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

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 promo

SUBMITTER: Wang J 

PROVIDER: S-EPMC2194789 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature