, the exon curve was almost always contained within the ORF curve. For genomes that clearly conformed to the random model (Homo, Arabidopsis, Caenorhabditis, Drosophila) the ORF curve was contained within the TCS curve (Figure 6A, 6B, 6C and 6D), indicating that a high fraction of ORFs were shorter than the TCS. This trend changed at the Plasmodium–Fungi ‘‘junction’’ (Figure 6E and 6F), below which the genomes did not conform to the random model, and the ORF curve extended beyond the TCS curve and included ORFs that contained long non-conforming exons; the corresponding exon curves also approached the TCS curves at this junction. Hence this divergence from the random model in protozoan parasite, fungal and bacterial genomes suggested that their long exons were the result of prior splicing of shorter exons from ancestral genes. Specific occurrence of stop codons within splice signals According to the ROSG model (Figure 7A), RNA splicing evolved to circumvent the problem of short ORFs caused by frequent stop codons in primordial random DNA. Thus the coding-sequence pieces within the short ORFs became exons, and the intervening sequences with clusters of stop codons became introns. The ROSG model therefore requires that the exons are bordered by stop codons. The EOPs of genes from the seven genomes were examined to verify if this was true (see EOP plots in Figure S1). To further examine this concept, we analyzed the frequencies of the 64 different codons around the splice junctions (at positions 220 to +20 bases with respect to the 59 and 39 splice junctions) in all the exons of the human genome. Only the stop codons were found to be present at very high frequencies bordering the exons (Table S1 and Figure 7B). Two key observations were made. Firstly, we found that the majority of the codons (73%) that border the 39 end of exons (one base after the exons) were stop codons and that all the three stop codons occurred at this position; among the remaining non-stop codons PLoS ONE | www.plosone.org 6 October 2008 | Volume 3 | Issue 10 | e3456