Putting the ‘bio’ into bioinformatics

Bioinformatic analyses have grown rapidly in sophistication and efficiency to accommodate the vast increase in available data. One of the major challenges has been to incorporate the growing appreciation of the complexity of molecular evolution into new analytical methods. As the reliance on molecular data in biology and medicine increases, we need to be confident that these methods adequately reflect the underlying processes of genome change. This special issue focuses on the way that patterns and processes of molecular evolution are influenced by features of populations of whole organisms, such as selection pressure, population size and life history. The advantage of this approach to molecular evolution is that it views genomic change not simply as a biochemical or stochastic process, but as the result of a complex series of interactions that shape the kinds of genomic changes that can and do happen.     

Improving DNA array data quality by minimising ‘neighbourhood’ effects

Gene expression studies using cDNA arrays require robust and sensitive detection methods. Being extremely sensitive, radioactive detection suffers from the influence of signals positioned in each other’s vicinity, the ‘neighbourhood’ effect. This limits the gene density of arrays and the quality of the results obtained. We have investigated the quantitative influence of different parameters on the ‘neighbourhood’ effect. By using a model experimental system, we could show that the effect is linear and depends only on the intensity of the hybridisation signal. We identified a common factor that can describe the influence of the neighbour spots based on their intensities. This factor is <1%, but it has to be taken into account if a high dynamic range of gene expression is to be detected. We could also derive the factor, although with less precision, from comparison of duplicate spots on arrays of 4565 different clones and replication of the hybridisation experiments. The calculated coefficient applied to our actual experimental results not only revealed previously undetected tissue or cell-specific expression differences, but also increased the dynamic range of detection. It thus provides a relatively simple way of improving DNA array data quality with few experimental modifications.     

Targeted ‘knockdown’ of spliceosome function in mammalian cells

The existence of two sophisticated parallel splicing machineries in multicellular organisms has raised intriguing questions—ranging from their impact on proteome expansion to the evolution of splicing and of metazoan genomes. Exploring roles for the distinct splicing systems in vivo has, however, been restricted by the lack of techniques to selectively inhibit their function in cells. In this study, we show that morpholino oligomers complementary to the branch-site recognition elements of U2 or U12 small nuclear RNA specifically suppress the function of the two splicing systems in mammalian cells. The data provide the first evidence for a role of distinct spliceosomes in pre-mRNA splicing from endogenous mammalian genes and establish a tool to define roles for the different splicing machineries in vivo.     

Proximity ligation assays with peptide conjugate ‘burrs’ for the sensitive detection of spores

The proximity ligation assay (PLA) has previously been used for the sensitive and specific detection of single proteins. In order to adapt PLA methods for the detection of cell surfaces, we have generated multivalent peptide–oligonucleotide–phycoerythrin conjugates (‘burrs’) that can bind adjacent to one another on a cell surface and be ligated together to form unique amplicons. Real-time PCR detection of burr ligation events specifically identified as few as 100 Bacillus anthracis, 10 Bacillus subtilis and 1 Bacillus cereus spore. Burrs should prove to be generally useful for detecting and mapping interactions and distances between cell surface proteins.