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|◆ Field Informatics|
|◇ Field of Research on Crop Production Management Systems|
|◇ Field of Research on the Function and Regulation of Animal Production|
|◇ Field of Research on Crop Genomic Breeding|
|◇ Field of Research on Fruit Tree Genomic Breeding|
|◇ Field of Research on the Development and Utilization of New Breeding Materials for Ornamental Plants|
|Outline of the Field Informatics research field|
| Information technology is essential for modern agricultural or biological
research. However, genuine results from the field of information technology
are difficult to apply to the fields of agriculture or biology without
any modification. It is important that we pioneer a new field in which
the fields of information technology and agriculture or biology can be
integrated. We also need to successfully apply existing knowledge on information
technology after we gain a full understanding of the characteristics of
the unique information possessed by living organisms and the environment
In light of such considerations, the Field Informatics laboratory aims to discuss and understand, in a comprehensive manner, the basic methods of measurement, analysis, modeling, and evaluation of various living organisms, ecosystems, and the environment which show complicated interactive responses at levels ranging from gene to population on the basis of state-of-art information technologies such as grid computing or data mining. In particular, our education and research are promoted mainly on the following subjects: field monitoring, applied statistics, computer information technology, bioinformatics, image analysis, and biological modeling.
・Sensor networks and field monitoring
・Model theories to quantify and evaluate diverse and complicated biological functions and environmental dynamics
・Bioinformatics, Image analysis, pattern recognition, and data mining that target living organisms
・Software technology for grid computing and modeling
In the following section, we show some research examples for each instructor, but the range of research covered by Field Informatics is not necessarily limited to these examples. You can freely develop your research by using you own ideas. In addition, when you learn in this field, you do not need to have both specific knowledge of information technology and expertise in agriculture or biology. We think that it is perfectly all right for you to have knowledge in only one of these two fields: it is more important that human resources with knowledge in different areas meet in our laboratory to develop new research fields.
We are looking forward to you participating in our research field.
| We are conducting research on statistical genetics, in which genetic phenomena are analyzed using statistical techniques. Our studies are focusing on developing efficient methods for linkage and QTL analysis and predicting breeding values using a large number of genome-wide markers of plants and animals which are easily obtained by high-throughput genotyping technologies. The objective is to establish novel efficient methods of plant and animal breeding using genomic information by elucidating the relationship between genome and agricultural traits using advanced statistical methods. We explain linkage analysis of the fruit color of tomato as an example of our recent research.
In collaboration with researchers of tomato breeding, we constructed an experimental segregating population derived from a cross between two tomato cultivars A and B, the fruit colors of which are red and pink, respectively, as shown in the above photos. We developed two statistical methods for linkage analysis: one is a modified interval mapping applicable for a categorical trait such as fruit color, and the other is a Bayesian linkage mapping method, and used them to analyze the fruit color of tomato. As shown in the above graph, we could identify the genome region affecting the fruit color of tomato.
We are not only interested in theoretical research for developing new methodologies to analyze the correlation between genomic information and phenotypes, but also applied research in cooperation with researchers of crops and animals as described in the above example.
I analyze genome sequences using Next Generation Sequences (NGS) and
conduct comparative genome analysis for breeding of rice, wheat and
barley. Currently, genome sequences are available easily not only for each
species but also for each cultivar and individuals because of advancement
in NGS techniques. However, since it is quite hard to analyze NGS data by
a personal computer, extract of only beneficial data from NGS data is
indispensable to breeding.
Therefore, I develop some databases of plant genome, such as RAP-DB for rice, bex-db for barley and KomugiGSP for wheat as infrastructures of genome and gene information. In addition, I construct markers to evaluate domestic barley cultivars so that they are applied to QTL analysis and GWAS. Even if it is often considered that “Bioinformatics” is difficult research field for biologists, many experimental researchers currently analyze NGS data by their own efforts. I was also an experimental researcher originally and started to learn informatics (molecular evolution) from Ph. D. degree. Once you are interested in the bioinformatics, please contact me.
Traditional artificial intelligence and
statistical methods have used data in a single form to create a model or a
regression equation by a single method. However, because the era has
arrived in which data in various forms are available in large quantities, prediction or
control that fully uses the information included in the data has become
impossible by traditional methods. For this reason, we are developing
methods for more practical prediction or control by understanding data in
different forms and characteristics in a comprehensive manner. We
synthesize models and equation regressions based on various viewpoints and
apply these methods to actual data. In this case, various methods
developed in non-parametric regression and a number of technologies used
in artificial intelligence are used expansively. The data targeted for
creation of the model and the regression equation will be selected in
accordance with the interests or aims of the students. For example,
agricultural production, geological information, remote sensing data, and
classification of living organisms can be listed as candidates.