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  • 1
    Language: English
    In: Methods in molecular biology (Clifton, N.J.), 2007, Vol.402, pp.269-86
    Description: Single-nucleotide polymorphism (SNP) genotyping can be carried out by annealing an oligonucleotide primer directly adjacent to the polymorphism and carrying out a single base extension using a polymerase reaction with labeled dideoxynucleotide triphosphates. This can be multiplexed by attaching a unique tag at the 5'-end of each oligonucleotide primer and binding the corresponding antitag to a DNA microarray or microbead. After the polymerase reaction, the tag-antitag system can be used to demultiplex the experiment. However, such an assay requires careful primer and tag design to avoid any crossreactivity among the primers, tags, antitags, and template sequence. A procedure for designing the primers is described in this chapter.
    Keywords: Oligonucleotide Array Sequence Analysis ; Polymerase Chain Reaction ; Polymorphism, Single Nucleotide ; DNA Primers -- Chemistry
    ISSN: 1064-3745
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 2
    Language: English
    In: Clinical cancer research : an official journal of the American Association for Cancer Research, 01 March 2007, Vol.13(5), pp.1459-65
    Description: To assess the feasibility of predicting neuroblastoma outcome using highly parallel quantitative real-time PCR data. We generated expression profiles of 63 neuroblastoma patients, 47 of which were analyzed by both Affymetrix U95A microarrays and highly parallel real-time PCR on microfluidic cards (MFC; Applied Biosystems). Top-ranked genes discriminating patients with event-free survival or relapse according to high-level analysis of Affymetrix chip data, as well as known neuroblastoma marker genes (MYCN and NTRK1/TrkA), were quantified simultaneously by real-time PCR. Analysis of PCR data was accomplished using high-level bioinformatics methods including prediction analysis of microarray, significance analysis of microarray, and Computerized Affected Sibling Pair Analyzer and Reporter. Internal validation of the MFC method proved it highly reproducible. Correlation of MFC and chip expression data varied markedly for some genes. Outcome prediction using prediction analysis of microarray on real-time PCR data resulted in 80% accuracy, which is comparable to results obtained using the Affymetrix platform. Real-time PCR data were useful for risk assessment of relapsing neuroblastoma (P = 0.0006, log-rank test) when Computerized Affected Sibling Pair Analyzer and Reporter analysis was applied. These data suggest that multiplex real-time PCR might be a promising approach to reduce the complexity of information obtained from whole-genome array experiments. It could provide a more convenient and less expensive tool for routine application in a clinical setting.
    Keywords: Microfluidic Analytical Techniques ; Brain Neoplasms -- Genetics ; Gene Expression Profiling -- Methods ; Neuroblastoma -- Genetics
    ISSN: 1078-0432
    E-ISSN: 15573265
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  • 3
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007. Proceedings, pp.77-89
    Description: It has previously been demonstrated that gene expression data correlate with event-free and overall survival in several cancers. A number of methods exist that assign patients to different risk classes based on expression profiles of their tumor. However, predictions of actual survival times in years for the individual patient, together with confidence intervals on the predictions made, would provide a far more detailed view, and could aid the clinician considerably in evaluating different treatment options. Similarly, a method able to make such predictions could be analyzed to infer knowledge about the relevant disease genes, hinting at potential disease pathways and pointing to relevant targets for drug design. Here too, confidences on the relevance values for the individual genes would be useful to have.Our algorithm to tackle these questions builds on a hierarchical Bayesian approach, combining a Cox regression model with a hierarchical prior distribution on the regression parameters for feature selection. This prior enables the method to efficiently deal with the low sample number, high dimensionality setting characteristic of microarray datasets. We then sample from the posterior distribution over a patients survival time, given gene expression measurements and training data. This enables us to make statements such as ”with probability 0.6, the patient will survive between 3 and 4 years”. A similar approach is used to compute relevance values with confidence intervals for the individual genes measured.The method is evaluated on a simulated dataset, showing feasibility of the approach. We then apply the algorithm to a publicly available dataset on diffuse large B-cell lymphoma, a cancer of the lymphocytes, and demonstrate that it successfully predicts survival times and survival time distributions for the individual patient.
    Keywords: Computer Science ; Computational Biology/Bioinformatics ; Data Mining and Knowledge Discovery ; Artificial Intelligence (Incl. Robotics) ; Information Storage and Retrieval ; Probability and Statistics in Computer Science ; Computer Appl. in Life Sciences ; Biology ; Computer Science
    ISBN: 9783540712329
    ISBN: 3540712321
    Source: SpringerLink Books
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  • 4
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Lecture Notes in Computer Science, Bioinformatics Research and Development: First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007. Proceedings, pp.1-15
    Description: Differential equations have been established to model the dynamic behavior of gene regulatory networks in the last few years. They provide a detailed insight into regulatory processes at a molecular level. However, in a top down approach aiming at the inference of the underlying regulatory network from gene expression data, the corresponding optimization problem is usually severely underdetermined, since the number of unknowns far exceeds the number of timepoints available. Thus one has to restrict the search space in a biologically meaningful way.We use differential equations to model gene regulatory networks and introduce a Bayesian regularized inference method that is particularly suited to deal with sparse and noisy datasets. Network inference is carried out by embedding our model into a probabilistic framework and maximizing the posterior probability. A specifically designed hierarchical prior distribution over interaction strenghts favours sparse networks, enabling the method to efficiently deal with small datasets.Results on a simulated dataset show that our method correctly learns network structure and model parameters even for short time series. Furthermore, we are able to learn main regulatory interactions in the yeast cell cycle.
    Keywords: Computer Science ; Computational Biology/Bioinformatics ; Data Mining and Knowledge Discovery ; Artificial Intelligence (Incl. Robotics) ; Information Storage and Retrieval ; Probability and Statistics in Computer Science ; Computer Appl. in Life Sciences ; Biology ; Computer Science
    ISBN: 9783540712329
    ISBN: 3540712321
    Source: SpringerLink Books
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