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153 results on '"Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris"'

151. Contribution of epigenetic landscapes and transcription factors to X-chromosome reactivation in the inner cell mass

Author

Ikuhiro Okamoto, Konstantinos Anastassiadis, Katia Ancelin, Edith Heard, Rafael Galupa, Azim Surani, Laurène Syx, Mitinori Saitou, Guillaume Guilbaud, Christel Picard, Patricia Diabangouaya, Chong-Jian Chen, Nicolas Servant, Maud Borensztein, Emmanuel Barillot, Génétique et Biologie du Développement, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Cambridge [UK] (CAM), Kyoto University [Kyoto], JST, ERATO, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Medical Research Council Laboratory of Molecular Biology, Cambridge, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Borensztein, Maud [0000-0002-4378-5018], Ancelin, Katia [0000-0002-2117-9754], Galupa, Rafael [0000-0001-7319-043X], Barillot, Emmanuel [0000-0003-2724-2002], Apollo - University of Cambridge Repository, MINES ParisTech - École nationale supérieure des mines de Paris, and 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)

Subjects
Male, 0301 basic medicine, Embryology, Epigenetic memory, Science, General Physics and Astronomy, Biology, Article, General Biochemistry, Genetics and Molecular Biology, X-inactivation, Epigenesis, Genetic, Histones, Mice, 03 medical and health sciences, 0302 clinical medicine, Genes, X-Linked, Pregnancy, X Chromosome Inactivation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Animals, Inner cell mass, Epigenetics, Dosage compensation, lcsh:Science, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Transcription factor, In Situ Hybridization, Fluorescence, Multidisciplinary, Models, Genetic, Sequence Analysis, RNA, Reprogramming, General Chemistry, Chromatin, Cell biology, Mice, Inbred C57BL, 030104 developmental biology, Mice, Inbred DBA, Blastocyst Inner Cell Mass, biology.protein, Demethylase, lcsh:Q, Female, RNA, Long Noncoding, Single-Cell Analysis, 030217 neurology & neurosurgery, Transcription Factors
Abstract

X-chromosome inactivation is established during early development. In mice, transcriptional repression of the paternal X-chromosome (Xp) and enrichment in epigenetic marks such as H3K27me3 is achieved by the early blastocyst stage. X-chromosome inactivation is then reversed in the inner cell mass. The mechanisms underlying Xp reactivation remain enigmatic. Using in vivo single-cell approaches (allele-specific RNAseq, nascent RNA-fluorescent in situ hybridization and immunofluorescence), we show here that different genes are reactivated at different stages, with more slowly reactivated genes tending to be enriched in H3meK27. We further show that in UTX H3K27 histone demethylase mutant embryos, these genes are even more slowly reactivated, suggesting that these genes carry an epigenetic memory that may be actively lost. On the other hand, expression of rapidly reactivated genes may be driven by transcription factors. Thus, some X-linked genes have minimal epigenetic memory in the inner cell mass, whereas others may require active erasure of chromatin marks., X-chromosome inactivation is reversed in the mouse inner cell mass (ICM) through a mechanism that is not fully understood. Here, the authors investigate this process and characterize the contributions of the epigenetic landscape and transcription factors in X-linked gene reactivation dynamics.

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152. The Kendall and Mallows kernels for permutations

Author

Jiao, Y., Jean-Philippe Vert, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, 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), This work was supported by the European Union 7th Framework Program through the Marie Curie ITN MLPM grant No 316861, the European Research Council grant ERC- SMAC-280032, the Miller Institute for Basic Research in Science [to JPV], and the Fulbright Foundation [to JPV]., Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de Mathématiques et Applications - ENS Paris (DMA), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Centre de Bioinformatique ( CBIO ), MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL ), Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, and MINES ParisTech - École nationale supérieure des mines de Paris-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -INSTITUT CURIE

Subjects
Computational biology, Ranking learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Machine learning, Kernel methods, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], ComputingMilieux_MISCELLANEOUS
Abstract

We show that the widely used Kendall tau correlation coefficient, and the related Mallows kernel, are positive definite kernels for permutations. They offer computationally attractive alternatives to more complex kernels on the symmetric group to learn from rankings, or learn to rank. We show how to extend these kernels to partial rankings, multivariate rankings and uncertain rankings. Examples are presented on how to formulate typical problems of learning from rankings such that they can be solved with state-of-the-art kernel algorithms. We demonstrate promising results on clustering heterogeneous rank data and high-dimensional classification problems in biomedical applications.

153. Detecting separate time scales in genetic expression data

Author

Philip N. Benfey, Sebastian E. Ahnert, Siobhan M. Brady, David A. Orlando, Thomas M. A. Fink, Apollo - University of Cambridge Repository, BMC, Ed., Department of Biology, Duke University [Durham]-IGSP Center for Systems Biology, Whitehead Institute, Massachusetts Institute of Technology (MIT), Department of Plant Biology and Genome Center, University of California (UC), 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), Compartimentation et dynamique cellulaires (CDC), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), London Institute for Mathematical Sciences, Cavendish Laboratory, University of Cambridge [UK] (CAM), SMB was partially supported by a Natural Science and Engineering Research Council postdoctoral fellowship. This work was funded in part by the Defense Advanced Research Projects Agency (DARPA) Fundamental Laws of Biology (FunBio) grant HR 0011-05-1-0057 to PNB and TMAF, and by a NSF AT2010 grant to PNB. SEA was supported by The Leverhulme Trust, UK and The Royal Society, UK., University of California, Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC)

Subjects
0106 biological sciences, Time Factors, lcsh:QH426-470, Transcription, Genetic, lcsh:Biotechnology, Arabidopsis, MESH: Plant Roots, MESH: Cell Cycle, Computational biology, Saccharomyces cerevisiae, 01 natural sciences, Plant Roots, 03 medical and health sciences, MESH: Gene Expression Profiling, MESH: Benchmarking, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Multiple time, Genetics, Segmentation, MESH: Arabidopsis, 030304 developmental biology, 0303 health sciences, Plant roots, biology, Methodology Article, MESH: Transcription, Genetic, Gene Expression Profiling, MESH: Time Factors, Cell Cycle, Replicate, biology.organism_classification, MESH: Saccharomyces cerevisiae, Gene expression profiling, Plant development, lcsh:Genetics, Benchmarking, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], DNA microarray, 010606 plant biology & botany, Biotechnology
Abstract

Background Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales. Results We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions. Conclusions The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.

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