Genomic resources for flatfish research and their applications

Flatfishes are a group of teleosts of high commercial and environmental interest, whose biology is still poorly understood. The recent rapid development of different 'omic' technologies is, however, enhancing the knowledge of the complex genetic control underlying different physiological processes of flatfishes. This review describes the different functional genomic approaches and resources currently available for flatfish research and summarizes different areas where microarray-based gene expression analysis has been applied. The increase in genome sequencing data has also allowed the construction of genetic linkage maps in different flatfish species; these maps are invaluable for investigating genome organization and identifying genetic traits of commercial interest. Despite the significant progress in this field, the genomic resources currently available for flatfish are still scarce. Further intensive research should be carried out to develop larger genomic sequence databases, high-density microarrays and, more detailed, complete linkage maps, using second-generation sequencing platforms. These tools will be crucial for further expanding the knowledge of flatfish physiology, and it is predicted that they will have important implications for wild fish population management, improved fish welfare and increased productivity in aquaculture.     

Technologies for the study of gene and protein expression in Plasmodium

With the imminent completion of the genome sequences of several species of Plasmodium, attention is now turning to the exploitation of these genomic sequence data for vaccine, drug and diagnostic development. Several technologies have been developed over the past decade to assist in the determination of gene and protein expression on a global scale. Of these, DNA microarrays, novel high-throughput proteomic technologies and recombinational cloning technologies are lowering the barrier to functional genomic studies in Plasmodium. Of equal importance is the capacity to manipulate, store, retrieve and analyse the tremendous quantity of data generated from these genomic studies. This paper will address the use of these technologies as well as some of the computational tools that will be ultimately required to adequately study gene and protein expression in Plasmodium.     

Web Tools for Rice Transcriptome Analyses

Gene expression databases provide profiling data for the expression of thousands of genes to researchers worldwide. Oligonucleotide microarray technology is a useful tool that has been employed to produce gene expression profiles in most species. In rice, there are five genome-wide DNA microarray platforms: NSF 45K, BGI/Yale 60K, Affymetrix, Agilent Rice 44K, and NimbleGen 390K. Presently, more than 1,700 hybridizations of microarray gene expression data are available from public microarray depositing databases such as NCBI gene expression omnibus and Arrayexpress at EBI. More processing or reformatting of public gene expression data is required for further applications or analyses. Web-based databases for expression meta-analyses are useful for guiding researchers in designing relevant research schemes. In this review, we summarize various databases for expression meta-analyses of rice genes and web tools for further applications, such as the development of co-expression network or functional gene network.     

Advances in plant cell type-specific genome-wide studies of gene expression

Cell is the functional unit of life.To study the complex interactions of systems of biological molecules,it is crucial to dissect these molecules at the cell level.In recent years,major progresses have been made by plant biologists to profile gene expression in specific cell types at the genome-wide level.Approaches based on the isolation of cells,polysomes or nuclei have been developed and successfully used for studying the cell types from distinct organs of several plant species.These cell-level data sets revealed previously unrecognized cellular properties,such as cell-specific gene expression modules and hormone response centers,and should serve as essential resources for functional genomic analyses.Newly developed technologies are more affordable to many laboratories and should help to provide new insights at the cellular resolution in the near future.     

Genome-wide identification studies – A primer to explore new genes in plant species

Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.     

Putting microarrays in a context: integrated analysis of diverse biological data

In recent years, multiple types of high-throughput functional genomic data that facilitate rapid functional annotation of sequenced genomes have become available. Gene expression microarrays are the most commonly available source of such data. However, genomic data often sacrifice specificity for scale, yielding very large quantities of relatively lower-quality data than traditional experimental methods. Thus sophisticated analysis methods are necessary to make accurate functional interpretation of these large-scale data sets. This review presents an overview of recently developed methods that integrate the analysis of microarray data with sequence, interaction, localisation and literature data, and further outlines current challenges in the field. The focus of this review is on the use of such methods for gene function prediction, understanding of protein regulation and modelling of biological networks.     

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

Information regarding gene coexpression is useful to predict gene function. Several databases have been constructed for gene coexpression in model organisms based on a large amount of publicly available gene expression data measured by GeneChip platforms. In these databases, Pearson''s correlation coefficients (PCCs) of gene expression patterns are widely used as a measure of gene coexpression. Although the coexpression measure or GeneChip summarization method affects the performance of the gene coexpression database, previous studies for these calculation procedures were tested with only a small number of samples and a particular species. To evaluate the effectiveness of coexpression measures, assessments with large-scale microarray data are required. We first examined characteristics of PCC and found that the optimal PCC threshold to retrieve functionally related genes was affected by the method of gene expression database construction and the target gene function. In addition, we found that this problem could be overcome when we used correlation ranks instead of correlation values. This observation was evaluated by large-scale gene expression data for four species: Arabidopsis, human, mouse and rat.     

Genome-wide expression analysis of plant cell cycle modulated genes

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analysing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues. The control of cell division involves the temporal expression of different sets of genes, allowing the dividing cell to progress through the different phases of the cell cycle. A landmark study using DNA microarrays to follow the patterns of gene expression in synchronously dividing yeast cells has allowed the identification of several hundreds of genes that are involved in the cell cycle. Although DNA microarrays provide a convenient tool for genome-wide expression analysis, their use is limited to organisms for which the complete genome sequence or a large cDNA collection is available. For other organisms, including most plant species, DNA fragment analysis based methods, such as cDNA-AFLP, provide a more appropriate tool for genome-wide expression analysis. Furthermore, cDNA-AFLP exhibits properties that complement DNA microarrays and, hence, constitutes a useful tool for gene discovery.     

Gene discovery using the maize genome database ZmDB

Zea mays DataBase (ZmDB) is a repository and analysis tool for sequence, expression and phenotype data of the major crop plant maize. The data accessible in ZmDB are mostly generated in a large collaborative project of maize gene discovery, sequencing and phenotypic analysis using a transposon tagging strategy and expressed sequence tag (EST) sequencing. ESTs constitute most of the current content. Database search tools, convenient links to external databases, and novel sequence analysis programs for spliced alignment are provided and together serve as an efficient protocol for gene discovery by sequence inspection. ZmDB can be accessed at http://zmdb. iastate.edu. ZmDB also provides web-based ordering of materials generated in the project, including EST and genomic DNA clones, seeds of mutant plants and microarrays of amplified EST and genomic DNA sequences.     

Lessons from a decade of integrating cancer copy number alterations with gene expression profiles

Over the last decade, multiple functional genomic datasets studying chromosomal aberrations and their downstream effects on gene expression have accumulated for several cancer types. A vast majority of them are in the form of paired gene expression profiles and somatic copy number alterations (CNA) information on the same patients identified using microarray platforms. In response, many algorithms and software packages are available for integrating these paired data. Surprisingly, there has been no serious attempt to review the currently available methodologies or the novel insights brought using them. In this work, we discuss the quantitative relationships observed between CNA and gene expression in multiple cancer types and biological milestones achieved using the available methodologies. We discuss the conceptual evolution of both, the step-wise and the joint data integration methodologies over the last decade. We conclude by providing suggestions for building efficient data integration methodologies and asking further biological questions.