A Gene Discovery Method from Large-Scale Gene Expression Data with Next-Generation Sequencing Technology

High-throughput RNA sequencing technologies with next generation sequencers (NGS) facilitate identification of novel genes and their biological functions. The large-scale gene expression data from NGS leads to more detailed classifications of genes according to expression profiles. However, the current method, such as hierarchical clustering, for a large-scale dataset requires the long calculation time and large-scale computer system. To overcome this problem, we have developed a statistical method based on correspondence analysis (CA). For even large-scale data, it permits us to quickly classify genes and construct gene expression networks in a personal computer.

We employed rice and Arabidopsis Illumina sequences from NCBI SRA to show the theoretical advantage of this method in classification of genes according to expression profiles. To obtain digital gene expression profiles, the number of reads mapped to each gene (cDNAs) was used as the expression level. Our method allows us to efficiently detect genes related to phenotypes or biological conditions of interest. In addition, the comparisons between gene expression networks of rice and Arabidopsis were easily performed. A calculation for the large-scale expression data matrix presented here takes within 1 minute with a Windows PC [4GB memory, Core(TM)2 Duo]. We have also developed a GUI software package “CA Plot Viewer” which is available to analyze users' own expression data.