Increasing numbers of methodologies are available to find functional genomic clusters in RNA expression data. We describe a technique that computes comprehensive pair-wise mutual information for all genes in such a data set. An association with a high mutual information means that one gene is non-randomly associated with another; we hypothesize this means the two are related biologically. By picking a threshold mutual information and using only associations at or above the threshold, we show how this technique was used on a public data set of 79 RNA expression measurements of 2,467 genes to construct 22 clusters, or Relevance Networks. The biological significance of each Relevance Network is explained.

Background: Organisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. Many proteins serve different roles depending on the demands of the organism, and therefore the corresponding genes are often coexpressed with different groups of genes under different situations. This poses a challenge in analyzing wholegenome expression data, because many genes will be similarly expressed to multiple, distinct groups of genes. Because most commonly used analytical methods cannot appropriately represent these relationships, the connections between conditionally coregulated genes are often missed.

Research article

Algorithms for performing feature extraction and normalization on high-density oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such low-level analysis methods are essential to increase the sensitivity and specificity of detecting whether genes are present and/or differentially expressed. We have developed and implemented a number of algorithms for the analysis of expression array data in a software application, the DNA-Chip Analyzer (dChip). In this report, we describe the algorithms for feature extraction and normalization, and present validation data and comparison results with some of the algorithms currently in use.

ABSTRACT Complex cellular processes are modular and are accomplished by the concerted action of functional modules (Ravasz et al., Science 2002;297:1551–1555; Hartwell et al., Nature 1999;402: C47–52). These modules encompass groups of genes or proteins involved in common elementary biological functions. One important and largely unsolved goal of functional genomics is the identification of functional modules from genomewide information, such as transcription profiles or protein interactions. To cope with the ever-increasing volume and complexity of protein interaction data (Bader et al., Nucleic Acids Res 2001;29:242–245; Xenarios et al., Nucleic Acids Res 2002;30:303–305), new automated approaches for pattern discovery in these densely connected interaction networks are required

The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the management and analysis of all areas of microarray experimentation. All software necessary to run a local server is freely available.

Objectives: To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods: Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results: The ontologies under investigation in this survey include

A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. In this review we provide an introduction and overview of the current state of the field. We discuss the main principles that underpin bioinformatics analyses, look at the types of biological information and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems. 2. Introduction Biological data are flooding in at an unprecedented rate (1). For example as of August 2000, the GenBank repository of nucleic acid sequences contained 8,214,000 entries (2) and the SWISS-PROT databas...

dy demonstrates that cDNA microarrays can be successfully used to define the transcriptional networks controlled by transcription factors in vertebrate tissue in vivo. Background Studiesof neural development have highlighted the role of cell- and tissue-specify transcriptionfransc in regulating both cell fly determination events and the later morphological stagesof neuronal difnaly6jAzzyT [1]. Little is known, however, about the gene expression or transcriptional networks regulated by thesefesey6 or those controlling cell fll determination. In the developing vertebrate retina, several transcription ftrans have been implicated in thedifM/6AyT--MzM/ of specif - cell types, including the paired-typefaire member Chx10 (bipolar neurons; [2]) and the POU-domain transcriptionfript fipti member Brn-3b (subtypeof ganglion cells [3]). The transcriptionfrans Crx (cone, rod homeobox) has a pivotal role in the morphologicaldifpho entiationof both rod and cone pho

This paper reviews applications of supervised methods in the analysis of microarray experiments with special reference to pharmacogenomics. It is widely appreciated that there are many important applications of microarrays in pharmacogenomics, for example: . molecular target identification and drug discovery . toxicology . molecular diagnostics The massive amount of data generated by genomic methods has led to a need for computational methods to manage and analyze this data and the methods used will influence the results and their interpretation. The data mining tools employed range from various clustering techniques to supervised learning schemes [9]. The main emphasis of this review is on supervised classifications methods, a brief summary of some of the unsupervised methods used for array analysis is also given. This will provide some necessary requisites for the following discussion on the advantages of using supervised methods in the context of pharmacogenomics. The most common methods used to analyze array data are listed in Tab e 1