Biomedical subjects

Tijl De Bie

Publications and source records attributed to Tijl De Bie.

6 recordsLinked to original sources

PubMed2006

The evolution of mammalian gene families.

Gene families are groups of homologous genes that are likely to have highly similar functions. Differences in family size due to lineage-specific gene duplication and gene loss may provide clues to the evolutionary forces that have shaped mammalian genomes. Here we analyze the gene families contained within the whole genomes of human, chimpanzee, mouse, rat, and dog. In total we find that more than half of the 9,990 families present in the mammalian common ancestor have either expanded or contracted along at least one lineage. Additionally, we find that a large number of families are completely lost from one or more mammalian genomes, and a similar number of gene families have arisen subsequent to the mammalian common ancestor. Along the lineage leading to modern humans we infer the gain of 689 genes and the loss of 86 genes since the split from chimpanzees, including changes likely driven by adaptive natural selection. Our results imply that humans and chimpanzees differ by at least 6% (1,418 of 22,000 genes) in their complement of genes, which stands in stark contrast to the oft-cited 1.5% difference between orthologous nucleotide sequences. This genomic "revolving door" of gene gain and loss represents a large number of genetic differences separating humans from our closest relatives.

Animals↗

PubMed2006

Inferring transcriptional modules from ChIP-chip, motif and microarray data.

'ReMoDiscovery' is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles. When compared to published module discovery algorithms, ReMoDiscovery is fast and easily tunable. We evaluated our method on yeast data, where it was shown to generate biologically meaningful findings and allowed the prediction of potential novel roles of transcriptional regulators.

Algorithms↗

PubMed2006

CAFE: a computational tool for the study of gene family evolution.

SUMMARY: We present CAFE (Computational Analysis of gene Family Evolution), a tool for the statistical analysis of the evolution of the size of gene families. It uses a stochastic birth and death process to model the evolution of gene family sizes over a phylogeny. For a specified phylogenetic tree, and given the gene family sizes in the extant species, CAFE can estimate the global birth and death rate of gene families, infer the most likely gene family size at all internal nodes, identify gene families that have accelerated rates of gain and loss (quantified by a p-value) and identify which branches cause the p-value to be small for significant families. AVAILABILITY: Software is available from http://www.bio.indiana.edu/~hahnlab/Software.html

Algorithms↗

PubMed2005

Estimating the tempo and mode of gene family evolution from comparative genomic data.

Comparison of whole genomes has revealed that changes in the size of gene families among organisms is quite common. However, there are as yet no models of gene family evolution that make it possible to estimate ancestral states or to infer upon which lineages gene families have contracted or expanded. In addition, large differences in family size have generally been attributed to the effects of natural selection, without a strong statistical basis for these conclusions. Here we use a model of stochastic birth and death for gene family evolution and show that it can be efficiently applied to multispecies genome comparisons. This model takes into account the lengths of branches on phylogenetic trees, as well as duplication and deletion rates, and hence provides expectations for divergence in gene family size among lineages. The model offers both the opportunity to identify large-scale patterns in genome evolution and the ability to make stronger inferences regarding the role of natural selection in gene family expansion or contraction. We apply our method to data from the genomes of five yeast species to show its applicability.

Evolution, Molecular↗

PubMed2005

Discovering transcriptional modules from motif, chip-chip and microarray data.

We present a method for inference of transcriptional modules from heterogeneous data sources. It allows identifying the responsible set of regulators in combination with their corresponding DNA recognition sites (motifs) and target genes. Our approach distinguishes itself from previous work in literature because it fully exploits the knowledge of three independently acquired data sources: ChIP-chip data; motif information as obtained by phylogenetic shadowing; and gene expression profiles obtained using microarray experiments. Moreover, these three data sources are dealt with in a new and fully integrated manner. By avoiding approaches that take the different data sources into account sequentially or iteratively, the transparency of the method and the interpretability of the results are ensured. Using our method on biological data demonstrated the biological relevance of the inference.

Algorithms↗

PubMed2004

A statistical framework for genomic data fusion.

MOTIVATION: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. RESULTS: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. AVAILABILITY: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm

Algorithms↗