Molecular profiling of platinum resistant ovarian cancer: use of the model in clinical practice.
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Antineoplastic Agents↗
Biomedical subjects
Bart De Moor
Publications and source records attributed to Bart De Moor.
Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.
MOTIVATION: Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. RESULTS: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices.
Bayes Theorem↗
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↗
More robust detection of motifs in coexpressed genes by using phylogenetic information.
BACKGROUND: Several motif detection algorithms have been developed to discover overrepresented motifs in sets of coexpressed genes. However, in a noisy gene list, the number of genes containing the motif versus the number lacking the motif might not be sufficiently high to allow detection by classical motif detection tools. To still recover motifs which are not significantly enriched but still present, we developed a procedure in which we use phylogenetic footprinting to first delineate all potential motifs in each gene. Then we mutually compare all detected motifs and identify the ones that are shared by at least a few genes in the data set as potential candidates. RESULTS: We applied our methodology to a compiled test data set containing known regulatory motifs and to two biological data sets derived from genome wide expression studies. By executing four consecutive steps of 1) identifying conserved regions in orthologous intergenic regions, 2) aligning these conserved regions, 3) clustering the conserved regions containing similar regulatory regions followed by extraction of the regulatory motifs and 4) screening the input intergenic sequences with detected regulatory motif models, our methodology proves to be a powerful tool for detecting regulatory motifs when a low signal to noise ratio is present in the input data set. Comparing our results with two other motif detection algorithms points out the robustness of our algorithm. CONCLUSION: We developed an approach that can reliably identify multiple regulatory motifs lacking a high degree of overrepresentation in a set of coexpressed genes (motifs belonging to sparsely connected hubs in the regulatory network) by exploiting the advantages of using both coexpression and phylogenetic information.
Algorithms↗
A calibration method for estimating absolute expression levels from microarray data.
MOTIVATION: We describe an approach to normalize spotted microarray data, based on a physically motivated calibration model. This model consists of two major components, describing the hybridization of target transcripts to their corresponding probes on the one hand, and the measurement of fluorescence from the hybridized, labeled target on the other hand. The model parameters and error distributions are estimated from external control spikes. RESULTS: Using a publicly available dataset, we show that our procedure is capable of adequately removing the typical non-linearities of the data, without making any assumptions on the distribution of differences in gene expression from one biological sample to the next. Since our model links target concentration to measured intensity, we show how absolute expression values of target transcripts in the hybridization solution can be estimated up to a certain degree.
Algorithms↗
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.
BACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. RESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. CONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.
Algorithms↗
Gene prioritization through genomic data fusion.
The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.
Algorithms↗
Characterization of the condensin component Cnap1 and protein kinase Melk as novel E2F target genes down-regulated by 1,25-dihydroxyvitamin D3.
1,25-Dihydroxyvitamin D(3) (1,25(OH)(2)D(3)) has potent antiproliferative effects characterized by a hampered G(1)/S transition. cDNA microarrays were used to monitor expression of 21,492 genes in MC3T3-E1 mouse osteoblasts at 1, 6, 12, 24, and 36 h after treatment with 1,25(OH)(2)D(3). Statistical analysis revealed a cluster of genes that were strongly down-regulated by 1,25(OH)(2)D(3) and which not only function in cell cycle regulation and DNA replication but also mediate checkpoint control, DNA repair, chromosome modifications, and mitosis. Because many of these genes were shown earlier to be regulated by the transcriptional repressor E2F4, the intergenic regions of these 1,25(OH)(2)D(3)-down-regulated genes were searched for the presence of E2F binding sites. This led to the characterization of two novel E2F target genes, chromosome condensation-related SMC-associated protein 1 (Cnap1) and maternal embryonic leucine zipper kinase (Melk). Transfection studies and site-directed mutagenesis confirmed Cnap1 and Melk to be bona fide E2F targets. Repression of Cnap1 and Melk by 1,25(OH)(2)D(3) was confirmed not only in MC3T3-E1 cells but also in several other bone-unrelated cell types. This down-regulation as well as the antiproliferative effect of 1,25(OH)(2)D(3) depended on the pocket proteins p107 and p130 because 1,25(OH)(2)D(3) failed to repress these E2F target genes and lost its antiproliferative action in p107(-/-);p130(-/-) cells but not in pRb(-/-) cells.
Animals↗
BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.
biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.
Algorithms↗
TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis.
We present the second and improved release of the TOUCAN workbench for cis-regulatory sequence analysis. TOUCAN implements and integrates fast state-of-the-art methods and strategies in gene regulation bioinformatics, including algorithms for comparative genomics and for the detection of cis-regulatory modules. This second release of TOUCAN has become open source and thereby carries the potential to evolve rapidly. The main goal of TOUCAN is to allow a user to come to testable hypotheses regarding the regulation of a gene or of a set of co-regulated genes. TOUCAN can be launched from this location: http://www.esat.kuleuven.ac.be/~saerts/software/toucan.php.
Algorithms↗
arrayCGHbase: an analysis platform for comparative genomic hybridization microarrays.
BACKGROUND: The availability of the human genome sequence as well as the large number of physically accessible oligonucleotides, cDNA, and BAC clones across the entire genome has triggered and accelerated the use of several platforms for analysis of DNA copy number changes, amongst others microarray comparative genomic hybridization (arrayCGH). One of the challenges inherent to this new technology is the management and analysis of large numbers of data points generated in each individual experiment. RESULTS: We have developed arrayCGHbase, a comprehensive analysis platform for arrayCGH experiments consisting of a MIAME (Minimal Information About a Microarray Experiment) supportive database using MySQL underlying a data mining web tool, to store, analyze, interpret, compare, and visualize arrayCGH results in a uniform and user-friendly format. Following its flexible design, arrayCGHbase is compatible with all existing and forthcoming arrayCGH platforms. Data can be exported in a multitude of formats, including BED files to map copy number information on the genome using the Ensembl or UCSC genome browser. CONCLUSION: ArrayCGHbase is a web based and platform independent arrayCGH data analysis tool, that allows users to access the analysis suite through the internet or a local intranet after installation on a private server. ArrayCGHbase is available at http://medgen.ugent.be/arrayCGHbase/.
Base Sequence↗
Comparison of the PhoPQ regulon in Escherichia coli and Salmonella typhimurium.
The PhoPQ two-component system acts as a transcriptional regulator that responds to Mg(2+) starvation both in Escherichia coli and Salmonella typhimurium (Garcia et al. 1996; Kato et al. 1999). By monitoring the availability of extracellular Mg(2+), this two-component system allows S. typhimurium to sense the transition from an extracellular environment to a subcellular location. Concomitantly with this transition, a set of virulence factors essential for survival in the intracellular environment is activated by the PhoPQ system (Groisman et al. 1989; Miller et al. 1989). Compared to nonpathogenic strains, such as E. coli K12, the PhoPQ regulon in pathogens must contain target genes specifically contributing to the virulence phenotype. To verify this hypothesis, we compared the composition of the PhoPQ regulon between E. coli and S. typhimurium using a combination of expression experiments and motif data. PhoPQ-dependent genes in both organisms were identified from PhoPQ-related microarray experiments. To distinguish between direct and indirect targets, we searched for the presence of the regulatory motif in the promoter region of the identified PhoPQ-dependent genes. This allowed us to reconstruct the direct PhoPQ-dependent regulons in E. coli K12 and S. typhimurium LT2. Comparison of both regulons revealed a very limited overlap of PhoPQ-dependent genes between both organisms. These results suggest that the PhoPQ system has acquired a specialized function during evolution in each of these closely related species that allows adaptation to the specificities of their lifestyles (e.g., pathogenesis in S. typhimurium).
Bacterial Proteins↗
Assessing computational tools for the discovery of transcription factor binding sites.
The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.
Amino Acid Motifs↗
PKA and Sch9 control a molecular switch important for the proper adaptation to nutrient availability.
In the yeast Saccharomyces cerevisiae, PKA and Sch9 exert similar physiological roles in response to nutrient availability. However, their functional redundancy complicates to distinguish properly the target genes for both kinases. In this article, we analysed different phenotypic read-outs. The data unequivocally showed that both kinases act through separate signalling cascades. In addition, genome-wide expression analysis under conditions and with strains in which either PKA and/or Sch9 signalling was specifically affected, demonstrated that both kinases synergistically or oppositely regulate given gene targets. Unlike PKA, which negatively regulates stress-responsive element (STRE)- and post-diauxic shift (PDS)-driven gene expression, Sch9 appears to exert additional positive control on the Rim15-effector Gis1 to regulate PDS-driven gene expression. The data presented are consistent with a cyclic AMP (cAMP)-gating phenomenon recognized in higher eukaryotes consisting of a main gatekeeper, the protein kinase PKA, switching on or off the activities and signals transmitted through primary pathways such as, in case of yeast, the Sch9-controlled signalling route. This mechanism allows fine-tuning various nutritional responses in yeast cells, allowing them to adapt metabolism and growth appropriately.
Adaptation, Physiological↗
Molecular karyotyping: array CGH quality criteria for constitutional genetic diagnosis.
Array CGH (comparative genomic hybridization) enables the identification of chromosomal copy number changes. The availability of clone sets covering the human genome opens the possibility for the widespread use of array CGH for both research and diagnostic purposes. In this manuscript we report on the parameters that were critical for successful implementation of the technology, assess quality criteria, and discuss the potential benefits and pitfalls of the technology for improved pre- and postnatal constitutional genetic diagnosis. We propose to name the genome-wide array CGH "molecular karyotyping," in analogy with conventional karyotyping that uses staining methods to visualize chromosomes.
Cell Line↗
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↗
Importing MAGE-ML format microarray data into BioConductor.
UNLABELLED: The microarray gene expression markup language (MAGE-ML) is a widely used XML (eXtensible Markup Language) standard for describing and exchanging information about microarray experiments. It can describe microarray designs, microarray experiment designs, gene expression data and data analysis results. We describe RMAGEML, a new Bioconductor package that provides a link between cDNA microarray data stored in MAGE-ML format and the Bioconductor framework for preprocessing, visualization and analysis of microarray experiments. AVAILABILITY: http://www.bioconductor.org. Open Source.
Database Management Systems↗
Comprehensive analysis of the base composition around the transcription start site in Metazoa.
BACKGROUND: The transcription start site of a metazoan gene remains poorly understood, mostly because there is no clear signal present in all genes. Now that several sequenced metazoan genomes have been annotated, we have been able to compare the base composition around the transcription start site for all annotated genes across multiple genomes. RESULTS: The most prominent feature in the base compositions is a significant local variation in G+C content over a large region around the transcription start site. The change is present in all animal phyla but the extent of variation is different between distinct classes of vertebrates, and the shape of the variation is completely different between vertebrates and arthropods. Furthermore, the height of the variation correlates with CpG frequencies in vertebrates but not in invertebrates and it also correlates with gene expression, especially in mammals. We also detect GC and AT skews in all clades (where %G is not equal to %C or %A is not equal to %T respectively) but these occur in a more confined region around the transcription start site and in the coding region. CONCLUSIONS: The dramatic changes in nucleotide composition in humans are a consequence of CpG nucleotide frequencies and of gene expression, the changes in Fugu could point to primordial CpG islands, and the changes in the fly are of a totally different kind and unrelated to dinucleotide frequencies.
AT Rich Sequence↗