The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Jurg Ott - One of the best experts on this subject based on the ideXlab platform.
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Statistical Gene Mapping of traits in humans-hypertension as a complex trait: Is it amenable to Genetic analysis?
Seminars in nephrology, 2002Co-Authors: Michael Nothnagel, Jurg OttAbstract:Gene Mapping refers to the localization of disease Genes on the human Gene map. The currently used mathematical methods for Gene Mapping are outlined and compared. The example of Gilles de la Tourette syndrome is discussed in detail.
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A train of thoughts on Gene Mapping.
Theoretical population biology, 2001Co-Authors: Josephine Hoh, Jurg OttAbstract:Complex traits, by definition, are the pheonotypic outcome from multiple interacting Genes. The traditional analysis of association studies on complex traits is to test one locus at a time, but a better approach is to analyze all markers simultaneously. We previously proposed a two-stage approach, first selecting the influential markers and then modeling main and interaction effects of these markers. Here we introduce alternative approaches to marker selection and discuss issues regarding analytical tools for disease Gene Mapping, marker selection, and statistical modeling.
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Statistical approaches to Gene Mapping.
American journal of human genetics, 2000Co-Authors: Jurg Ott, Josephine HohAbstract:In this brief primer, we hope to provide a General overview on statistical methods for disease-Gene Mapping. Of course, this cannot be complete—our apologies to researchers whose methods are not mentioned below. More-detailed information may be found in relevant textbooks (Ott 1999) and at the Web Resources of Genetic Linkage Analysis site (Laboratory of Statistical Genetics, Rockefeller University). The main purpose of this primer is to present, in a nontechnical manner, the methodological background and rationale of Genetic Mapping and to relate the various approaches to each other. In addition, current analysis methods for analysis of microarray data are discussed. Microarray data represent a new type of information that can provide important insight about the interaction of Genes and that thus can complement the statistical approaches to Gene Mapping. Statistical Genetic-Mapping methods all rest on one biological phenomenon, recombination (crossing-over), which is exploited for the purposes of determining the Genetic distance—or at least the closeness—between two loci. Crossovers between homologous chromosome strands occur semirandomly. Loci in close proximity to each other will rarely be separated by a recombination, whereas, for distant loci, recombinations occur as often as not. This phenomenon is used to derive a statistical measure of Genetic distance. In family pedigrees, recombinations may be seen more or less directly; on the other hand, the consequences of recombinations in past Generations can be observed in the form of linkage disequilibrium—that is, the preferential occurrence, in one gamete, of specific alleles at different loci.
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INVITED EDITORIAL Statistical Approaches to Gene Mapping
2000Co-Authors: Jurg Ott, Josephine HohAbstract:In this brief primer, we hope to provide a General overview on statistical methods for disease-Gene Mapping. Of course, this cannot be complete—our apologies to researchers whose methods are not mentioned below. More-detailed information may be found in relevant textbooks (Ott 1999) and at the Web Resources of Genetic Linkage Analysis site (Laboratory of Statistical Genetics, Rockefeller University). The main purpose of this primer is to present, in a nontechnical manner, the methodological background and rationale of Genetic Mapping and to relate the various approaches to each other. In addition, current analysis methods for analysis of microarray data are discussed. Microarray data represent a new type of information that can provide important insight about the interaction of Genes and that thus can complement the statistical approaches to Gene Mapping. Statistical Genetic-Mapping methods all rest on one biological phenomenon, recombination (crossing-over), which is exploited for the purposes of determining the Genetic distance—or at least the closeness—between two loci. Crossovers between homologous chromosome strands occur semirandomly. Loci in close proximity to each other will rarely be separated by a recombination, whereas, for distant loci, recombinations occur as often as not. This phenomenon is used to derive a statistical measure of Genetic distance. In family pedigrees, recombinations may be seen more or less directly; on the other hand, the consequences of recombinations in past Generations can be observed in the form of linkage disequilibrium—that is, the preferential occurrence, in one gamete, of specific alleles at different loci.
Alia Benkahla - One of the best experts on this subject based on the ideXlab platform.
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Methodology optimizing SAGE library tag-to-Gene Mapping: application to Leishmania.
BMC research notes, 2012Co-Authors: Sondos Smandi, Fatma Z. Guerfali, Mohamed Farhat, Khadija Ben-aissa, Dhafer Laouini, Lamia Guizani-tabbane, Koussay Dellagi, Alia BenkahlaAbstract:Background Leishmaniasis are widespread parasitic-diseases with an urgent need for more active and less toxic drugs and for effective vaccines. Understanding the biology of the parasite especially in the context of host parasite interaction is a crucial step towards such improvements in therapy and control. Several experimental approaches including SAGE (Serial analysis of Gene expression) have been developed in order to investigate the parasite transcriptome organisation and plasticity. Usual SAGE tag-to-Gene Mapping techniques are inadequate because almost all tags are normally located in the 3'-UTR outside the CDS, whereas most information available for Leishmania transcripts is restricted to the CDS predictions. The aim of this work is to optimize a SAGE libraries tag-to-Gene Mapping technique and to show how this development improves the understanding of Leishmania transcriptome.
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Methodology optimizing SAGE library tag-to-Gene Mapping: application to Leishmania.
BMC Research Notes, 2012Co-Authors: Sondos Smandi, Fatma Z. Guerfali, Mohamed Farhat, Khadija Ben-aissa, Dhafer Laouini, Lamia Guizani-tabbane, Koussay Dellagi, Alia BenkahlaAbstract:Leishmaniasis are widespread parasitic-diseases with an urgent need for more active and less toxic drugs and for effective vaccines. Understanding the biology of the parasite especially in the context of host parasite interaction is a crucial step towards such improvements in therapy and control. Several experimental approaches including SAGE (Serial analysis of Gene expression) have been developed in order to investigate the parasite transcriptome organisation and plasticity. Usual SAGE tag-to-Gene Mapping techniques are inadequate because almost all tags are normally located in the 3'-UTR outside the CDS, whereas most information available for Leishmania transcripts is restricted to the CDS predictions. The aim of this work is to optimize a SAGE libraries tag-to-Gene Mapping technique and to show how this development improves the understanding of Leishmania transcriptome. FINDINGS: The in silico method implemented herein was based on Mapping the tags to Leishmania genome using BLAST then Mapping the tags to their Gene using a data-driven probability distribution. This optimized tag-to-Gene Mappings improved the knowledge of Leishmania genome structure and transcription. It allowed analyzing the expression of a maximal number of Leishmania Genes, the delimitation of the 3' UTR of 478 Genes and the identification of biological processes that are differentially modulated during the promastigote to amastigote differentiation. CONCLUSION: The developed method optimizes the assignment of SAGE tags in trypanosomatidae genomes as well as in any genome having polycistronic transcription and small intergenic regions.
Jakob C Mueller - One of the best experts on this subject based on the ideXlab platform.
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Gene Mapping and marker clustering using shannon s mutual information
IEEE ACM Transactions on Computational Biology and Bioinformatics, 2006Co-Authors: Zaher Dawy, Bernhard Goebel, J Hagenauer, Christophe Andreoli, Thomas Meitinger, Jakob C MuellerAbstract:Finding the causal Genetic regions underlying complex traits is one of the main aims in human Genetics. In the context of complex diseases, which are believed to be controlled by multiple contributing loci of largely unknown effect and position, it is especially important to develop General yet sensitive methods for Gene Mapping. We discuss the use of Shannon's information theory for population-based Gene Mapping of discrete and quantitative traits and for marker clustering. Various measures of mutual information were employed in order to develop a comprehensive framework for Gene Mapping analyses. An algorithm aimed at finding so-called relevance chains of causal markers is proposed. Moreover, entropy measures are used in conjunction with multidimensional scaling to visualize clusters of Genetic markers. The relevance chain algorithm successfully detected the two causal regions in a simulated scenario. The approach has also been applied to a published clinical study on autoimmune (Graves') disease. Results were consistent with those of standard statistical methods, but identified an additional locus of interest in the promotor region of the associated Gene CTLA4. The developed software is freely available at http://www.lnt.ei.tum.de/download/InfoGeneMap/.
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ICASSP (5) - A novel Gene Mapping algorithm based on independent component analysis
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1Co-Authors: Zaher Dawy, J Hagenauer, Michel Sarkis, Jakob C MuellerAbstract:Identifying the causal Genetic markers responsible for complex diseases is a main aim in human Genetics. In the context of complex diseases, which are believed to have multiple causal loci of largely unknown effect and position, there is a need to develop advanced methods for Gene Mapping. In this work, we propose a novel algorithm based on independent component analysis for Gene Mapping. To apply the algorithm, we model the intra-cellular interactions as a mixing process of multiple sources. Results prove the superiority of the proposed algorithm over conventional statistical based methods, and demonstrate yet another successful application of a well known signal processing technique to an important problem in the field of human Genetics.
Barbara J Trask - One of the best experts on this subject based on the ideXlab platform.
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fluorescence in situ hybridization applications in cytoGenetics and Gene Mapping
Trends in Genetics, 1991Co-Authors: Barbara J TraskAbstract:Unique sequences, chromosomal subregions, or entire genomes can be specifically highlighted in metaphase or interphase cells by fluorescence in situ hybridization (FISH). This technique can be used to identify chromosomes, detect chromosomal abnormalities or determine the chromosomal location of specific sequences. FISH plays an increasingly important role in a variety of research areas, including cytoGenetics, prenatal diagnosis, tumor biology, Gene amplification and Gene Mapping.
Sondos Smandi - One of the best experts on this subject based on the ideXlab platform.
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Methodology optimizing SAGE library tag-to-Gene Mapping: application to Leishmania.
BMC research notes, 2012Co-Authors: Sondos Smandi, Fatma Z. Guerfali, Mohamed Farhat, Khadija Ben-aissa, Dhafer Laouini, Lamia Guizani-tabbane, Koussay Dellagi, Alia BenkahlaAbstract:Background Leishmaniasis are widespread parasitic-diseases with an urgent need for more active and less toxic drugs and for effective vaccines. Understanding the biology of the parasite especially in the context of host parasite interaction is a crucial step towards such improvements in therapy and control. Several experimental approaches including SAGE (Serial analysis of Gene expression) have been developed in order to investigate the parasite transcriptome organisation and plasticity. Usual SAGE tag-to-Gene Mapping techniques are inadequate because almost all tags are normally located in the 3'-UTR outside the CDS, whereas most information available for Leishmania transcripts is restricted to the CDS predictions. The aim of this work is to optimize a SAGE libraries tag-to-Gene Mapping technique and to show how this development improves the understanding of Leishmania transcriptome.
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Methodology optimizing SAGE library tag-to-Gene Mapping: application to Leishmania.
BMC Research Notes, 2012Co-Authors: Sondos Smandi, Fatma Z. Guerfali, Mohamed Farhat, Khadija Ben-aissa, Dhafer Laouini, Lamia Guizani-tabbane, Koussay Dellagi, Alia BenkahlaAbstract:Leishmaniasis are widespread parasitic-diseases with an urgent need for more active and less toxic drugs and for effective vaccines. Understanding the biology of the parasite especially in the context of host parasite interaction is a crucial step towards such improvements in therapy and control. Several experimental approaches including SAGE (Serial analysis of Gene expression) have been developed in order to investigate the parasite transcriptome organisation and plasticity. Usual SAGE tag-to-Gene Mapping techniques are inadequate because almost all tags are normally located in the 3'-UTR outside the CDS, whereas most information available for Leishmania transcripts is restricted to the CDS predictions. The aim of this work is to optimize a SAGE libraries tag-to-Gene Mapping technique and to show how this development improves the understanding of Leishmania transcriptome. FINDINGS: The in silico method implemented herein was based on Mapping the tags to Leishmania genome using BLAST then Mapping the tags to their Gene using a data-driven probability distribution. This optimized tag-to-Gene Mappings improved the knowledge of Leishmania genome structure and transcription. It allowed analyzing the expression of a maximal number of Leishmania Genes, the delimitation of the 3' UTR of 478 Genes and the identification of biological processes that are differentially modulated during the promastigote to amastigote differentiation. CONCLUSION: The developed method optimizes the assignment of SAGE tags in trypanosomatidae genomes as well as in any genome having polycistronic transcription and small intergenic regions.
