SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip

doi:10.1186/1471-2105-10-131

BMC Bioinformatics
Volume 10

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Fujisawa H
Horiuchi Y
Harushima Y
Takada T
Eguchi S
Mochizuki T
Sakaguchi T
Shiroishi T
Kurata N

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Horiuchi Y
Harushima Y
Takada T
Eguchi S
Mochizuki T
Sakaguchi T
Shiroishi T
Kurata N
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Methodology article

SNEP: Simultaneous detection of nucleotide and expression polymorphisms using Affymetrix GeneChip

Hironori Fujisawa¹^,4 , Youko Horiuchi²^,4 , Yoshiaki Harushima²^,4 , Toyoyuki Takada³^,4 , Shinto Eguchi¹^,4 , Takako Mochizuki² , Takayuki Sakaguchi¹^,4 , Toshihiko Shiroishi³^,4 and Nori Kurata²^,4

¹The Institute of Statistical Mathematics, Tokyo 106-8569, Japan

²Plant Genetics Laboratory, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan

³Mammalian Genetics Laboratory, National Institute of Genetics, Mishima, Shizuoka 411-8540, Japan

⁴Transdisciplinary Research Integration Center, Research Organization of Information and Systems, Tokyo 105-0001, Japan

author email corresponding author email

BMC Bioinformatics 2009, 10:131doi:10.1186/1471-2105-10-131


Published:	6 May 2009

Abstract

Background

High-density short oligonucleotide microarrays are useful tools for studying biodiversity, because they can be used to investigate both nucleotide and expression polymorphisms. However, when different strains (or species) produce different signal intensities after mRNA hybridization, it is not easy to determine whether the signal intensities were affected by nucleotide or expression polymorphisms. To overcome this difficulty, nucleotide and expression polymorphisms are currently examined separately.

Results

We have developed SNEP, a new method that allows simultaneous detection of both nucleotide and expression polymorphisms. SNEP involves a robust statistical procedure based on the idea that a nucleotide polymorphism observed at the probe level can be regarded as an outlier, because the nucleotide polymorphism can reduce the hybridization signal intensity. To investigate the performance of SNEP, we used three species: barley, rice and mice. In addition to the publicly available barley data, we obtained new rice and mouse data from the strains with available genome sequences. The sensitivity and false positive rate of nucleotide polymorphism detection were estimated based on the sequence information. The robustness of expression polymorphism detection against nucleotide polymorphisms was also investigated.

Conclusion

SNEP performed well regardless of the genome size and showed a better performance for nucleotide polymorphism detection, when compared with other previously proposed methods. The R-software 'SNEP' is available at http://www.ism.ac.jp/~fujisawa/SNEP/ webcite.