Coevolution of activating and inhibitory receptors within mammalian carcinoembryonic antigen families

Background  

Most rapidly evolving gene families are involved in immune responses and reproduction, two biological functions which have been assigned to the carcinoembryonic antigen (CEA) gene family. To gain insights into evolutionary forces shaping the CEA gene family we have analysed this gene family in 27 mammalian species including monotreme and marsupial lineages.     

Global analysis of microRNA target gene expression reveals the potential roles of microRNAs in maintaining tissue identity

Background  

MicroRNAs are non-coding small RNAs of ~22 nucleotides that regulate the gene expression by base-paring with target mRNAs, leading to mRNA cleavage or translational repression. It is currently estimated that microRNAs account for ~ 1% of predicted genes in higher eukaryotic genomes and that up to 30% of genes might be regulated by microRNAs. However, only very few microRNAs have been functionally characterized and the general functions of microRNAs are not globally studied.     

Prediction of breast cancer prognosis using gene set statistics provides signature stability and biological context

Background  

Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from any one study are unstable. It has been suggested that the underlying pathways are essentially identical, and that the expression of gene sets, rather than that of individual genes, may be more informative with respect to prognosis and understanding of the underlying biological process.     

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.     

A survey of orphan enzyme activities

Background  

Using computational database searches, we have demonstrated previously that no gene sequences could be found for at least 36% of enzyme activities that have been assigned an Enzyme Commission number. Here we present a follow-up literature-based survey involving a statistically significant sample of such "orphan" activities. The survey was intended to determine whether sequences for these enzyme activities are truly unknown, or whether these sequences are absent from the public sequence databases but can be found in the literature.     

Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

Background  

Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene sets can provide clues on which functional themes or regulatory sequence motifs are associated with individual gene clusters. In spite of the potential utility, gene clustering and enrichment analysis have been used in separate platforms, thus, the development of integrative algorithm linking both methods is highly challenging.     

The sea lamprey Petromyzon marinus genome reveals the early origin of several chemosensory receptor families in the vertebrate lineage

Background  

In gnathostomes, chemosensory receptors (CR) expressed in olfactory epithelia are encoded by evolutionarily dynamic gene families encoding odorant receptors (OR), trace amine-associated receptors (TAAR), V1Rs and V2Rs. A limited number of OR-like sequences have been found in invertebrate chordate genomes. Whether these gene families arose in basal or advanced vertebrates has not been resolved because these families have not been examined systematically in agnathan genomes.     

In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles

Background  

In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task.     

New miRNA labeling method for bead-based quantification

Background  

microRNAs (miRNAs) are small single-stranded non-coding RNAs that act as crucial regulators of gene expression. Different methods have been developed for miRNA expression profiling in order to better understand gene regulation in normal and pathological conditions. miRNAs expression values obtained from large scale methodologies such as microarrays still need a validation step with alternative technologies.     

Identifying novel genes in <Emphasis Type="Italic">C. elegans</Emphasis> using SAGE tags

Background  

Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required.     

STEM: a tool for the analysis of short time series gene expression data

Background  

Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.     

Additive risk survival model with microarray data

Background  

Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements.     

A general co-expression network-based approach to gene expression analysis: comparison and applications

Background  

Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric.     

Accelerated exchange of exon segments in Viperid three-finger toxin genes (<Emphasis Type="Italic">Sistrurus catenatus edwardsii</Emphasis>; Desert Massasauga)

Background  

Snake venoms consist primarily of proteins and peptides showing a myriad of potent biological activities which have been shaped by both adaptive and neutral selective forces. Venom proteins are encoded by multigene families that have evolved through a process of gene duplication followed by accelerated evolution in the protein coding region.     

Testing the additional predictive value of high-dimensional molecular data

Background  

While high-dimensional molecular data such as microarray gene expression data have been used for disease outcome prediction or diagnosis purposes for about ten years in biomedical research, the question of the additional predictive value of such data given that classical predictors are already available has long been under-considered in the bioinformatics literature.