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BMC Bioinformatics. 2017 Jan 19;18(1):46. doi: 10.1186/s12859-016-1408-8.

Inactivity periods and postural change speed can explain atypical postural change patterns of Caenorhabditis elegans mutants.

Fukunaga T1,2,3, Iwasaki W4,5,6.

Author information

1
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. t.fukunaga@kurenai.waseda.jp.
2
Faculty of Science and Engineering, Waseda University, Tokyo, 169-0072, Japan. t.fukunaga@kurenai.waseda.jp.
3
Research Fellow of Japan Society for the Promotion of Science, Tokyo, Japan. t.fukunaga@kurenai.waseda.jp.
4
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, 277-8568, Japan. iwasaki@bs.s.u-tokyo.ac.jp.
5
Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, 277-8564, Japan. iwasaki@bs.s.u-tokyo.ac.jp.
6
Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, 113-0032, Japan. iwasaki@bs.s.u-tokyo.ac.jp.

Abstract

BACKGROUND:

With rapid advances in genome sequencing and editing technologies, systematic and quantitative analysis of animal behavior is expected to be another key to facilitating data-driven behavioral genetics. The nematode Caenorhabditis elegans is a model organism in this field. Several video-tracking systems are available for automatically recording behavioral data for the nematode, but computational methods for analyzing these data are still under development.

RESULTS:

In this study, we applied the Gaussian mixture model-based binning method to time-series postural data for 322 C. elegans strains. We revealed that the occurrence patterns of the postural states and the transition patterns among these states have a relationship as expected, and such a relationship must be taken into account to identify strains with atypical behaviors that are different from those of wild type. Based on this observation, we identified several strains that exhibit atypical transition patterns that cannot be fully explained by their occurrence patterns of postural states. Surprisingly, we found that two simple factors-overall acceleration of postural movement and elimination of inactivity periods-explained the behavioral characteristics of strains with very atypical transition patterns; therefore, computational analysis of animal behavior must be accompanied by evaluation of the effects of these simple factors. Finally, we found that the npr-1 and npr-3 mutants have similar behavioral patterns that were not predictable by sequence homology, proving that our data-driven approach can reveal the functions of genes that have not yet been characterized.

CONCLUSION:

We propose that elimination of inactivity periods and overall acceleration of postural change speed can explain behavioral phenotypes of strains with very atypical postural transition patterns. Our methods and results constitute guidelines for effectively finding strains that show "truly" interesting behaviors and systematically uncovering novel gene functions by bioimage-informatic approaches.

KEYWORDS:

Computational ethology; Eigenworms; Transition patterns of postures; Worm posture analysis

PMID:
28103804
PMCID:
PMC5244558
DOI:
10.1186/s12859-016-1408-8
[Indexed for MEDLINE]
Free PMC Article

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