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

Shinobu Okamoto, Yoshihiro Yamanishi, Shigeki Ehira, Shuichi Kawashima, Koichiro Tonomura, Minoru Kanehisa

    Research output: Contribution to journalArticlepeer-review

    7 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)900-909
    Number of pages10
    Issue number6
    Publication statusPublished - Mar 1 2007

    All Science Journal Classification (ASJC) codes

    • Biochemistry
    • Molecular Biology


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