The Genom of Homo sapiens.pdf

EVOLUTIONARY DISTANCE AND GENE PREDICTION 129formance. Finally, our results on CFTR agree qualitativelywith those that Thomas et al. obtained withBLASTZ (Schwartz et al. 2003), an algorithm that producesa very different type of alignment than BLASTN.The accuracy curve shown in Figure 4A reflects comparisonsat only four significantly different evolutionarydistances. The alignment curves in Figure 4B suggest thatfilling in the intermediate distances will yield an accuracycurve with a single peak at the evolutionary distance thatis optimal for informing gene modeling. If so, the peak isat a distance farther than that between mouse and rat butcloser than that between mouse and chicken. The peak atthe mouse–human comparison in Figure 4A is consistentwith a highly idealized theoretical analysis suggesting apeak somewhere in the vicinity of mouse–human, or possiblysomewhat farther out (Zhang et al. 2003). To determinethe peak location more precisely, we will need thesequences of more genomes. The most immediate possibilityfor a comparison at a distance intermediate betweenmouse–human and mouse–chicken will come from theopossum, Monodelphis domestica, a marsupial that hasbeen designated as a high-priority sequencing targetby the National Human Genome Research Institute(http://www.genome.gov/page.cfm?pageID=10002154).Although the mouse–rat divergence is much too closefor optimal annotation of mammalian genomes using asingle genome pair, preliminary data suggest that the situationmay be quite different when the target is a genomewith very short introns. For example, gene prediction inCryptococcus neoformans serotype D benefits fromalignments to serotype A. These alignments cover 100%of CDS and 87% of intron bases, excluding splice sites(A. Tenney, unpubl.). Visual inspection suggests thatwhen TWINSCAN is trained on these alignments, it predictsintrons or intergenic regions that include most unalignedregions; conversely, it rarely predicts an intronthat does not overlap an unaligned region. Because the intronsare very short (68 bp on average), the locations ofthe intron boundaries are quite constrained relative tothose of mammalian introns. Apparently, TWINSCANuses these alignments to find the general locations of introns,rather than their precise boundaries.The research presented here is a significant step towarddetermining the optimal distance for gene modeling usingpair-wise genome alignments. Looking ahead to the nextstep, we and other investigators are developing methodsthat use information from multiple genome alignmentsrather than choosing a single best alignment (Boffelli etal. 2003; Siepel and Haussler 2003). When it has beenfully developed, the multi-genome approach is expectedto yield real breakthroughs in the accuracy of genemodeling.ACKNOWLEDGMENTSWe are grateful to the centers that produced thegenome sequences used in this study. Special mention isdue to the Washington University Genome SequencingCenter for producing the chicken whole-genome shotgunsequence, the National Institutes of Health Intramural SequencingCenter for producing BAC-based sequences ofthe greater CFTR regions, and the Whitehead InstituteGenome Research Center for producing the dog wholegenomeshotgun sequence. The authors were supported inpart by grant HG-02278 from the National HumanGenome Research Institute.REFERENCESAlexandersson M., Cawley S., and Pachter L. 2003. SLAM:Cross-species gene finding and alignment with a generalizedpair hidden Markov model. 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