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Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression

Author

Listed:
  • Dmitri D. Pervouchine

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF
    Faculty of Bioengineering and Bioinformatics, Moscow State University)

  • Sarah Djebali

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Alessandra Breschi

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Carrie A. Davis

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Pablo Prieto Barja

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Alex Dobin

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Andrea Tanzer

    (Faculty of Chemistry, Institute for Theoretical Chemistry, University of Vienna)

  • Julien Lagarde

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Chris Zaleski

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Lei-Hoon See

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Meagan Fastuca

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Jorg Drenkow

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Huaien Wang

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

  • Giovanni Bussotti

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Baikang Pei

    (Program in Computational Biology and Bioinformatics, Yale University)

  • Suganthi Balasubramanian

    (Program in Computational Biology and Bioinformatics, Yale University
    Yale University)

  • Jean Monlong

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF
    McGill University)

  • Arif Harmanci

    (Program in Computational Biology and Bioinformatics, Yale University
    Yale University)

  • Mark Gerstein

    (Program in Computational Biology and Bioinformatics, Yale University
    Yale University
    Yale University)

  • Michael A. Beer

    (Johns Hopkins University)

  • Cedric Notredame

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Roderic Guigó

    (Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF)

  • Thomas R. Gingeras

    (Functional Genomics Group, Cold Spring Harbor Laboratory)

Abstract

Mice have been a long-standing model for human biology and disease. Here we characterize, by RNA sequencing, the transcriptional profiles of a large and heterogeneous collection of mouse tissues, augmenting the mouse transcriptome with thousands of novel transcript candidates. Comparison with transcriptome profiles in human cell lines reveals substantial conservation of transcriptional programmes, and uncovers a distinct class of genes with levels of expression that have been constrained early in vertebrate evolution. This core set of genes captures a substantial fraction of the transcriptional output of mammalian cells, and participates in basic functional and structural housekeeping processes common to all cell types. Perturbation of these constrained genes is associated with significant phenotypes including embryonic lethality and cancer. Evolutionary constraint in gene expression levels is not reflected in the conservation of the genomic sequences, but is associated with conserved epigenetic marking, as well as with characteristic post-transcriptional regulatory programme, in which sub-cellular localization and alternative splicing play comparatively large roles.

Suggested Citation

  • Dmitri D. Pervouchine & Sarah Djebali & Alessandra Breschi & Carrie A. Davis & Pablo Prieto Barja & Alex Dobin & Andrea Tanzer & Julien Lagarde & Chris Zaleski & Lei-Hoon See & Meagan Fastuca & Jorg D, 2015. "Enhanced transcriptome maps from multiple mouse tissues reveal evolutionary constraint in gene expression," Nature Communications, Nature, vol. 6(1), pages 1-11, May.
  • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms6903
    DOI: 10.1038/ncomms6903
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    Cited by:

    1. Hillary Koch & Cheryl A. Keller & Guanjue Xiang & Belinda Giardine & Feipeng Zhang & Yicheng Wang & Ross C. Hardison & Qunhua Li, 2022. "CLIMB: High-dimensional association detection in large scale genomic data," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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