Genome Informatics Journal has been publishing the accepted papers on International Conference on Genome Informatics (GIW) and International Workshop on Bioinformatics and Systems Biology (IBSB), and it is an official publication of the Japanese Society for Bioinformatics (JSBi). The Board Members of JSBi solicited contributions from all oral presenters in The 2010 Annual Conference of the Japanese Society for Bioinformatics (JSBi2010) to this special issue of Genome Informatics. We invited expanded papers based upon the 13 accepted oral presentations in the JSBi2010 Sessions. 6 papers were submitted and peer reviewed. Among them, 5 papers were accepted and published in this special issue of Genome Informatics.
A wiki-based repository for crude drugs and Kampo medicine is introduced. It provides taxonomic and chemical information for 158 crude drugs and 348 prescriptions of the traditional Kampo medicine in Japan, which is a variation of ancient Chinese medicine. The system is built on MediaWiki with extensions for inline page search and for sending user-input elements to the server. These functions together realize implementation of word checks and data integration at the user-level. In this scheme, any user can participate in creating an integrated database with controlled vocabularies on the wiki system. Our implementation and data are accessible at http://metabolomics.jp/wiki/.
When the DNA damage is generated, the tumor suppressor gene p53 is activated and selects the cell fate such as the cell cycle arrest, the DNA repair and the induction of apoptosis. Recently, the p53 oscillation was observed in MCF7 cell line. However, the biological meaning of p53 oscillation was still unclear. Here, we constructed a novel mathematical model of cell cycle regulatory system with p53 signaling network to investigate the relationship between the p53 oscillation and the cell cycle progression. First, the simulated result without DNA damage agreed with the biological findings. Next, the simulations with DNA damage realized both the p53 oscillation and the cell cycle arrest, and indicated that the generation of multiple p53 pulses disrupted the cell cycle progression. Moreover, the simulated results showed that the cell cycle disruption was caused by the catastrophe of M phase in the cell cycle, which resulted from the decline in cyclin A/cyclin-dependent kinase 2. The results in this study suggested that the generation of multiple p53 pulses against DNA damage may be used as a marker of cell cycle disruption.
Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pairs; then an F-measure value of 0.465 was obtained with our prediction system.
Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer “K computer” which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for “K computer” and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .
We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.