Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data

Background  

The association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited to analysis of large gene-expression matrices. To validate the ARD technique we have applied it to freely available human serial analysis of gene expression (SAGE) data.     

Analysis of large-scale gene expression data.

DNA microarray technology has resulted in the generation of large complex data sets, such that the bottleneck in biological investigation has shifted from data generation, to data analysis. This review discusses some of the algorithms and tools for the analysis and organisation of microarray expression data, including clustering methods, partitioning methods, and methods for correlating expression data to other biological data.     

A novel, high stringency selection system allows screening of few clones for high protein expression

To obtain highly productive mammalian cell lines, often large numbers of clones need to be screened. This is largely due to low selection stringencies, creating many, but low protein producing clones. To remedy this problem, a novel, very stringent selection system was designed, to create few, but high protein producing clones. In essence, a selection marker with a startcodon that confers attenuated translation initiation frequency was placed upstream of the gene of interest with a startcodon that confers optimal translation initiation. From the transcribed bicistronic mRNA, the selection marker is translated at a low frequency, and the protein of interest at a high frequency. This selection system is so stringent that clones form only rarely. However, application of anti-repressor elements, which increase promoter activity, did induce the formation of clones that expressed proteins at high levels. When combined with anti-repressor elements, this novel selection system can be a valuable tool to rapidly create few, but highly productive mammalian cell lines.     

A large-scale screening of the normalized mammalian mitochondrial gene expression profiles

Mammalian mitochondrial genomes are organized in a conserved and extremely compact manner, encoding molecules that play a vital role in oxidative phosphorylation (OXPHOS) and carry out a number of other important biological functions. A large-scale screening of the normalized mitochondrial gene expression profiles generated from publicly available mammalian serial analysis of gene expression (SAGE) datasets (over 17.7 millions of tags) was performed in this study. Acquired SAGE libraries represent an extensive range of human, mouse, rat, bovine and swine cell and tissue samples (normal and pathological) in a variety of conditions. Using a straightforward in silico algorithm, variations in total mitochondrial gene expression, as well as in the expression of individual genes encoded by mitochondrial genomes are addressed, and common patterns in the species- and tissue-specific mitochondrial gene expression profiles are discussed.     

Mining microarray expression data by literature profiling

Background  

The rapidly expanding fields of genomics and proteomics have prompted the development of computational methods for managing, analyzing and visualizing expression data derived from microarray screening. Nevertheless, the lack of efficient techniques for assessing the biological implications of gene-expression data remains an important obstacle in exploiting this information.     

天然反义转录物及其调控基因的表达机制

天然反义转录(NATs)是一组编码蛋白质或非编码蛋白质的RNAs, 与其他(有义)转录物具有互补序列, 可以调节有义链的表达。这种调节可以发生在转录水平或转录后水平, 调节方式有转录干扰、RNA封闭、双链依赖机制或染色质重建(修饰)等。正义链和反义链分别加工成小RNAs调节基因表达, 也是NATs调节基因表达的重要方式, 如piRNAs的“乒乓机制”。实验或计算机研究已经证明了NATs在生物中广泛存在, 是一种重要的基因表达调节方式。文章论述了NATs的重要作用和机理, 重点论述了NATs的调节机制和相关的小RNAs。     

Assessment and integration of publicly available SAGE, cDNA microarray, and oligonucleotide microarray expression data for global coexpression analyses

Large amounts of gene expression data from several different technologies are becoming available to the scientific community. A common practice is to use these data to calculate global gene coexpression for validation or integration of other "omic" data. To assess the utility of publicly available datasets for this purpose we have analyzed Homo sapiens data from 1202 cDNA microarray experiments, 242 SAGE libraries, and 667 Affymetrix oligonucleotide microarray experiments. The three datasets compared demonstrate significant but low levels of global concordance (rc<0.11). Assessment against Gene Ontology (GO) revealed that all three platforms identify more coexpressed gene pairs with common biological processes than expected by chance. As the Pearson correlation for a gene pair increased it was more likely to be confirmed by GO. The Affymetrix dataset performed best individually with gene pairs of correlation 0.9-1.0 confirmed by GO in 74% of cases. However, in all cases, gene pairs confirmed by multiple platforms were more likely to be confirmed by GO. We show that combining results from different expression platforms increases reliability of coexpression. A comparison with other recently published coexpression studies found similar results in terms of performance against GO but with each method producing distinctly different gene pair lists.     

Effective dimensionality of large-scale expression data using principal component analysis

Large-scale expression data are today measured for thousands of genes simultaneously. This development is followed by an exploration of theoretical tools to get as much information out of these data as possible. One line is to try to extract the underlying regulatory network. The models used thus far, however, contain many parameters, and a careful investigation is necessary in order not to over-fit the models. We employ principal component analysis to show how, in the context of linear additive models, one can get a rough estimate of the effective dimensionality (the number of information-carrying dimensions) of large-scale gene expression datasets. We treat both the lack of independence of different measurements in a time series and the fact that that measurements are subject to some level of noise, both of which reduce the effective dimensionality and thereby constrain the complexity of models which can be built from the data.