Validation and functional annotation of expression-based clusters based on gene ontology

Background  

The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group.     

Incremental genetic K-means algorithm and its application in gene expression data analysis

Background  

In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data.     

Mining gene expression data by interpreting principal components

Background  

There are many methods for analyzing microarray data that group together genes having similar patterns of expression over all conditions tested. However, in many instances the biologically important goal is to identify relatively small sets of genes that share coherent expression across only some conditions, rather than all or most conditions as required in traditional clustering; e.g. genes that are highly up-regulated and/or down-regulated similarly across only a subset of conditions. Equally important is the need to learn which conditions are the decisive ones in forming such gene sets of interest, and how they relate to diverse conditional covariates, such as disease diagnosis or prognosis.     

Feature selection environment for genomic applications

Background  

Feature selection is a pattern recognition approach to choose important variables according to some criteria in order to distinguish or explain certain phenomena (i.e., for dimensionality reduction). There are many genomic and proteomic applications that rely on feature selection to answer questions such as selecting signature genes which are informative about some biological state, e.g., normal tissues and several types of cancer; or inferring a prediction network among elements such as genes, proteins and external stimuli. In these applications, a recurrent problem is the lack of samples to perform an adequate estimate of the joint probabilities between element states. A myriad of feature selection algorithms and criterion functions have been proposed, although it is difficult to point the best solution for each application.     

A statistical framework for differential network analysis from microarray data

Background  

It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types.     

A new regularized least squares support vector regression for gene selection

Background  

Selection of influential genes with microarray data often faces the difficulties of a large number of genes and a relatively small group of subjects. In addition to the curse of dimensionality, many gene selection methods weight the contribution from each individual subject equally. This equal-contribution assumption cannot account for the possible dependence among subjects who associate similarly to the disease, and may restrict the selection of influential genes.     

Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments

Background  

Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map.     

Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation

Background  

High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpretation of these experiments problematic. Here, we propose and evaluate an approach to find functional associations between large numbers of genes and other biomedical concepts from free-text literature. For each gene, a profile of related concepts is constructed that summarizes the context in which the gene is mentioned in literature. We assign a weight to each concept in the profile based on a likelihood ratio measure. Gene concept profiles can then be clustered to find related genes and other concepts.     

Efficient four fragment cloning for the construction of vectors for targeted gene replacement in filamentous fungi

Background  

The rapid increase in whole genome fungal sequence information allows large scale functional analyses of target genes. Efficient transformation methods to obtain site-directed gene replacement, targeted over-expression by promoter replacement, in-frame epitope tagging or fusion of coding sequences with fluorescent markers such as GFP are essential for this process. Construction of vectors for these experiments depends on the directional cloning of two homologous recombination sequences on each side of a selection marker gene.     

RSCA genotyping of MHC for high-throughput evolutionary studies in the model organism three-spined stickleback Gasterosteus aculeatus

Background  

In all jawed vertebrates, highly polymorphic genes of the major histocompatibility complex (MHC) encode antigen presenting molecules that play a key role in the adaptive immune response. Their polymorphism is composed of multiple copies of recently duplicated genes, each possessing many alleles within populations, as well as high nucleotide divergence between alleles of the same species. Experimental evidence is accumulating that MHC polymorphism is a result of balancing selection by parasites and pathogens. In order to describe MHC diversity and analyse the underlying mechanisms that maintain it, a reliable genotyping technique is required that is suitable for such highly variable genes.     

Functional dissection of the <Emphasis Type="Italic">ash2</Emphasis> and <Emphasis Type="Italic">ash1</Emphasis> transcriptomes provides insights into the transcriptional basis of wing phenotypes and reveals conserved protein interactions

Background  

The trithorax group (trxG) genes absent, small or homeotic discs 1 (ash1) and 2 (ash2) were isolated in a screen for mutants with abnormal imaginal discs. Mutations in either gene cause homeotic transformations but Hox genes are not their only targets. Although analysis of double mutants revealed that ash2 and ash1 mutations enhance each other's phenotypes, suggesting they are functionally related, it was shown that these proteins are subunits of distinct complexes.     

Age-specific effects of estrogen receptors' polymorphisms on the bone traits in healthy fertile women: the BONTURNO study

Background  

Skeletal characteristics such as height (Ht), bone mineral density (BMD) or bone turnover markers are strongly inherited. Common variants in the genes encoding for estrogen receptor alpha (ESR1) and beta (ESR2) are proposed as candidates for influencing bone phenotypes at the population level.     

Rate variation in parasitic plants: correlated and uncorrelated patterns among plastid genes of different function

Background  

The analysis of synonymous and nonsynonymous rates of DNA change can help in the choice among competing explanations for rate variation, such as differences in constraint, mutation rate, or the strength of genetic drift. Nonphotosynthetic plants of the Orobanchaceae have increased rates of DNA change. In this study 38 taxa of Orobanchaceae and relatives were used and 3 plastid genes were sequenced for each taxon.     

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

Background  

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.     

Modelling the correlation between the activities of adjacent genes in drosophila

Background  

Correlation between the expression levels of genes which are located close to each other on the genome has been found in various organisms, including yeast, drosophila and humans. Since such a correlation could be explained by several biochemical, evolutionary, genetic and technological factors, there is a need for statistical models that correspond to specific biological models for the correlation structure.     

Effect of data normalization on fuzzy clustering of DNA microarray data

Background  

Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap.     

Retrofitting BACs with G418 resistance, luciferase, and oriP and EBNA-1 – new vectors for in vitro and in vivo delivery

Background  

Bacterial artificial chromosomes (BACs) have been used extensively for sequencing the human and mouse genomes and are thus readily available for most genes. The large size of BACs means that they can generally carry intact genes with all the long range controlling elements that drive full levels of tissue-specific expression. For gene expression studies and gene therapy applications it is useful to be able to retrofit the BACs with selectable genes such as G418 resistance, reporter genes such as luciferase, and oriP/EBNA-1 from Epstein Barr virus which allows long term episomal maintenance in mammalian cells.     

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes,prepared without passaging through E. coli

Background  

Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library.