Identification and validation of reference genes for quantitative RT-PCR normalization in wheat

Background  

Usually the reference genes used in gene expression analysis have been chosen for their known or suspected housekeeping roles, however the variation observed in most of them hinders their effective use. The assessed lack of validated reference genes emphasizes the importance of a systematic study for their identification. For selecting candidate reference genes we have developed a simple in silico method based on the data publicly available in the wheat databases Unigene and TIGR.     

Joint mapping of genes and conditions via multidimensional unfolding analysis

Background  

Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding.     

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Background  

A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.     

A factor analysis model for functional genomics

Background  

Expression array data are used to predict biological functions of uncharacterized genes by comparing their expression profiles to those of characterized genes. While biologically plausible, this is both statistically and computationally challenging. Typical approaches are computationally expensive and ignore correlations among expression profiles and functional categories.     

In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR

Background  

Housekeeping genes are commonly used as endogenous reference genes for the relative quantification of target genes in gene expression studies. No conclusive systematic study comparing the suitability of different candidate reference genes in clear cell renal cell carcinoma has been published to date. To remedy this situation, 10 housekeeping genes for normalizing purposes of RT-PCR measurements already recommended in various studies were examined with regard to their usefulness as reference genes.     

Microarray analysis of relative gene expression stability for selection of internal reference genes in the rhesus macaque brain

Background  

Normalization of gene expression data refers to the comparison of expression values using reference standards that are consistent across all conditions of an experiment. In PCR studies, genes designated as "housekeeping genes" have been used as internal reference genes under the assumption that their expression is stable and independent of experimental conditions. However, verification of this assumption is rarely performed. Here we assess the use of gene microarray analysis to facilitate selection of internal reference sequences with higher expression stability across experimental conditions than can be expected using traditional selection methods.     

Global expression analysis of nucleotide binding site-leucine rich repeat-encoding and related genes in Arabidopsis

Background  

Nucleotide binding site-leucine rich repeat (NBS-LRR)-encoding genes comprise the largest class of plant disease resistance genes. The 149 NBS-LRR-encoding genes and the 58 related genes that do not encode LRRs represent approximately 0.8% of all ORFs so far annotated in Arabidopsis ecotype Col-0. Despite their prevalence in the genome and functional importance, there was little information regarding expression of these genes.     

Selecting normalization genes for small diagnostic microarrays

Background  

Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems.     

Efficient generation of double heterologous promoter controlled oncolytic adenovirus vectors by a single homologous recombination step in Escherichia coli

Background  

Oncolytic adenoviruses are promising agents for the multimodal treatment of cancer. However, tumor-selectivity is crucial for their applicability in patients. Recent studies by several groups demonstrated that oncolytic adenoviruses with tumor-/tissue-specific expression of the E1 and E4 genes, which are pivotal for adenoviral replication, have a specificity profile that is superior to viruses that solely target the expression of E1 or E4 genes. Presently the E1 and E4 regions are modified in a time consuming sequential fashion.     

Gene regulatory networks modelling using a dynamic evolutionary hybrid

Background  

Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.