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3 results on '"ARRAY-CGH DATA"'

1. SegAnnDB: interactive web-based genomic segmentation

Author

Francis Bach, Masao Seto, Wilfrid Richer, Olivier Delattre, G Schleiermacher, Valentina Boeva, Guillem Rigaill, Toby Dylan Hocking, Isabelle Janoueix-Lerosey, Franck Bourdeaut, Miyuki Suguro, Jean-Philippe Vert, Department of Computer Science, Tokyo Institute of Technology [Tokyo] (TITECH), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Institut National de la Recherche Agronomique (INRA), Université d'Évry-Val-d'Essonne (UEVE), Centre National de la Recherche Scientifique (CNRS), Unité de génétique et biologie des cancers (U830), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Aichi Cancer Center Research Institute, Statistical Machine Learning and Parsimony (SIERRA), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), European Research Council [SIERRA-ERC-239993, SMAC-ERC-280032], Digiteo [DIGITEO-BIOVIZ-2009-25D], Annenberg Foundation, French Programme Hospitalier de Recherche Clinique [PHRC] [IC2007-09], French National Cancer Institute [INCA-2007-1-RT-4-IC], French Anti-Cancer League, Ministry of Health, Labor and Welfare of Japan, Ministry of Education, Culture, Sports, Science and Technology of the Japan, Japan Society for the Promotion of Science, Takeda Science Foundation, Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University (PSL), Université Paris Descartes - Paris 5 (UPD5)-Institut Curie-Institut National de la Santé et de la Recherche Médicale (INSERM), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, MINES ParisTech - École nationale supérieure des mines de Paris, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Descartes - Paris 5 (UPD5), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Tokyo Institute of Technology [Tokyo] ( TITECH ), MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE, Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), INRA, Institut National de la Recherche Agronomique ( INRA ), Unité de génétique et biologie des cancers ( U830 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut Curie-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Statistical Machine Learning and Parsimony ( SIERRA ), Département d'informatique de l'École normale supérieure ( DI-ENS ), École normale supérieure - Paris ( ENS Paris ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Paris ( ENS Paris ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ) -Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique ( Inria ), and PSL Research University (PSL)-MINES ParisTech - École nationale supérieure des mines de Paris

Subjects
Statistics and Probability, Source code, DNA Copy Number Variations, media_common.quotation_subject, Chromosome Breakpoints, Gene Expression, SNP, Genomics, Genome browser, Biology, Biochemistry, World Wide Web, Chromosome (genetic algorithm), Web application, TOOL, Segmentation, [ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Molecular Biology, ComputingMilieux_MISCELLANEOUS, media_common, Internet, Information retrieval, business.industry, ARRAY-CGH DATA, PROFILES, Original Papers, CANCER, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Visualization, Computational Mathematics, COPY NUMBER, Computational Theory and Mathematics, MICROARRAY DATA, VISUALIZATION, business, Algorithms, Software
Abstract

Motivation: DNA copy number profiles characterize regions of chromosome gains, losses and breakpoints in tumor genomes. Although many models have been proposed to detect these alterations, it is not clear which model is appropriate before visual inspection the signal, noise and models for a particular profile. Results: We propose SegAnnDB, a Web-based computer vision system for genomic segmentation: first, visually inspect the profiles and manually annotate altered regions, then SegAnnDB determines the precise alteration locations using a mathematical model of the data and annotations. SegAnnDB facilitates collaboration between biologists and bioinformaticians, and uses the University of California, Santa Cruz genome browser to visualize copy number alterations alongside known genes. Availability and implementation: The breakpoints project on INRIA GForge hosts the source code, an Amazon Machine Image can be launched and a demonstration Web site is http://bioviz.rocq.inria.fr. Contact: toby@sg.cs.titech.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Published
2014

2. Advanced analysis and visualization of gene copy number and expression data

Author

University of Helsinki, Research Program in Genome-Scale Biology, Autio, Reija, Saarela, Matti, Jarvinen, Anna-Kaarina, Hautaniemi, Sampsa, Astola, Jaakko, University of Helsinki, Research Program in Genome-Scale Biology, Autio, Reija, Saarela, Matti, Jarvinen, Anna-Kaarina, Hautaniemi, Sampsa, and Astola, Jaakko

Published
2009

3. Supervised Lowess normalization of comparative genome hybridization data - application to lactococcal strain comparisons

Author

Jan Kok, Victoria Martin-Requena, Sacha A. F. T. van Hijum, Richard J.S. Baerends, Aldert Zomer, Oscar P. Kuipers, Harma Karsens, Oswaldo Trelles, Groningen Biomolecular Sciences and Biotechnology, and Molecular Genetics

Subjects
Normalization (statistics), Sequence analysis, DIVERSITY, STREPTOCOCCUS-LACTIS, Computational biology, Biology, lcsh:Computer applications to medicine. Medical informatics, Sensitivity and Specificity, Genome, Biochemistry, Bacterial genetics, Structural Biology, Databases, Genetic, Lactococcus, Genetic variation, TOOL, lcsh:QH301-705.5, Gene, Molecular Biology, In Situ Hybridization, Oligonucleotide Array Sequence Analysis, GENE-EXPRESSION, Genetics, Applied Mathematics, CDNA MICROARRAY DATA, Chromosome Mapping, Genetic Variation, Reproducibility of Results, Comparative Genome Hybridization, ARRAY-CGH DATA, Sequence Analysis, DNA, FRAMEWORK, Computer Science Applications, lcsh:Biology (General), lcsh:R858-859.7, DNA microarray, Genome, Bacterial, Research Article
Abstract

Background Array-based comparative genome hybridization (aCGH) is commonly used to determine the genomic content of bacterial strains. Since prokaryotes in general have less conserved genome sequences than eukaryotes, sequence divergences between the genes in the genomes used for an aCGH experiment obstruct determination of genome variations (e.g. deletions). Current normalization methods do not take into consideration sequence divergence between target and microarray features and therefore cannot distinguish a difference in signal due to systematic errors in the data or due to sequence divergence. Results We present supervised Lowess, or S-Lowess, an application of the subset Lowess normalization method. By using a predicted subset of array features with minimal sequence divergence between the analyzed strains for the normalization procedure we remove systematic errors from dual-dye aCGH data in two steps: (1) determination of a subset of conserved genes (i.e. likely conserved genes, LCG); and (2) using the LCG for subset Lowess normalization. Subset Lowess determines the correction factors for systematic errors in the subset of array features and normalizes all array features using these correction factors. The performance of S-Lowess was assessed on aCGH experiments in which differentially labeled genomic DNA fragments of Lactococcus lactis IL1403 and L. lactis MG1363 strains were hybridized to IL1403 DNA microarrays. Since both genomes are sequenced and gene deletions identified, the success rate of different aCGH normalization methods in detecting these deletions in the MG1363 genome were determined. S-Lowess detects 97% of the deletions, whereas other aCGH normalization methods detect up to only 60% of the deletions. Conclusion S-Lowess is implemented in a user-friendly web-tool accessible from http://bioinformatics.biol.rug.nl/websoftware/s-lowess. We demonstrate that it outperforms existing normalization methods and maximizes detection of genomic variation (e.g. deletions) from microbial aCGH data.

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