Protein interaction verification and functional annotation by integrated analysis of genome-scale data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.     

Enhanced inhibition of splenic lymphocyte proliferation by 2,3,7,8-tetrachlorodibenzo-p-dioxin in C57B1/10 (Ah+Ah+) mice compared to DBA/2 (AhAh) mice

In concanavalin A-treated cultures TCDD (10(-8)M) inhibited proliferation of mouse spleen lymphocytes in cells of C57B1/10/mice by 65% and in cells of DBA/2 mice--by 42%. In phytohaemagglutinin-treated cultures of spleen lymphocytes of both strains TCDD produced no significant decrease in incorporation of 3H-thymidine into DNA. These observations indicate that effects of TCDD on lymphocyte proliferation are mediated by pleiotropic activity of Ah gene.     

Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods

We present a web resource MEM (Multi-Experiment Matrix) for gene expression similarity searches across many datasets. MEM features large collections of microarray datasets and utilizes rank aggregation to merge information from different datasets into a single global ordering with simultaneous statistical significance estimation. Unique features of MEM include automatic detection, characterization and visualization of datasets that includes the strongest coexpression patterns. MEM is freely available at .     

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation

The advent of genome-wide RNA interference (RNAi)–based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function—mitotic chromosome condensation—and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.     

Gene expression data analysis

Microarrays are one of the latest breakthroughs in experimental molecular biology, which allow monitoring of gene expression for tens of thousands of genes in parallel and are already producing huge amounts of valuable data. Analysis and handling of such data is becoming one of the major bottlenecks in the utilization of the technology. The raw microarray data are images, which have to be transformed into gene expression matrices--tables where rows represent genes, columns represent various samples such as tissues or experimental conditions, and numbers in each cell characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. In this paper we concentrate on discussing bioinformatics methods used for such analysis. We briefly discuss supervised and unsupervised data analysis and its applications, such as predicting gene function classes and cancer classification. Then we discuss how the gene expression matrix can be used to predict putative regulatory signals in the genome sequences. In conclusion we discuss some possible future directions.     

Structural studies of mannans from the cell walls of the pathogenic yeasts Candida albicans serotypes A and B and Candida parapsilosis

A comparative study of three cell-wall mannans, of Candida albicans serotypes A and B and Candida parapsilosis, by means of methylation analysis supports a model of yeast mannans as having an alpha-(1----6)-linked backbone with some units (depending on the origin of the mannan) being substituted at O-2 with oligosaccharides joined by alpha-(1---2) and, to a lesser extent, by alpha-(1----3) glycosidic bonds. Branching points in the side chains of Candida albicans mannans were found in substantial proportions for the first time, and the corresponding branched hexasaccharides were isolated by means of acetolysis and subsequent gel filtration. 13C-N.m.r. spectroscopy of the mannans, as well as a 1H-n.m.r. spectroscopic study of the oligosaccharides obtained on acetolysis of the mannans, led to results that agreed with those of methylation analysis.