Knowledge-based data analysis and interpretation.
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Artificial Intelligence↗
Biomedical subjects
Blaz Zupan
Publications and source records attributed to Blaz Zupan.
Developmentally regulated DNA methylation in Dictyostelium discoideum.
Methylation of cytosine residues in DNA plays a critical role in the silencing of gene expression, organization of chromatin structure, and cellular differentiation of eukaryotes. Previous studies failed to detect 5-methylcytosine in Dictyostelium genomic DNA, but the recent sequencing of the Dictyostelium genome revealed a candidate DNA methyltransferase gene (dnmA). The genome sequence also uncovered an unusual distribution of potential methylation sites, CpG islands, throughout the genome. DnmA belongs to the Dnmt2 subfamily and contains all the catalytic motifs necessary for cytosine methyltransferases. Dnmt2 activity is typically weak in Drosophila melanogaster, mouse, and human cells and the gene function in these systems is unknown. We have investigated the methylation status of Dictyostelium genomic DNA with antibodies raised against 5-methylcytosine and detected low levels of the modified nucleotide. We also found that DNA methylation increased during development. We searched the genome for potential methylation sites and found them in retrotransposable elements and in several other genes. Using Southern blot analysis with methylation-sensitive and -insensitive restriction endonucleases, we found that the DIRS retrotransposon and the guaB gene were indeed methylated. We then mutated the dnmA gene and found that DNA methylation was reduced to about 50% of the wild-type level. The mutant cells exhibited morphological defects in late development, indicating that DNA methylation has a regulatory role in Dictyostelium development. Our findings establish a role for a Dnmt2 methyltransferase in eukaryotic development.
Animals↗
GenePath: from mutations to genetic networks and back.
GenePath is a web-based application for the analysis of mutant-based experiments and synthesis of genetic networks. Here, we introduce GenePath and describe a number of new approaches, including conflict resolution, handling cyclic pathways, confidence level assignment, what-if analysis and new experiment proposal. We illustrate the key concepts using data from a study of adhesion genes in Dictyostelium discoideum and show that GenePath discovered genetic interactions that were ignored in the original publication. GenePath is available at http://www.genepath.org/genepath2.
Animals↗
Epistasis analysis with global transcriptional phenotypes.
Classical epistasis analysis can determine the order of function of genes in pathways using morphological, biochemical and other phenotypes. It requires knowledge of the pathway's phenotypic output and a variety of experimental expertise and so is unsuitable for genome-scale analysis. Here we used microarray profiles of mutants as phenotypes for epistasis analysis. Considering genes that regulate activity of protein kinase A in Dictyostelium, we identified known and unknown epistatic relationships and reconstructed a genetic network with microarray phenotypes alone. This work shows that microarray data can provide a uniform, quantitative tool for large-scale genetic network analysis.
Animals↗
TA-clustering: cluster analysis of gene expression profiles through Temporal Abstractions.
This paper describes a new technique for clustering short time series of gene expression data. The technique is a generalization of the template-based clustering and is based on a qualitative representation of profiles which are labelled using trend Temporal Abstractions (TAs); clusters are then dynamically identified on the basis of this qualitative representation. Clustering is performed in an efficient way at three different levels of aggregation of qualitative labels, each level corresponding to a distinct degree of qualitative representation. The developed TA-clustering algorithm provides an innovative way to cluster gene profiles. We show the developed method to be robust, efficient and to perform better than the standard hierarchical agglomerative clustering approach when dealing with temporal dislocations of time series. Results of the TA-clustering algorithm can be visualized as a three-level hierarchical tree of qualitative representations and as such easy to interpret. We demonstrate the utility of the proposed algorithm on a set of two simulated data sets and on a study of gene expression data from S. cerevisiae.
Algorithms↗
VizRank: finding informative data projections in functional genomics by machine learning.
UNLABELLED: VizRank is a tool that finds interesting two-dimensional projections of class-labeled data. When applied to multi-dimensional functional genomics datasets, VizRank can systematically find relevant biological patterns. AVAILABILITY: http://www.ailab.si/supp/bi-vizrank SUPPLEMENTARY INFORMATION: http://www.ailab.si/supp/bi-vizrank.
Algorithms↗
Microarray data mining with visual programming.
UNLABELLED: Visual programming offers an intuitive means of combining known analysis and visualization methods into powerful applications. The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analyses by combining common data analysis tools to fit their needs. AVAILABILITY: http://www.ailab.si/supp/bi-visprog SUPPLEMENTARY INFORMATION: http://www.ailab.si/supp/bi-visprog.
Chromosome Mapping↗
GenePath: a system for automated construction of genetic networks from mutant data.
MOTIVATION: Genetic networks are often used in the analysis of biological phenomena. In classical genetics, they are constructed manually from experimental data on mutants. The field lacks formalism to guide such analysis, and accounting for all the data becomes complicated when large amounts of data are considered. RESULTS: We have developed GenePath, an intelligent assistant that automates the analysis of genetic data. GenePath employs expert-defined patterns to uncover gene relations from the data, and uses these relations as constraints in the search for a plausible genetic network. GenePath formalizes genetic data analysis, facilitates the consideration of all the available data in a consistent manner, and the examination of the large number of possible consequences of planned experiments. It also provides an explanation mechanism that traces every finding to the pertinent data. AVAILABILITY: GenePath can be accessed at http://genepath.org. SUPPLEMENTARY INFORMATION: Supplementary material is available at http://genepath.org/bi-.supp.
Animals↗
GenePath: a system for inference of genetic networks and proposal of genetic experiments.
A genetic network is a formalism that is often used in biology to represent causalities and reason about biological phenomena related to genetic regulation. We present GenePath, a computer-based system that supports the inference of genetic networks from a set of genetic experiments. Implemented in Prolog, GenePath uses abductive inference to elucidate network constraints based on background knowledge and experimental results. Additionally, it can propose genetic experiments that may further refine the discovered network and establish relations between genes that could not be related based on the original experimental data. We illustrate GenePath's approach and utility on analysis of data on aggregation and sporulation of the soil amoeba Dictyostelium discoideum.
Animals↗