Animals, plants, and microorganisms can all be analyzed.
For species with few experimental cases, we can provide information based on model organisms and other species with many experimental cases.
We collect experimental data (RNA-Seq data) uploaded to public databases and convert all experimental data into metadata by adding ontology to the experimental conditions. The ontology uses Plant Ontology (PO: representing plant parts) and Plant Experimental Conditions Ontology (PECO: representing experimental treatments), which allows detailed classification, search, and extraction of each RNA-Seq data. By using the metadata of this large-scale RNA-Seq data, we can accurately and quickly understand which genes are highly expressed under which conditions.
In addition, by using metadata, it is possible to extract only RNA-Seq data under the desired experimental conditions and obtain genome-wide gene expression information under those experimental conditions. By annotating each gene (function, GO, metabolism, transcription factor, etc.), gene discovery can be made more efficient, accurate, fast, and cost-effective.
Searching, investigating, and reading papers is an extremely effective way to find out what cases have been reported about a certain gene, morphology, or phenotype. However, it is difficult to read and understand all of the papers in the current target journals and papers in various research fields.
As a method suitable for extracting necessary information from vast amounts of text, we propose using AI to extract information from papers.
We have developed AI text mining technology to process the reading and understanding of research papers by computer, and for example, we can extract descriptions of gene functions from a huge number of research papers and provide them in an easy-to-understand format (e.g., ****) called Relationship table. This gene information contains embedded hyperlinks to related web pages of original research papers, so original research papers can be checked quickly.
By constructing a gene-compound network, we can obtain an overview of the functions of gene groups. By utilizing AI text mining and our proprietary transcription factor prediction technology, the relationships between acting genes and compounds (binding, expression induction, metabolism, etc.) can be expressed in a comprehensive network diagram. Network information is provided as a file that can be freely edited and modified with mouse operations, making it easier to understand gene functions.
Because we have identified homologs between many species, we can integrate and provide not only the genetic information of the species under study, but also the genetic information of the homologs of different species. Therefore, even if the research subject is a species with few research examples, we can integrate and provide homolog information of many species, accelerating the research and development of users.
We provide consultation and analysis on bioinformatics analysis at low cost.
We can solve your concerns if you don't have specialized knowledge of bioinformatics, don't know where to start, or don't know which analysis method is appropriate.
We support research promotion with all kinds of requests related to bioinformatics, such as providing ideas and skills regarding analysis policies during research planning and promotion, one-off information analysis, support for the introduction of bioinformatics analysis infrastructure within laboratories, human resource development, and requests for advisory services (monthly to yearly, etc.).
The results of the analysis will be provided online as electronic files.
Payment will be made by bank transfer.