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Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence

Author

Listed:
  • Yuval Tabach

    (Massachusetts General Hospital
    Harvard Medical School)

  • Allison C. Billi

    (Life Sciences Institute, University of Michigan
    University of Michigan)

  • Gabriel D. Hayes

    (Massachusetts General Hospital
    Harvard Medical School)

  • Martin A. Newman

    (Massachusetts General Hospital
    Harvard Medical School)

  • Or Zuk

    (Broad Institute of MIT and Harvard)

  • Harrison Gabel

    (Massachusetts General Hospital
    Harvard Medical School)

  • Ravi Kamath

    (Massachusetts General Hospital
    Harvard Medical School)

  • Keren Yacoby

    (Massachusetts General Hospital)

  • Brad Chapman

    (Massachusetts General Hospital)

  • Susana M. Garcia

    (Massachusetts General Hospital
    Harvard Medical School)

  • Mark Borowsky

    (Massachusetts General Hospital
    Harvard Medical School)

  • John K. Kim

    (Life Sciences Institute, University of Michigan
    University of Michigan)

  • Gary Ruvkun

    (Massachusetts General Hospital
    Harvard Medical School)

Abstract

To identify comprehensively factors involved in RNAi and microRNA-mediated gene expression regulation, this study performed a phylogenetic analysis of 86 eukaryotic species; the candidates this approach highlighted were subjected to Bayesian analysis with transcriptional and proteomic interaction data, identifying protein orthologues of already known RNAi silencing factors, as well as other hits involved in splicing, suggesting a connection between the two processes.

Suggested Citation

  • Yuval Tabach & Allison C. Billi & Gabriel D. Hayes & Martin A. Newman & Or Zuk & Harrison Gabel & Ravi Kamath & Keren Yacoby & Brad Chapman & Susana M. Garcia & Mark Borowsky & John K. Kim & Gary Ruvk, 2013. "Identification of small RNA pathway genes using patterns of phylogenetic conservation and divergence," Nature, Nature, vol. 493(7434), pages 694-698, January.
    • Handle: RePEc:nat:nature:v:493:y:2013:i:7434:d:10.1038_nature11779
    DOI: 10.1038/nature11779
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    Cited by:

    1. Doron Stupp & Elad Sharon & Idit Bloch & Marinka Zitnik & Or Zuk & Yuval Tabach, 2021. "Co-evolution based machine-learning for predicting functional interactions between human genes," Nature Communications, Nature, vol. 12(1), pages 1-14, December.

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