Abstract
Housekeeping genes (HKGs) generally have fundamental functions in basic biochemical processes in organisms, and usually have relatively steady expression levels across various tissues. They play an important role in the normalization of microarray technology. Using Fourier analysis we transformed gene expression time-series from a Hela cell cycle gene expression dataset into Fourier spectra, and designed an effective computational method for discriminating between HKGs and non-HKGs using the support vector machine (SVM) supervised learning algorithm which can extract significant features of the spectra, providing a basis for identifying specific gene expression patterns. Using our method we identified 510 human HKGs, and then validated them by comparison with two independent sets of tissue expression profiles. Results showed that our predicted HKG set is more reliable than three previously identified sets of HKGs.
Highlights
A housekeeping gene (HKG) is typically a constitutive gene which is required for the maintenance of basic cellular functions, and generally has a steady expression level across various tissues through all phases of cell development irrespective of environmental conditions
In order to test whether the Fourier spectrum of a gene is a distinct feature of an HKG, we established two classification models based on 24 frequency components obtained with Fourier analysis: the HN model (HKG/non-HKG; true model) and the NN model
It is evident that HKG frequency components have characteristic structures that can be detected by support vector machine (SVM), indicating that the frequency components of gene expression can be used to effectively discriminate between HKGs and non-HKGs
Summary
A housekeeping gene (HKG) is typically a constitutive gene which is required for the maintenance of basic cellular functions, and generally has a steady expression level across various tissues through all phases of cell development irrespective of environmental conditions. This makes HKGs excellent controls for the normalization of Gene Chip technology, and allows the sample quality and consistency of sample quantity on chips to be assessed [1]. High levels of background noise and reproducibility problems are difficult to avoid in microarray experiments
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