Design And Implementation Of Bioinformatics Strategy And Software For Identifying Drought Tolerant Genes In Plants Based On QTL Information

The discovery of genes controlling complex traits such as drought tolerance is considered to be one of the most important and difficult challenges. Quantitative Trait Locus (QTL) mapping methods were widely applied in recent years to identify candidate genes related to complex traits. In this study, a strategy is described to identify novel gene families based on QTL information derived from published literature. Then, a database system for identifying candidate genes in rice and Arabidopsis by gene expression and QTL information is designed and implemented. A microarray analysis system for mapping QTL and microarray gene expression data is designed and implemented.QTLs such as drought-related QTL can be readily identified in available databases and literatures. These drought-related QTLs were collected and integrated into a QTL database and summarized in the form of a consensus map. An in silico strategy was then deployed to mine for candidate genes associated with QTLs using rice dbEST and rice genome databases. QTLs on rice chromosomes 1, 2, 4, 8, and 9 were selected to test the method. The result showed candidate genes associated with drought tolerance could be readily identified after experimental detection.In order to facilitate QTL based candidate gene identification and gene function analysis. We have designed and implemented a web-based database system, called PlantQTL-GE, We collected a large number of genes, gene expression information in microarray data and expressed sequence tags (ESTs) and genetic markers from multiple sources of Oryza sativa and Arabidopsis thaliana. The system integrates these diverse data sources and has a uniform web interface for easy access. It supports QTL queries specifying QTL marker intervals or genomic loci, and displays, on rice or Arabidopsis genome, known genes, microarray data, ESTs and candidate genes and similar putative genes in the other plant. Candidate genes in QTL intervals are further annotated based on matching ESTs, microarray gene expression data and cis-elements in regulatory sequences. The system is freely available at http://www.scbit.org/qtl2gene/new/.Microarray is a new technique that has been widely used for plant research. But few research report has beed published to identify candidate genes within QTL interval. In this thesis, we designed and implemented a software system, called PMBA, to facilitate QTL based candidate gene identification using gene expression information in microarray data in rice and Arabidopsis. Its functions include:1) Construction of a consensus map by integrating difference expression genes in microarray result and QTL information.2) Identifying chromosomal positions of difference expression genes in microarray and QTLs by use of BLAST tool;3) Gene ontology (GO) classification of difference expression genes within QTL interval.