High resolution in vivo 31P-MRS of the liver: potential advantages in the assessment of non-alcoholic fatty liver disease.

BACKGROUND: Biopsy remains the current gold-standard for assessing non-alcoholic fatty liver disease (NAFLD). To develop a non-invasive means of assessing the disease, 31P magnetic resonance spectroscopy (31P-MRS) has been explored, but the severe spectral overlaps and low signal-to-noise-ratio in 31P-MRS spectra at clinical field strength are clearly limiting factors. PURPOSE: To investigate potential advantages of high resolution in vivo 31P-MRS in assessing NAFLD. MATERIAL AND METHODS: The study was conducted at 9.4T in control and carbon tetrachloride (CCl4)-treated rats. Rats were divided according to histopathologic findings into a control group (n = 15), a non-alcoholic steatohepatitis group (n = 17), and a cirrhosis group (n = 12). Data were presented with different reference peaks that are commonly used for peak normalization such as total phosphorous signal, phosphomonoester + phosphodiester (PME + PDE), and nucleotide triphosphate (NTP). Then, multivariate analyses were performed. RESULTS: In all spectra PME and PDE were well resolved into phosphoethanolamine (PE) and phosphocholine (PC), and into glycerophosphorylethanolamine (GPE) and glycerophosphorylcholine (GPC), respectively. Those MRS measures quantifiable only in highly resolved spectra had higher correlations with histology than those conventional MRS measures such as PME, PDE, and NTP. The optimized partial least-squares discriminant analysis (PLS-DA) model correctly classified 79% (22/28) of the rats in the training set and correctly predicted 69% (11/16) of the rats in the test set. CONCLUSION: PE, PC, GPE, GPC, and nicotinamide adenine dinucleotide phosphate (NADP) that can be separately quantifiable in highly resolved spectra may further improve the potential efficacy of 31P-MRS in the diagnosis of NAFLD.

GoBean: a Java GUI application for visual exploration of GO term enrichments.

We have developed a biologist-friendly, Java GUI application (GoBean) for GO term enrichment analysis. It was designed to be a comprehensive and flexible GUI tool for GO term enrichment analysis, combining the merits of other programs and incorporating extensive graphic exploration of enrichment results. An intuitive user interface with multiple panels allows for extensive visual scrutiny of analysis results. The program includes many essential and useful features, such as enrichment analysis algorithms, multiple test correction methods, and versatile filtering of enriched GO terms for more focused analyses. A unique graphic interface reflecting the GO tree structure was devised to facilitate comparisons of multiple GO analysis results, which can provide valuable insights for biological interpretation. Additional features to enhance user convenience include built in ID conversion, evidence code-based gene-GO association filtering, set operations of gene lists and enriched GO terms, and user -provided data files. It is available at http://neon.gachon.ac.kr/GoBean/.

IdBean: a Java GUI application for conversion of biological identifiers.

We have developed a biologist-friendly, stand-alone Java GUI application, IdBean, for ID conversion. Our tool integrated most of the widely used ID conversion services that provide programmatic access. It is the first GUI ID conversion application that supports the direct merging as well as comparison of conversion results from multiple ID conversion services without manual effort. This tool will greatly help biologists who handle multiple ID types for the analyses of gene or gene product lists. By referring to multiple conversion services, the number of failed IDs can be reduced. By accessing ID conversion service online, it will potentially provide the most up-to-date conversion results. The application was developed in modular form; however, it can be re-packaged into plug-in form. For the development of a bioinformatics analysis tool, the module can be used as a built-in ID conversion component. It is available at http://neon.gachon.ac.kr/IdBean/.

GARNET--gene set analysis with exploration of annotation relations.

BACKGROUND: Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. RESULTS: GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. CONCLUSIONS: GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

Differential coexpression analysis using microarray data and its application to human cancer.

MOTIVATION: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.

TO-GO: a Java-based Gene Ontology navigation environment.

UNLABELLED: TO-GO is a Gene Ontology (GO) navigation tool, which is implemented as a Java application. After the initial data downloading, the GO term tree can be interactively navigated without further network transfer. Local annotation can be incorporated. It supports querying by GO terms or associated gene product information, displaying the result as a table or a sub-tree. The result from the search for a set of external database accessions includes the number of gene products associated with each node, inclusive of sub-nodes. Search results can be further processed by set operations and these set operations can be quite useful for expression profile data analysis. A copy/paste function is also implemented in order to facilitate data exchange between applications. AVAILABILITY: TO-GO is freely available at http://www.ngic.re.kr/togo/index.html CONTACT: ungsik@kribb.re.kr

Bioinformatics in the post-genome era.

Recent years saw a dramatic increase in genomic and proteomic data in public archives. Now with the complete genome sequences of human and other species in hand, detailed analyses of the genome sequences will undoubtedly improve our understanding of biological systems and at the same time require sophisticated bioinformatic tools. Here we review what computational challenges are ahead and what are the new exciting developments in this exciting field.

Combining multiple microarray studies and modeling interstudy variation.

We have established a method for systematic integration of multiple microarray datasets. The method was applied to two different sets of cancer profiling studies. The change of gene expression in cancer was expressed as 'effect size', a standardized index measuring the magnitude of a treatment or covariate effect. The effect sizes were combined to obtain the estimate of the overall mean. The statistical significance was determined by a permutation test extended to multiple datasets. It was shown that the data integration promotes the discovery of small but consistent expression changes with increased sensitivity and reliability. The effect size methods provided the efficient modeling framework for addressing interstudy variation as well. Based on the result of homogeneity tests, a fixed effects model was adopted for one set of datasets that had been created in controlled experimental conditions. By contrast, a random effects model was shown to be appropriate for the other set of datasets that had been published by independent groups. We also developed an alternative modeling procedure based on a Bayesian approach, which would offer flexibility and robustness compared to the classical procedure.