Ilkka Havukkala

We present a high-throughput method for analyzing large-scale bitmapped bio-data: processing of elongated molecular structures by 2D images and analyzing their shapes for chemoinformatics databases. Two-dimensional structure databases are transfered to bitmap images — a commonly used visualization widely spread online. Then, an efficient clustering of the molecular structures is achieved by a mutual symmetry-based binary matrix representation of the shapes. We present a method to compute the difference between two of such representations and evaluate its performance with respect to time and quality of matching. In our tests we use two bitmap databases, one containing true human microRNA folded 2D structures and one with claimed human microRNA folded 2D structures. We show the stability of the matching with respect to parameterization and orientation of the shapes. Our method enables a good automatic clustering of structures with high visual similarity.

‘Candidatus Phytoplasma australiense’ is associated with at least nine diseases in Australia and New Zealand. The impact of this phytoplasma is considerable, both economically and environmentally. The genome of a NZ isolate was sequenced in an effort to understand its pathogenicity and ecology. Comparison with a closely related Australian isolate enabled us to examine mechanisms of genomic rearrangement.

It is a challenging task to predict with high reliability whether plant genomic sequences contain a polyadenylation (polyA) site or not. In this paper, we solve the task by means of a systematic machine-learning procedure applied on a dataset of 1000 Arabidopsis thaliana sequences flanking polyA sites. Our procedure consists of three steps. In the first step, we extract informative features from the sequences using the highly informative k-mer windows approach. Experiments with five classifiers show that the best performance is approximately 83%. In the second step, we improve performance to 95% by reducing the number of features using linear discriminant analysis, followed by applying the linear discriminant classifier. In the third step, we apply the transductive confidence machines approach and the receiver operating characteristic isometrics approach. The resulting two classifiers enable presetting any desired performance by dealing carefully with sequences for which it is unclear whether they contain polyA sites or not. For example, in our case study, we obtain 99% performance by leaving 26% of the sequences unclassified, and 100% performance by leaving 40% of the sequences unclassified. This is clearly useful for experimental verification of putative polyA sites in the laboratory. The novel methods in our machine-learning procedure should find applications in several areas of bioinformatics.