An improved algorithm for the detection of genomic variation using short oligonucleotide expression microarrays

High‐throughput microarray experiments often generate far more biological information than is required to test the experimental hypotheses. Many microarray analyses are considered finished after differential expression and additional analyses are typically not performed, leaving untapped biological information left undiscovered. This is especially true if the microarray experiment is from an ecological study of multiple populations. Comparisons across populations may also contain important genomic polymorphisms, and a subset of these polymorphisms may be identified with microarrays using techniques for the detection of single feature polymorphisms (SFP). SFPs are differences in microarray probe level intensities caused by genetic polymorphisms such as single‐nucleotide polymorphisms and small insertions/deletions and not expression differences. In this study, we provide a new algorithm for the detection of SFPs, evaluate the algorithm using existing data from two publicly available Affymetrix Barley (Hordeum vulgare) microarray data sets and compare them to two previously published SFP detection algorithms. Results show that our algorithm provides more consistent and sensitive calling of SFPs with a lower false discovery rate. Simultaneous analysis of SFPs and differential expression is a low‐cost method for the enhanced analysis of microarray data, enabling additional biological inferences to be made.     

Identification and molecular characterization of novel anther-specific genes in Oryza sativa L. by using cDNA microarray

The complicated genetic pathway regulates the developmental programs of male reproductive organ, anther tissues. To understand these molecular mechanisms, we performed cDNA microarray analyses and in situ hybridization to monitor gene expression patterns during anther development in rice. Microarray analysis of 4,304 cDNA clones revealed that the hybridization signal of 396 cDNA clones (271 non-redundant groups) increased more than six-fold in every stage of the anthers compared with that of leaves. Cluster analysis with the expression data showed that 259 cDNA clones (156 non redundant groups) were specifically or predominantly expressed in anther tissues and were regulated by developmental stage-specific manners in the anther tissues. These co-regulated genes would be important for development of functional anther tissues. Furthermore, we selected several clones for RNA in situ hybridization analysis. From these analyses, we found several novel genes that show temporal and spatial expression patterns during anther development in addition to anther-specific genes reported so far. These results indicate that the genes identified in this experiment are controlled by different programs and are specialized in their developmental and cell types.     

Improved Microarray-Based Decision Support with Graph Encoded Interactome Data

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.     

Integrative analysis of multiple gene expression profiles applied to liver cancer study

A statistical method for combining multiple microarray studies has been previously developed by the authors. Here, we present the application of the method to our hepatocellular carcinoma (HCC) data and report new findings on gene expression changes accompanying HCC. From the cross-verification result of our studies and that of published studies, we found that single microarray analysis might lead to false findings. To avoid those pitfalls of single-set analyses, we employed our effect size method to integrate multiple datasets. Of 9982 genes analyzed, 477 significant genes were identified with a false discovery rate of 10%. Gene ontology (GO) terms associated with these genes were explored to validate our method in the biological context with respect to HCC. Furthermore, it was demonstrated that the data integration process increases the sensitivity of analysis and allows small but consistent expression changes to be detected. These integration-driven discoveries contained meaningful and interesting genes not reported in previous expression profiling studies, such as growth hormone receptor, erythropoietin receptor, tissue factor pathway inhibitor-2, etc. Our findings support the use of meta-analysis for a variety of microarray data beyond the scope of this specific application.     

The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized,curated gateway to Arabidopsis biology,research materials and community

Arabidopsis thaliana is the most widely-studied plant today. The concerted efforts of over 11 000 researchers and 4000 organizations around the world are generating a rich diversity and quantity of information and materials. This information is made available through a comprehensive on-line resource called the Arabidopsis Information Resource (TAIR) (http://arabidopsis.org), which is accessible via commonly used web browsers and can be searched and downloaded in a number of ways. In the last two years, efforts have been focused on increasing data content and diversity, functionally annotating genes and gene products with controlled vocabularies, and improving data retrieval, analysis and visualization tools. New information include sequence polymorphisms including alleles, germplasms and phenotypes, Gene Ontology annotations, gene families, protein information, metabolic pathways, gene expression data from microarray experiments and seed and DNA stocks. New data visualization and analysis tools include SeqViewer, which interactively displays the genome from the whole chromosome down to 10 kb of nucleotide sequence and AraCyc, a metabolic pathway database and map tool that allows overlaying expression data onto the pathway diagrams. Finally, we have recently incorporated seed and DNA stock information from the Arabidopsis Biological Resource Center (ABRC) and implemented a shopping-cart style on-line ordering system.     

基因表达谱芯片的数据挖掘

随着基因芯片技术的迅速发展,表达谱芯片分析及aCGH等方法已被广泛应用于生命科学各个研究领域,由此产生的数据也呈指数级增长。如何从海量数据中获取有生物学意义的结果成为摆在生物学工作者面前的难题。对表达谱芯片数据挖掘方法进行了综述。介绍了基本分析思路,当前重点分析方向,如GO分析、pathway与调控网络分析、聚类分析等计算法则和相关几款易用的分析软件。并介绍了几种科学自由计算软件在表达谱生物信息学分析中的应用。藉此为从事芯片分析的研究人员提供参考。     

A cut-off based approach for gene expression analysis of formalin-fixed and paraffin-embedded tissue samples

Microarray analysis of formalin-fixed and paraffin-embedded (FFPE) tissue seems to be of importance for the detection of molecular marker sets in prostate cancer (PC). The compromised RNA integrity of FFPE tissue results in a high degree of variability at the probe level of microarray data as shown by degradation plot. We tested methods that reduce the variability by including all probes within 300 nucleotides, within 600 nucleotides, or up to a calculated breakpoint with reference to the 3'-end. Accepted PC pathways such as the Wnt signaling pathway could be observed to be significantly regulated within FFPE microarray datasets. The best representation of PC gene expression, as well as better comparability to meta-analysis and fresh-frozen microarray data, could be obtained with a 600-nucleotide cutoff. Beyond the specific impact for PC microarray data analysis we propose a cutoff of 600 nucleotides for samples for which the integrity of the RNA cannot be guaranteed.     

用生物信息学方法处理基因芯片结果

基因芯片是一种能够同时检测大量基因在同一组织中表达情况的有力工具.利用前期工作筛选的2210个鼻咽癌差异表达基因和Biocarta信号通路资源库,构建了一个基于信号通路的基因相互作用网络.通过统计学分析,进一步筛选出一批对该基因相互作用网络具有重大影响的基因(特别是RAN、CEL、RELA).随后,采用RT-PCR方法检测候选基因在鼻咽癌活检组织中的表达,发现RAN和CEL基因在高达80%的鼻咽癌组织中高表达.进一步将网络分析结果和ArrayXPath软件分析的结果比较,共计有40%(32/80)基因结果吻合,这验证了网络分析方法的有效性和可行性.最终探索建立了新的分析基因芯片的方法.     

Prediction of regulatory pathways using mRNA expression and protein interaction data: application to identification of galactose regulatory pathway

We propose a novel technique that constructs gene regulatory networks from DNA microarray data and gene-protein databases and then applies Mason rule to systematically search for the most dominant regulators of the network. The algorithm then recommends the identified dominant regulator genes as the best candidates for future knock-out experiments. Actively choosing the genes for knock-out experiments allows optimal perturbation of the pathway and therefore produces the most informative DNA microarray data for pathway identification purposes. This approach is more practically advantageous in analysis of large pathways where the time and cost of DNA microarray data experiments can be reduced using the proposed optimal experiment design. The proposed method was successfully tested on the galactose regulatory network.     

A robust measure of correlation between two genes on a microarray

Background  

The underlying goal of microarray experiments is to identify gene expression patterns across different experimental conditions. Genes that are contained in a particular pathway or that respond similarly to experimental conditions could be co-expressed and show similar patterns of expression on a microarray. Using any of a variety of clustering methods or gene network analyses we can partition genes of interest into groups, clusters, or modules based on measures of similarity. Typically, Pearson correlation is used to measure distance (or similarity) before implementing a clustering algorithm. Pearson correlation is quite susceptible to outliers, however, an unfortunate characteristic when dealing with microarray data (well known to be typically quite noisy.)     

Biochemical systems analysis of genome-wide expression data

MOTIVATION: Modern methods of genomics have produced an unprecedented amount of raw data. The interpretation and explanation of these data constitute a major, well-recognized challenge. RESULTS: Biochemical Systems Theory (BST) is the mathematical basis of a well-established methodological framework for analyzing networks of biochemical reactions. An existing BST model of yeast glycolysis is used here to explain and interpret the glycolytic gene expression pattern of heat shocked yeast. Our analysis demonstrates that the observed gene expression profile satisfies the primary goals of increased ATP, trehalose, and NADPH production, while maintaining intermediate metabolites at reasonable levels. Based on a systematic exploration of alternative, hypothetical expression profiles, we show that the observed profile outperforms other profiles. Conclusion: BST is a useful framework for combining DNA microarray data with enzymatic process information to yield new insights into metabolic pathway regulation. AVAILABILITY: All analyses were executed with the software PLAS(Copyright), which is freely available at http://correio.cc.fc.ul.pt/~aenf/plas.html for academic use. CONTACT: VoitEO@MUSC.edu