Prediction of nitrogen metabolism-related genes in Anabaena by kernel-based network analysis

Authors

  • Shinobu Okamoto Dr.,

    Corresponding author
    1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
    2. Current address: Department of Plant Genome Research, Kazusa DNA Research Institute, Chiba, Japan
    • Research fellow, Department of Plant Genome Research, Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan Fax: +81-438-52-3951
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  • Yoshihiro Yamanishi,

    1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
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  • Shigeki Ehira,

    1. Department of Biochemistry and Molecular Biology, Faculty of Science, Saitama University, Saitama, Japan
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  • Shuichi Kawashima,

    1. Human Genome Center, Institute of Medical Science, University of Tokyo, Meguro, Japan
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  • Koichiro Tonomura,

    1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
    2. Current address: Department of Genomic Drug Discovery Science, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan
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  • Minoru Kanehisa

    1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
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Abstract

Prediction of molecular interaction networks from large-scale datasets in genomics and other omics experiments is an important task in terms of both developing bioinformatics methods and solving biological problems. We have applied a kernel-based network inference method for extracting functionally related genes to the response of nitrogen deprivation in cyanobacteria Anabaena sp. PCC 7120 integrating three heterogeneous datasets: microarray data, phylogenetic profiles, and gene orders on the chromosome. We obtained 1348 predicted genes that are somehow related to known genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. While this dataset contained previously known genes related to the nitrogen deprivation condition, it also contained additional genes. Thus, we attempted to select any relevant genes using the constraints of Pfam domains and NtcA-binding sites. We found candidates of nitrogen metabolism-related genes, which are depicted as extensions of existing KEGG pathways. The prediction of functional relationships between proteins rather than functions of individual proteins will thus assist the discovery from the large-scale datasets.

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