Kentaro Yano
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Shin Terashima
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Yukino Nakamura
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Toru Kudo
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Maasa Kanno
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Misa Saito
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Satomi Asano
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Koji Yokoyama
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Masaaki Kobayashi
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Hajime Ohyanagi
,
School of Agriculture, Meiji University, Kawasaki, Kanagawa, Japan
Large-scale omics information is an important source for a clue to gain insight into gene functions. To provide a platform for seamless access to the omics information across multiple plant species, we have developed and been maintaining a web-database Plant Omics Data Center (PODC; http://bioinf.mind.meiji.ac.jp/podc/). PODC stores information such as NGS-based gene expression profile, gene expression networks (GENs), orthologous relationships, and literature-based functional annotations. Here, we report about the recent major updates in PODC.
For species, information on Nicotiana tabacum and Zea mays become available in addition to 8 species in the previous version (Arabidopsis thaliana, Solanum lycopersicum, Solanum tuberosum, Oryza sativa, Sorghum bicolor, Vitis vinifera, Medicago truncatula, and Glycine max). PODC has been greatly improved in the database functions for data mining (e.g., GEN construction with genes of interest, statistical test for gene expression profiles). The original descriptions of experimental conditions (e.g., developmental stages, treatments, organs) for each RNA-seq dataset in SRA are not practically controlled. To easily compare gene expressions among experimental conditions in PODC, we also organized the descriptions of experimental conditions in SRA into easily accessed and manipulated formats by manual curation. The records for highly-reliable functional annotations, obtained from natural language processing (NLP) and manual curation processes, for genes have also considerably increased (the current version contains approximately 18,000 records). With the comprehensive omics and knowledge-based information, PODC provides a powerful platform for identification of novel genes and biological functions.
This research was supported by Research Funding for Computational Software Supporting Program from Meiji University.
