PUBLICATION
Automated Phenotype Pattern Recognition of Zebrafish for High-throughput Screening
- Authors
- Schutera, M., Dickmeis, T., Mione, M., Peravali, R., Marcato, D., Reischl, M., Mikut, R., Pylatiuk, C.
- ID
- ZDB-PUB-160611-2
- Date
- 2016
- Source
- Bioengineered 7(4): 261-5 (Journal)
- Registered Authors
- Dickmeis, Thomas, Mikut, Ralf, Mione, Marina, Peravali, Ravindra, Pylatiuk, Christian
- Keywords
- feature detection, high-throughput screening, pattern recognition, support vector machine, zebrafish (Danio rerio)
- MeSH Terms
-
- Algorithms
- Animals
- High-Throughput Screening Assays
- Image Processing, Computer-Assisted
- Larva/genetics
- Larva/metabolism
- Models, Genetic
- Pattern Recognition, Automated*
- Phenotype
- Software
- Zebrafish/embryology*
- Zebrafish/genetics*
- PubMed
- 27285638 Full text @ Bioengineered
Citation
Schutera, M., Dickmeis, T., Mione, M., Peravali, R., Marcato, D., Reischl, M., Mikut, R., Pylatiuk, C. (2016) Automated Phenotype Pattern Recognition of Zebrafish for High-throughput Screening. Bioengineered. 7(4):261-5.
Abstract
Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of four different phenotypes can be classified through pattern recognition at 72 hours post fertilization (hpf), allowing the software to classify an embryo into two distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79-99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping