PhenoCurve: Capturing dynamic phenotype-environment relationships using phenomics data

Yifan Yang, Lei Xu, Zheyun Feng, Jeffrey A. Cruz, Linda J. Savage, David M. Kramer, Jin Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Motivation: Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments. Results: We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The results on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity.

Original languageEnglish
Pages (from-to)1370-1378
Number of pages9
Issue number9
StatePublished - May 1 2017

Bibliographical note

Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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