Novel Gene Discovery in the Human Malaria Parasite using Nucleosome Positioning Data

N. Pokhriyal, N. Ponts, E. Y. Harris, K. G. Le Roch, S. Lonardi*

Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA. stelo@cs.ucr.edu

Proc LSS Comput Syst Bioinform Conf. August, 2010. Vol. 9, p. 124-135. Full-Text PDF

*To whom correspondence should be addressed.

Recent genome-wide studies on nucleosome positioning in model organisms have shown strong evidence that nucleosome landscapes in the proximity of protein-coding genes exhibit regular characteristic patterns. Here, we propose a computational framework to discover novel genes in the human malaria parasite genome P. falciparum using nucleosome positioning inferred from MAINE-seq data. We rely on a classifier trained on the nucleosome landscape profiles of experimentally verified genes, and then used to discover new genes (without considering the primary DNA sequence). Cross-validation experiments show that our classifier is very accurate. About two-thirds of the locations reported by the classifier match experimentally determined expressed sequence tags in GenBank, for which no gene has been annotated in the human malaria parasite.