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Population-based 3D genome structure analysis reveals driving forces in spatial genome organization.
Tjong, Harianto; Li, Wenyuan; Kalhor, Reza; Dai, Chao; Hao, Shengli; Gong, Ke; Zhou, Yonggang; Li, Haochen; Zhou, Xianghong Jasmine; Le Gros, Mark A; Larabell, Carolyn A; Chen, Lin; Alber, Frank.
Afiliación
  • Tjong H; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Li W; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Kalhor R; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Dai C; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Hao S; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Gong K; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Zhou Y; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Li H; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Zhou XJ; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089;
  • Le Gros MA; Department of Anatomy, University of California, San Francisco, CA 94148; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94702; National Center for X-Ray Tomography, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94702;
  • Larabell CA; Department of Anatomy, University of California, San Francisco, CA 94148; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94702; National Center for X-Ray Tomography, Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA 94702;
  • Chen L; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089; Department of Chemistry and Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089.
  • Alber F; Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089; alber@usc.edu.
Proc Natl Acad Sci U S A ; 113(12): E1663-72, 2016 Mar 22.
Article en En | MEDLINE | ID: mdl-26951677
Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cromosomas / Imagenología Tridimensional / Metagenómica Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2016 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cromosomas / Imagenología Tridimensional / Metagenómica Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2016 Tipo del documento: Article Pais de publicación: Estados Unidos