Research article

MetaProm: a neural network based meta-predictor for alternative human promoter prediction

Junwen Wang^1,2,3,4,5 , Lyle H Ungar^1,3 , Hung Tseng^6,7,8 and Sridhar Hannenhalli^1,2,3

¹  Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104, USA

²  Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA

³  Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA

⁴  Core Genotyping Facility, Advanced Technology Program, SAIC-Frederick, Frederick, MD 21702, USA

⁵  Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, MD 20892, USA

⁶  Department of Dermatology, University of Pennsylvania, Philadelphia, PA 19104, USA

⁷  Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA 19104, USA

⁸  Center for Research on Reproduction and Women's Health, University of Pennsylvania, Philadelphia, PA 19104, USA

author email corresponding author email

BMC Genomics 2007, 8:374doi:10.1186/1471-2164-8-374

Published:	17 October 2007

Abstract

Background

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.