Statistical Methods in Medical Research current issue

Editorial: Special Issue on Statistical Bioinformatics

Gilks, W. R. — Fri, 25 Sep 2009 02:58:04 PDT

Considerations for the processing and analysis of GoldenGate-based two-colour Illumina platforms

Lynch, A., Dunning, M., Iddawela, M., Barbosa-Morais, N., Ritchie, M. — Fri, 25 Sep 2009 02:58:04 PDT

Illumina’s GoldenGate technology is a two-channel microarray platform that allows for the simultaneous interrogation of about 1 500 locations in the genome. GoldenGate has proved a flexible platform not only in the choice of those 1 500 locations, but also in the choice of the property being measured at them. It retains the desirable properties of Illumina’s BeadArrays in that the probes (in this case ‘beads’) are randomly arranged across the microarray, there are multiple instances of each probe and many samples can be processed simultaneously. As for other Illumina technologies, however, these properties are not exploited as they might be. Here we review the various common adaptations of the GoldenGate platform, review the analysis methods that are associated with each adaptation and then, with the aid of a number of example data sets we illustrate some of the improvements that can be made over the default analysis.

Stochastic models of sequence evolution including insertion--deletion events

Miklos, I., Novak, A., Satija, R., Lyngso, R., Hein, J. — Fri, 25 Sep 2009 02:58:04 PDT

Comparison of sequences that have descended from a common ancestor based on an explicit stochastic model of substitutions, insertions and deletions has risen to prominence in the last decade. Making statements about the positions of insertions-deletions (abbr. indels) is central in sequence and genome analysis and is called alignment. This statistical approach is harder conceptually and computationally, than competing approaches based on choosing an alignment according to some optimality criteria. But it has major practical advantages in terms of testing evolutionary hypotheses and parameter estimation. Basic dynamic approaches can allow the analysis of up to 4—5 sequences. MCMC techniques can bring this to about 10—15 sequences. Beyond this, different or heuristic approaches must be used. Besides the computational challenges, increasing realism in the underlying models is presently being addressed. A recent development that has been especially fruitful is combining statistical alignment with the problem of sequence annotation, making statements about the function of each nucleotide/amino acid. So far gene finding, protein secondary structure prediction and regulatory signal detection has been tackled within this framework. Much progress can be reported, but clearly major challenges remain if this approach is to be central in the analyses of large incoming sequence data sets.

Modelling the evolution of multi-gene families

Nye, T. M. — Fri, 25 Sep 2009 02:58:04 PDT

A number of biological processes can lead to genes being copied within the genome of some given species. Duplicate genes of this form are called paralogs and such genes share a high degree sequence similarity as well as often having closely related functions. Some genes have become widely duplicated to form multigene families in which the copies are distributed both within the genomes of individual species and across different species. Statistical modelling of gene duplication and the evolution of multi-gene families currently lags behind well-established models of DNA sequence evolution despite an increasing volume of available data, but the analysis of multi-gene families is important as part of a wider effort to understand evolution at the genomic level. This article reviews existing approaches to modelling multi-gene families and presents various challenges and possibilities for this exciting area of research.

Probabilistic models and machine learning in structural bioinformatics

Hamelryck, T. — Fri, 25 Sep 2009 02:58:04 PDT

Structural bioinformatics is concerned with the molecular structure of biomacromolecules on a genomic scale, using computational methods. Classic problems in structural bioinformatics include the prediction of protein and RNA structure from sequence, the design of artificial proteins or enzymes, and the automated analysis and comparison of biomacromolecules in atomic detail. The determination of macromolecular structure from experimental data (for example coming from nuclear magnetic resonance, X-ray crystallography or small angle X-ray scattering) has close ties with the field of structural bioinformatics. Recently, probabilistic models and machine learning methods based on Bayesian principles are providing efficient and rigorous solutions to challenging problems that were long regarded as intractable. In this review, I will highlight some important recent developments in the prediction, analysis and experimental determination of macromolecular structure that are based on such methods. These developments include generative models of protein structure, the estimation of the parameters of energy functions that are used in structure prediction, the superposition of macromolecules and structure determination methods that are based on inference. Although this review is not exhaustive, I believe the selected topics give a good impression of the exciting new, probabilistic road the field of structural bioinformatics is taking.