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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Bioinformatics

Open Access Research

A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum

Hong Cai1, Changjin Hong2, Timothy G Lilburn3, Armando L Rodriguez14, Sheng Chen2, Jianying Gu5, Rui Kuang2* and Yufeng Wang16*

Author Affiliations

1 Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA

2 Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA

3 Department of Bacteriology, American Type Culture Collection, Manassas, VA 20110, USA

4 Department of Computer Science, University of Texas at San Antonio, San Antonio, TX 78249, USA

5 Department of Biology, College of Staten Island, City University of New York, Staten Island, NY 10314, USA

6 South Texas Center for Emerging Infectious Diseases, University of Texas at San Antonio, San Antonio, TX 78249, USA

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BMC Bioinformatics 2013, 14(Suppl 12):S2  doi:10.1186/1471-2105-14-S12-S2

Published: 24 September 2013

Abstract

Background

According to the World Health organization, half the world's population is at risk of contracting malaria. They estimated that in 2010 there were 219 million cases of malaria, resulting in 660,000 deaths and an enormous economic burden on the countries where malaria is endemic. The adoption of various high-throughput genomics-based techniques by malaria researchers has meant that new avenues to the study of this disease are being explored and new targets for controlling the disease are being developed. Here, we apply a novel neighborhood subnetwork alignment approach to identify the interacting elements that help regulate the cell cycle of the malaria parasite Plasmodium falciparum.

Results

Our novel subnetwork alignment approach was used to compare networks in Escherichia coli and P. falciparum. Some 574 P. falciparum proteins were revealed as functional orthologs of known cell cycle proteins in E. coli. Over one third of these predicted functional orthologs were annotated as "conserved Plasmodium proteins" or "putative uncharacterized proteins" of unknown function. The predicted functionalities included cyclins, kinases, surface antigens, transcriptional regulators and various functions related to DNA replication, repair and cell division.

Conclusions

The results of our analysis demonstrate the power of our subnetwork alignment approach to assign functionality to previously unannotated proteins. Here, the focus was on proteins involved in cell cycle regulation. These proteins are involved in the control of diverse aspects of the parasite lifecycle and of important aspects of pathogenesis.