Cancer gene discovery in mouse and man

The elucidation of the human and mouse genome sequence and developments in high-throughput genome analysis, and in computational tools, have made it possible to profile entire cancer genomes. In parallel with these advances mouse models of cancer have evolved into a powerful tool for cancer gene discovery. Here we discuss the approaches that may be used for cancer gene identification in both human and mouse and discuss how a cross-species ‘oncogenomics’ approach to cancer gene discovery represents a powerful strategy for finding genes that drive tumourigenesis.     

Cancer gene discovery in the mouse

Developments in high-throughput genome analysis and in computational tools have made it possible to rapidly profile entire cancer genomes with basepair resolution. In parallel with these advances, mouse models of cancer have evolved into powerful tools for cancer gene discovery. Here we discuss some of the approaches that may be used for cancer gene identification in the mouse and discuss how a cross-species 'oncogenomics' approach to cancer gene discovery represents a powerful strategy for finding genes that drive tumorigenesis.     

BEST: a novel computational approach for comparing gene expression patterns from early stages of Drosophila melanogaster development

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.     

Market opportunity in computational proteomics

The current exuberance on the potential of proteomics as a means to deploy the wealth of the human genome is expected to last into the coming years. Unlike the genome, a finite entity with a fixed number of base pairs of the genetic material, the proteome is "plastic", changing throughout growth and development and environmental stresses, as well as in pathological situations. Our proteomes change over time, and therefore there is no one proteome; the proteome is for practical purposes an infinite entity. It is therefore crucial to build systems that are capable of manipulating the information content that is the proteome, thence the need for computational proteomics as a discipline. In this Market View article, we present the industry landscape that is emerging in the computational proteomics space. This space is still in its infancy and for the most part undefined; therefore we seek to present the market opportunity in informatics in the drug discovery space and then extend that to an examination of industry trends in proteomics. Thus, the gestalt is a set of predictions as to the evolution of the landscape in computational proteomics over the coming years.     

New computational tools for brassica genome research

With the increasing quantities of Brassica genomic data being entered into the public domain and in preparation for the complete Brassica genome sequencing effort, there is a growing requirement for the structuring and detailed bioinformatic analysis of Brassica genomic information within a user-friendly database. At the Plant Biotechnology Centre, Melbourne, Australia, we have developed a series of tools and computational pipelines to assist in the processing and structuring of genomic data, to aid its application to agricultural biotechnology research. These tools include a sequence database, ASTRA, a sequence processing pipeline incorporating annotation against GenBank, SwissProt and Arabidopsis Gene Ontology (GO) data and tools for molecular marker discovery and comparative genome analysis. All sequences are mined for simple sequence repeat (SSR) molecular markers using 'SSR primer' and mapped onto the complete Arabidopsis thaliana genome by sequence comparison. The database may be queried using a text-based search of sequence annotation or GO terms, BLAST comparison against resident sequences, or by the position of candidate orthologues within the Arabidopsis genome. Tools have also been developed and applied to the discovery of single nucleotide polymorphism (SNP) molecular markers and the in silico mapping of Brassica BAC end sequences onto the Arabidopsis genome. Planned extensions to this resource include the integration of gene expression data and the development of an EnsEMBL-based genome viewer.     

Application of a systems approach to study developmental gene regulation

All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.     

Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation

MOTIVATION: The whole genomes submitted to GenBank contain valuable information about the function of genes as well as the upstream sequences and whole cell expression provides valuable information on gene regulation. To utilize these large amounts of data for a biological understanding of the regulation of gene expression, new automatic methods for pattern finding are needed. RESULTS: Two word-analysis algorithms for automatic discovery of regulatory sequence elements have been developed. We show that sequence patterns correlated to whole cell expression data can be found using Kolmogorov-Smirnov tests on the raw data, thereby eliminating the need for clustering co-regulated genes. Regulatory elements have also been identified by systematic calculations of the significance of correlations between words found in the functional annotation of genes and DNA words occurring in their promoter regions. Application of these algorithms to the Saccharomyces cerevisiae genome and publicly available DNA array data sets revealed a highly conserved 9-mer occurring in the upstream regions of genes coding for proteasomal subunits. Several other putative and known regulatory elements were also found. AVAILABILITY: Upon request.     

Discovery of regulatory elements in vertebrates through comparative genomics

We have analyzed issues of reliability in studies in which comparative genomic approaches have been applied to the discovery of regulatory elements at a genome-wide level in vertebrates. We point out some potential problems with such studies, including difficulties in accurately identifying orthologous promoter regions. Many of these subtle analytical problems have become apparent only when studying the more complex vertebrate genomes. By determining motif reliability, we compared existing tools when applied to the discovery of vertebrate regulatory elements. We then used a statistical clustering method to produce a computational catalog of high quality putative regulatory elements from vertebrates, some of which are widely conserved among vertebrates and many of which are novel regulatory elements. The results provide a glimpse into the wealth of information that comparative genomics can yield and suggest the need for further improvement of genome-wide comparative computational techniques.     

Computational approaches for the discovery of bacterial small RNAs

Recent work has uncovered a growing number of bacterial small RNAs (sRNAs), some of which have been shown to regulate critical cellular processes. Computational approaches, in combination with experiments, have played an important role in the discovery of these sRNAs. In this article, we first give an overview of different computational approaches for genome-wide prediction of sRNAs. These approaches have led to the discovery of several novel sRNAs, however the regulatory roles are not yet known for a majority of these sRNAs. By contrast, several recent studies have highlighted the inverse problem where the functional role of the sRNA is already known and the challenge is to identify its genomic location. The focus of this article is on computational tools and strategies for identifying these specific sRNAs which function as key components of known regulatory pathways.