Biomedical subjects

Rene Te Boekhorst

Publications and source records attributed to Rene Te Boekhorst.

3 recordsLinked to original sources

PubMed2006

Statistical measures of the structure of genomic sequences: entropy, complexity, and position information.

Identifying regions of DNA with extreme statistical characteristics is an important aspect of the structural analysis of complete genomes. Linguistic methods, mainly based on estimating word frequency, can be used for this as they allow for the delineation of regions of low complexity. Low complexity may be due to biased nucleotide composition, by tandem- or dispersed repeats, by palindrome-hairpin structures, as well as by a combination of all these features. We developed software tools in which various numerical measures of text complexity are implemented, including combinatorial and linguistic ones. We also added Hurst exponent estimate to the software to measure dependencies in DNA sequences. By applying these tools to various functional genomic regions, we demonstrate that the complexity of introns and regulatory regions is lower than that of coding regions, whilst Hurst exponent is larger. Further analysis of promoter sequences revealed that the lower complexity of these regions is associated with long-range correlations caused by transcription factor binding sites.

Algorithms↗

PubMed2006

Transcription binding site prediction using Markov models.

One of the main goals of analysing DNA sequences is to understand the temporal and positional information that specifies gene expression. An important step in this process is the recognition of gene expression regulatory elements. Experimental procedures for this are slow and costly. In this paper we present a computational non-supervised algorithm that facilitates the process by statistically identifying the most likely regions within a putative regulatory sequence. A probabilistic technique is presented, based on the approximation of regulatory DNA with a Markov chain, for the location of putative transcription factor binding sites in a single stretch of DNA. Hereto we developed a procedure to approximate the order of Markov model for a given DNA sequence that circumvents some of the prohibitive assumptions underlying Markov modeling. Application of the algorithm to data from 55 genes in five species shows the high sensitivity of this Markov search algorithm. Our algorithm does not require any prior knowledge in the form of description or cross-genomic comparison; it is context sensitive and takes DNA heterogeneity into account.

Artificial Intelligence↗

PubMed2006

Improving computational predictions of cis-regulatory binding sites.

The location of cis-regulatory binding sites determine the connectivity of genetic regulatory networks and therefore constitute a natural focal point for research into the many biological systems controlled by such regulatory networks. Accurate computational prediction of these binding sites would facilitate research into a multitude of key areas, including embryonic development, evolution, pharmacogenemics, cancer and many other transcriptional diseases, and is likely to be an important precursor for the reverse engineering of genome wide, genetic regulatory networks. Many algorithmic strategies have been developed for the computational prediction of cis-regulatory binding sites but currently all approaches are prone to high rates of false positive predictions, and many are highly dependent on additional information, limiting their usefulness as research tools. In this paper we present an approach for improving the accuracy of a selection of established prediction algorithms. Firstly, it is shown that species specific optimization of algorithmic parameters can, in some cases, significantly improve the accuracy of algorithmic predictions. Secondly, it is demonstrated that the use of non-linear classification algorithms to integrate predictions from multiple sources can result in more accurate predictions. Finally, it is shown that further improvements in prediction accuracy can be gained with the use of biologically inspired post-processing of predictions.

Algorithms↗