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PublicationA Tutorial of the Poisson Random Field Model in Population Genetics(2008-01-01) Sethupathy, Praveen; Hannenhalli, Sridhar; Sethupathy, Praveen; Hannenhalli, SridharSelectionists and neutralists have fiercely debated, for the past five decades, the extent to which Darwinian selection has shaped molecular evolution. However, both camps do agree that Darwinian selection is a bona fide natural phenomenon. Therefore, various so-called "tests of neutrality" have been developed to detect natural selection on a particular gene or genomic location (for a review on this topic, see Biswas and Akey, 2006). However, these tests are often qualitative and only provide the directionality of selection. A decade and a half ago, S. Sawyer and D. Hartl provided a mathematical framework with which to determine quantitatively the intensity of selection on a particular gene, which they applied to the Adh locus in the Drosophila genome (Sawyer and Hartl, 1992). PublicationMetaProm: a neural network based meta-predictor for alternative human promoter prediction(2007-10-17) Ungar, Lyle H.; Tseng, Hung; Hannenhalli, Sridhar; Ungar, Lyle H.; Tseng, Hung; Hannenhalli, SridharBackground: De novo eukaryotic promoter prediction is important for discovering novel genes and understanding gene regulation. In spite of the great advances made in the past decade, recent studies revealed that the overall performances of the current promoter prediction programs (PPPs) are still poor, and predictions made by individual PPPs do not overlap each other. Furthermore, most PPPs are trained and tested on the most-upstream promoters; their performances on alternative promoters have not been assessed. Results: In this paper, we evaluate the performances of current major promoter prediction programs (i.e., PSPA, FirstEF, McPromoter, DragonGSF, DragonPF, and FProm) using 42,536 distinct human gene promoters on a genome-wide scale, and with emphasis on alternative promoters. We describe an artificial neural network (ANN) based meta-predictor program that integrates predictions from the current PPPs and the predicted promoters' relation to CpG islands. Our specific analysis of recently discovered alternative promoters reveals that although only 41% of the 3′ most promoters overlap a CpG island, 74% of 5′ most promoters overlap a CpG island. Conclusion: Our assessment of six PPPs on 1.06 × 109 bps of human genome sequence reveals the specific strengths and weaknesses of individual PPPs. Our meta-predictor outperforms any individual PPP in sensitivity and specificity. Furthermore, we discovered that the 5′ alternative promoters are more likely to be associated with a CpG island.