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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Fukunaga, Tsukasa Iwasaki, Wataru |
| 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. |
| Related Links | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-016-1408-8.pdf |
| Ending Page | 9 |
| Page Count | 9 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712105 |
| DOI | 10.1186/s12859-016-1408-8 |
| Journal | BMC Bioinformatics |
| Issue Number | 1 |
| Volume Number | 18 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2017-01-19 |
| Access Restriction | Open |
| Subject Keyword | Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Computational ethology Eigenworms Worm posture analysis Transition patterns of postures Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology |
| Journal Impact Factor | 2.9/2023 |
| 5-Year Journal Impact Factor | 3.6/2023 |
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