Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Department of Neurobiology, Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
BMC Biol. 2018 Jan 16;16(1):8. doi: 10.1186/s12915-017-0477-0.
Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis.
We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation.
WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a "quick and easy," convenient, high-throughput, and automated solution for nematode research.
秀丽隐杆线虫是一种强大的模式生物，但可见表型的量化仍然常常是劳动密集型的、有偏见的且容易出错的。我们开发了 WorMachine，这是一个基于 MATLAB 的三步图像分析软件，它允许（1）自动识别秀丽隐杆线虫，（2）提取形态特征和量化荧光信号，以及（3）用于高级分析的机器学习技术。
我们使用五个独立的代表性实验来检验 WorMachine 的功能：二元性别表型的监督分类、连续性别表型的评分、量化两种不同 RNA 干扰处理的效果以及测量细胞内蛋白质聚集。
WorMachine 适用于分析各种生物学问题，并提供了一种准确和可重复的分析工具，用于测量各种表型。它是线虫研究的一种“快速简便”、方便、高通量和自动化的解决方案。
