Performance and Scalability of Discriminative Metrics for Comparative Gene Identification in 12 Drosophila Genomes

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Abstract
Comparative genomics of multiple related species is a powerful methodology for the discovery of functional genomic elements, and its power should increase with the number of species compared. Here, we use 12 Drosophila genomes to study the power of comparative genomics metrics to distinguish between protein-coding and non-coding regions. First, we study the relative power of different comparative metrics and their relationship to single-species metrics. We find that even relatively simple multi-species metrics robustly outperform advanced single-species metrics, especially for shorter exons (≤240 nt), which are common in animal genomes. Moreover, the two capture largely independent features of protein-coding genes, with different sensitivity/specificity trade-offs, such that their combinations lead to even greater discriminatory power. In addition, we study how discovery power scales with the number and phylogenetic distance of the genomes compared. We find that species at a broad range of distances are comparably effective informants for pairwise comparative gene identification, but that these are surpassed by multi-species comparisons at similar evolutionary divergence. In particular, while pairwise discovery power plateaued at larger distances and never outperformed the most advanced single-species metrics, multi-species comparisons continued to benefit even from the most distant species with no apparent saturation. Last, we find that genes in functional categories typically considered fast-evolving can nonetheless be recovered at very high rates using comparative methods. Our results have implications for comparative genomics analyses in any species, including the human. Comparing the genomes of related species is a powerful approach to the discovery of functional elements such as protein-coding genes. Theoretically, using more species should lead to more discovery power. Many questions remain, however, surrounding the optimal choice of species to compare and how to best use multi-species alignments. It is even possible that practical limitations in the sequencing, assembly, and alignment of genomes could effectively negate the benefit of using more species. Here, we used 12 complete fly genomes to study a variety of metrics used to identify protein-coding genes, including methods that analyze only the genome of interest and comparative methods that examine evolutionary signatures in genome alignments. We found that species over a surprisingly broad range of phylogenetic distances were effective in comparative analyses, and that discovery power continued to scale with each additional species without apparent saturation. We also examined whether comparative methods systematically miss genes considered fast-evolving, and studied how performance is influenced by genome alignment strategies. Our results can help guide species selection for future comparative studies and provide methodological guidance for a variety of gene identification tasks, including the design of future de novo gene predictors and the search for unusual gene structures.