Deep phenotyping unveils hidden traits and genetic relations in subtle mutants

Adriana San-Miguel; Peri T. Kurshan; Matthew M. Crane; Yuehui Zhao; Patrick T. McGrath; Kang Shen; Hang Lu

doi:10.1038/ncomms12990

Nature Communications (Nov 2016)

Deep phenotyping unveils hidden traits and genetic relations in subtle mutants

Adriana San-Miguel,
Peri T. Kurshan,
Matthew M. Crane,
Yuehui Zhao,
Patrick T. McGrath,
Kang Shen,
Hang Lu

Affiliations

Adriana San-Miguel: School of Chemical and Biomolecular Engineering, Georgia Institute of Technology
Peri T. Kurshan: Department of Biology, Howard Hughes Medical Institute, Stanford University
Matthew M. Crane: Interdisciplinary Program in Bioengineering, Georgia Institute of Technology
Yuehui Zhao: School of Biological Sciences, Georgia Institute of Technology
Patrick T. McGrath: School of Biological Sciences, Georgia Institute of Technology
Kang Shen: Department of Biology, Howard Hughes Medical Institute, Stanford University
Hang Lu: School of Chemical and Biomolecular Engineering, Georgia Institute of Technology

DOI: https://doi.org/10.1038/ncomms12990
Journal volume & issue: Vol. 7, no. 1
pp. 1 – 13

Abstract

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Experimenter scoring of cellular imaging data can be biased. This study describes an automated and unbiased multidimensional phenotyping method that relies on machine learning and complex feature computation of imaging data, and identifies weak alleles affecting synapse morphology in live C. elegans.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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