Biomedical subjects

Xizeng Mao

Publications and source records attributed to Xizeng Mao.

4 recordsLinked to original sources

PubMed2006

Genome comparison using Gene Ontology (GO) with statistical testing.

BACKGROUND: Automated comparison of complete sets of genes encoded in two genomes can provide insight on the genetic basis of differences in biological traits between species. Gene ontology (GO) is used as a common vocabulary to annotate genes for comparison. Current approaches calculate the fold of unweighted or weighted differences between two species at the high-level GO functional categories. However, to ensure the reliability of the differences detected, it is important to evaluate their statistical significance. It is also useful to search for differences at all levels of GO. RESULTS: We propose a statistical approach to find reliable differences between the complete sets of genes encoded in two genomes at all levels of GO. The genes are first assigned GO terms from BLAST searches against genes with known GO assignments, and for each GO term the abundance of genes in the two genomes is compared using a chi-squared test followed by false discovery rate (FDR) correction. We applied this method to find statistically significant differences between two cyanobacteria, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120. We then studied how the set of identified differences vary when different BLAST cutoffs are used. We also studied how the results vary when only subsets of the genes were used in the comparison of human vs. mouse and that of Saccharomyces cerevisiae vs. Schizosaccharomyces pombe. CONCLUSION: There is a surprising lack of statistical approaches for comparing complete genomes at all levels of GO. With the rapid increase of the number of sequenced genomes, we hope that the approach we proposed and tested can make valuable contribution to comparative genomics.

Anabaena↗

PubMed2006

KOBAS server: a web-based platform for automated annotation and pathway identification.

There is an increasing need to automatically annotate a set of genes or proteins (from genome sequencing, DNA microarray analysis or protein 2D gel experiments) using controlled vocabularies and identify the pathways involved, especially the statistically enriched pathways. We have previously demonstrated the KEGG Orthology (KO) as an effective alternative controlled vocabulary and developed a standalone KO-Based Annotation System (KOBAS). Here we report a KOBAS server with a friendly web-based user interface and enhanced functionalities. The server can support input by nucleotide or amino acid sequences or by sequence identifiers in popular databases and can annotate the input with KO terms and KEGG pathways by BLAST sequence similarity or directly ID mapping to genes with known annotations. The server can then identify both frequent and statistically enriched pathways, offering the choices of four statistical tests and the option of multiple testing correction. The server also has a 'User Space' in which frequent users may store and manage their data and results online. We demonstrate the usability of the server by finding statistically enriched pathways in a set of upregulated genes in Alzheimer's Disease (AD) hippocampal cornu ammonis 1 (CA1). KOBAS server can be accessed at http://kobas.cbi.pku.edu.cn.

Alzheimer Disease↗

PubMed2006

Molecular analysis of early rice stamen development using organ-specific gene expression profiling.

Elucidating the regulatory mechanisms of plant organ formation is an important component of plant developmental biology and will be useful for crop improvement applications. Plant organ formation, or organogenesis, occurs when a group of primordial cells differentiates into an organ, through a well-orchestrated series of events, with a given shape, structure and function. Research over the past two decades has elucidated the molecular mechanisms of organ identity and dorsalventral axis determinations. However, little is known about the molecular mechanisms underlying the successive processes. To develop an effective approach for studying organ formation at the molecular level, we generated organ-specific gene expression profiles (GEPs) reflecting early development in rice stamen. In this study, we demonstrated that the GEPs are highly correlated with early stamen development, suggesting that this analysis is useful for dissecting stamen development regulation. Based on the molecular and morphological correlation, we found that over 26 genes, that were preferentially up-regulated during early stamen development, may participate in stamen development regulation. In addition, we found that differentially expressed genes during early stamen development are clustered into two clades, suggesting that stamen development may comprise of two distinct phases of pattern formation and cellular differentiation. Moreover, the organ-specific quantitative changes in gene expression levels may play a critical role for regulating plant organ formation.

Cluster Analysis↗

PubMed2005

Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary.

MOTIVATION: High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways. RESULTS: We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.

Artificial Intelligence↗