Dr. Xiu-Jie Wang is currently an investigator at Institute of Genetics and Developmental Biology (IGDB), Chinese Academy of Sciences. Dr. Wang received her bachelor and master degrees in biology, and earned her Ph.D. in bioinformatics from The Rockefeller University in 2004.

Since joining IGDB at the beginning of 2005, Dr. Wang has been heading a research group working on bioinformatics and systems biology, with an emphasis on non-coding RNA prediction and functional study as well as transcriptomic data analysis. Dr. Wang and her research group have developed a series of algorithms to predict new non-coding RNAs, and to identify the expression regulatory mechanisms and potential functions of non-coding RNAs in various species, including Arabidopsis, rice, chicken, human, mouse and some viruses. The ultimate goal of her research group is to develop novel computational methods to analyze the fast increasing genomic, transcriptomic, proteomic and other large-scale biological data, to identify novel non-coding regulatory RNA genes in eukaryotic genomes, to decipher their transcription regulatory mechanisms and to construct non-coding RNA involved gene regulatory networks. Dr. Xiu-Jie Wang received NSFC Outstanding Young Scientist Award and DuPont Young Scientist Award in 2007.

Dr. Wang has published many papers in top journals, including Nature and Genome Biology.

Tentative Title

GOEAST: A Web-based Software Toolkit for Gene Ontology Enrichment Analysis

Abstract

Gene Ontology (GO) analysis has become a commonly used approach for functional studies of large scale genomic or transcriptomic data. Although there have been a lot of software with GO-related analysis functions, new tools are still needed to meet the requirements for data generated by newly developed technologies or for advanced analysis purpose. Here we present GOEAST, an easy-to-use web-based toolkit that identifies statistically over-represented GO terms within given gene sets. Compared with available GO analysis tools, GOEAST has the following improved features: (1) GOEAST displays enriched GO terms in graphical format according to their relationships in the hierarchical-tree of each GO category (biological process, molecular function, cellular component), therefore provides better understanding of the correlations among enriched GO terms; (2) GOEAST supports analysis for data from various sources (probe or probe set IDs of Affymetrix, Illumina, Agilent, or customized microarrays, as well as different gene identifiers) and multiple species (about 60 prokaryote and eukaryote species); (3) One unique feature of GOEAST is to allow cross comparison of the GO enrichment status of multiple experiments to identify functional correlations among them. GOEAST also provides rigorous statistical tests to enhance the reliability of analysis results. GOEAST is freely accessible at http://omicslab.genetics.ac.cn/GOEAST/.