Biomedical subjects

Yvan Saeys

Publications and source records attributed to Yvan Saeys.

6 recordsLinked to original sources

PubMed2004

SpliceMachine: predicting splice sites from high-dimensional local context representations.

MOTIVATION: In this age of complete genome sequencing, finding the location and structure of genes is crucial for further molecular research. The accurate prediction of intron boundaries largely facilitates the correct prediction of gene structure in nuclear genomes. Many tools for localizing these boundaries on DNA sequences have been developed and are available to researchers through the internet. Nevertheless, these tools still make many false positive predictions. RESULTS: This manuscript presents a novel publicly available splice site prediction tool named SpliceMachine that (i) shows state-of-the-art prediction performance on Arabidopsis thaliana and human sequences, (ii) performs a computationally fast annotation and (iii) can be trained by the user on its own data. AVAILABILITY: Results, figures and software are available at http://www.bioinformatics.psb.ugent.be/supplementary_data/ CONTACT: sven.degroeve@psb.ugent.be; yves.vandepeer@psb.ugent.be.

Algorithms↗

PubMed2004

Feature selection for splice site prediction: a new method using EDA-based feature ranking.

BACKGROUND: The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. RESULTS: In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. CONCLUSION: We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do) this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

Adenosine↗

PubMed2004

Building genomic profiles for uncovering segmental homology in the twilight zone.

The identification of homologous regions within and between genomes is an essential prerequisite for studying genome structure and evolution. Different methods already exist that allow detecting homologous regions in an automated manner. These methods are based either on finding sequence similarities at the DNA level or on identifying chromosomal regions showing conservation of gene order and content. Especially the latter approach has proven useful for detecting homology between highly divergent chromosomal regions. However, until now, such map-based approaches required that candidate homologous regions show significant collinearity with other segments to be considered as being homologous. Here, we present a novel method that creates profiles combining the gene order and content information of multiple mutually homologous genomic segments. These profiles can be used to scan one or more genomes to detect segments that show significant collinearity with the entire profile but not necessarily with individual segments. When applying this new method to the combined genomes of Arabidopsis and rice, we find additional evidence for ancient duplication events in the rice genome.

Algorithms↗

PubMed2003

Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction.

MOTIVATION: Feature subset selection is an important preprocessing step for classification. In biology, where structures or processes are described by a large number of features, the elimination of irrelevant and redundant information in a reasonable amount of time has a number of advantages. It enables the classification system to achieve good or even better solutions with a restricted subset of features, allows for a faster classification, and it helps the human expert focus on a relevant subset of features, hence providing useful biological knowledge. RESULTS: We present a heuristic method based on Estimation of Distribution Algorithms to select relevant subsets of features for splice site prediction in Arabidopsis thaliana. We show that this method performs a fast detection of relevant feature subsets using the technique of constrained feature subsets. Compared to the traditional greedy methods the gain in speed can be up to one order of magnitude, with results being comparable or even better than the greedy methods. This makes it a very practical solution for classification tasks that can be solved using a relatively small amount of discriminative features (or feature dependencies), but where the initial set of potential discriminative features is rather large.

Algorithms↗

PubMed2003

Investigating ancient duplication events in the Arabidopsis genome.

The complete genomic analysis of Arabidopsis thaliana has shown that a major fraction of the genome consists of paralogous genes that probably originated through one or more ancient large-scale gene or genome duplication events. However, the number and timing of these duplications still remains unclear, and several different hypotheses have been put forward recently. Here, we reanalyzed duplicated blocks found in the Arabidopsis genome described previously and determined their date of divergence based on silent substitution estimations between the paralogous genes and, where possible, by phylogenetic reconstruction. We show that methods based on averaging protein distances of heterogeneous classes of duplicated genes lead to unreliable conclusions and that a large fraction of blocks duplicated much more recently than assumed previously. We found clear evidence for one large-scale gene or even complete genome duplication event somewhere between 70 to 90 million years ago. Traces pointing to a much older (probably more than 200 million years) large-scale gene duplication event could be detected. However, for now it is impossible to conclude whether these old duplicates are the result of one or more large-scale gene duplication events.

Arabidopsis↗

PubMed2002

The automatic detection of homologous regions (ADHoRe) and its application to microcolinearity between Arabidopsis and rice.

It is expected that one of the merits of comparative genomics lies in the transfer of structural and functional information from one genome to another. This is based on the observation that, although the number of chromosomal rearrangements that occur in genomes is extensive, different species still exhibit a certain degree of conservation regarding gene content and gene order. It is in this respect that we have developed a new software tool for the Automatic Detection of Homologous Regions (ADHoRe). ADHoRe was primarily developed to find large regions of microcolinearity, taking into account different types of microrearrangements such as tandem duplications, gene loss and translocations, and inversions. Such rearrangements often complicate the detection of colinearity, in particular when comparing more anciently diverged species. Application of ADHoRe to the complete genome of Arabidopsis and a large collection of concatenated rice BACs yields more than 20 regions showing statistically significant microcolinearity between both plant species. These regions comprise from 4 up to 11 conserved homologous gene pairs. We predict the number of homologous regions and the extent of microcolinearity to increase significantly once better annotations of the rice genome become available.

Arabidopsis↗