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Open Access Highly Accessed Methodology article

Detection of genome-wide polymorphisms in the AT-rich Plasmodium falciparum genome using a high-density microarray

Hongying Jiang1, Ming Yi2, Jianbing Mu1, Louie Zhang3, Al Ivens4, Leszek J Klimczak5, Yentram Huyen5, Robert M Stephens2 and Xin-zhuan Su1*

Author Affiliations

1 Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA

2 Advanced Technology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA

3 Medical School of Drexel University, 2900 Queen Lane, Philadelphia, PA 19129, USA

4 Pathogen Microarrays Group, The Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK

5 Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA

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BMC Genomics 2008, 9:398  doi:10.1186/1471-2164-9-398

Published: 25 August 2008

Abstract

Background

Genetic mapping is a powerful method to identify mutations that cause drug resistance and other phenotypic changes in the human malaria parasite Plasmodium falciparum. For efficient mapping of a target gene, it is often necessary to genotype a large number of polymorphic markers. Currently, a community effort is underway to collect single nucleotide polymorphisms (SNP) from the parasite genome. Here we evaluate polymorphism detection accuracy of a high-density 'tiling' microarray with 2.56 million probes by comparing single feature polymorphisms (SFP) calls from the microarray with known SNP among parasite isolates.

Results

We found that probe GC content, SNP position in a probe, probe coverage, and signal ratio cutoff values were important factors for accurate detection of SFP in the parasite genome. We established a set of SFP calling parameters that could predict mSFP (SFP called by multiple overlapping probes) with high accuracy (≥ 94%) and identified 121,087 mSFP genome-wide from five parasite isolates including 40,354 unique mSFP (excluding those from multi-gene families) and ~18,000 new mSFP, producing a genetic map with an average of one unique mSFP per 570 bp. Genomic copy number variation (CNV) among the parasites was also cataloged and compared.

Conclusion

A large number of mSFP were discovered from the P. falciparum genome using a high-density microarray, most of which were in clusters of highly polymorphic genes at chromosome ends. Our method for accurate mSFP detection and the mSFP identified will greatly facilitate large-scale studies of genome variation in the P. falciparum parasite and provide useful resources for mapping important parasite traits.