Japonica group cultivar Nipponbare (Nip) and Indica group cultivar 93-11 are the two main cultivated varieties and parental lines for breeding in Asia. The database provides a vastly improved de novo assembly and annotation of Nipponbare and 93-11 reference genomes. The newly assembled Nip and 93-11 reference genomes displayed 2.2-fold (contig N50: 16,974 kb versus 7,711 kb) and 460-fold (9,640 kb versus 21 kb) higher contiguity than Nip MSU7 (http://rice.plantbiology.msu.edu/index.shtml) and 93-11 BGI (http://rise2.genomics.org.cn/page/rice/index.jsp) genomes, respectively. The new reference assembly has greatly reduced the gap number, from 905 gaps of MSU7 to 18 gaps of BRI for Nip, and 54,600 gaps of BGI to 65 gaps of BRI for 93-11. The new genome assemblies corrected 152 and 8,347 gene annotation errors in Nip and 93-11, respectively, including corrections of gene orientation and gene structure, and identified a number of new genes. Recently, DNA N6-methyldeoxyadenine (6mA) opens a new and promising dimension of epigenetic research. This website firstly provides 6mA DNA methylation at a single-nucleotide resolution in Nip and 93-11 associated with actively expressed genes in rice. 6mA is broadly distributed across rice genomes and is significantly enriched in promoters and exons. Importantly, based on the expression data, 6mA is associated with actively expressed genes in rice.

Link to eRice

Reverse genetics is a powerful tool for funtional genomics study. Chemical mutagenesis efficiently generates phenotypic variations in otherwise homogeneous genetic backgrounds, enabling linking biological function with a gene of interest. Using ethyl methanesulfonate (EMS), we mutagenized the fresh pollen from maize B73 inbred and obtained a large number of mutants. Exome capture-Based SNP Discovery (EBSD) was used to identify all mutated loci. A website, called Maize EMS induced Mutant Database (MEMD), is constructed to allow scientists to search for and order the mutant seeds of interest for non-profit research. We will keep updating all the information related to the mutants.

Link to MEMD

Presyncodon was used to predict the gene coden from its protein sequence with the evolutionary information in the expression host. The synonymous codon usage pattern of peptide was learned from the big data of genomes (Escherichia coli, Bacillus subtilis and Saccharomyces cerevisiae). The machine-learning models were constructed to predict synonymous codon (low- or high-frequency-usage codon) selection in a gene. All possible synonymous codon selection tendency of the middle residue in the fragment was predicted by the predicting model and stored in the PostgreSQL database. Now, the method could be easily and efficiently used to design new genes from protein sequences for optimal expression in the three expression hosts (E. coli, B. subtilis and S. cerevisiae).

Link to Presyncodon