| The development of molecular marker technology facilitated genetic mapping of genes and QTLs, also provided an important tool to comparative mapping of different species at the DNA level. QTL localizations and genomic sequence analysis of grass species with rice as model plant generate a great capacity of data. In this study, a local maize bioinformatics database was constructed to store and manage all kinds of information. On the basis of local database, comparative genomics research and maize QTL analysis were performed and following results were obtained:(1) Operating system Red Hat Linux 9.0 was used to construct the local database. All kinds of information of grass genomes, e.g. sequences, maps, markers and QTLs, were integrated, and some useful bioinformatics tools, e.g. BLAST, CMap and SSR finder, were installed.(2) Based on the local database, a new method of in silico mapping of maize sequences was created and assessed.(3) 1201 published maize QTLs conferring for 68 traits were collected and imported into local CMap software to construct an integrated QTL map, which can be used for marker-mining, QTL localization, gene cloning and marker-assisted selection.(4) The maize integrated QTL map was compared with many rice QTL maps by CMap software. Results showed that maize QTLs for various traits were usually clustered in all chromosomes. 22 plant height QTLs of maize were co-linear with 64 plant height QTLs of rice, 43 grain yield QTLs of maize were co-liner with 7 grain yield QTLs of rice.(5) 127 plant height QTLs of maize were refined by the means of "overview" analysis, 40 "real" QTLs were identifed. A substantial number of candidate quantitative trait genes for plant height of maize were found.These results established an important bioinformatics platform for extracting the most ofmaize QTL information.
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