An ontology-based search engine for digital reconstructions of neuronal morphology.
An ontology-based search engine for digital reconstructions of neuronal morphology.
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基于本体的搜索引擎，用于神经元形态的数字重建。
DOI：
10.1007/s40708-017-0062-x
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发表时间：
2017
影响因子：
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通讯作者：
Ascoli,GiorgioA
中科院分区：
文献类型：
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作者：
Polavaram,Sridevi;Ascoli,GiorgioA
动物物种、发育阶段、大脑区域和细胞类型之间和内部的神经元形态极其多样化。这种多样性在功能上很重要，因为神经元结构强烈影响突触整合、尖峰动力学和网络连接。因此，轴突和树突轴的数字重建对于神经系统信息处理的量化和建模至关重要。 NeuroMorpho.Org 是一个成熟的存储库，包含数以万计的数字重建神经元，由全球数百个实验室共享。每个神经元都根据已发布的参考文献和数据所有者提供的其他详细信息用特定的元数据进行注释。多年来，随着可用数据的增加，所表示的元数据概念的数量也在增长。然而，到目前为止，缺乏标准化术语和充分结构化的元数据模式限制了用户搜索的有效性。在这里，我们提出了 NeuroMorpho.Org 元数据的新组织，该元数据基于一组互连的层次结构，重点关注动物物种、解剖区域和细胞类型的主要维度。我们将 NeuroMorpho.Org 中的每个元数据术语全面映射到这个形式本体，明确解决了由同义和同音引起的所有歧义。利用这个一致的框架，我们引入了 OntoSearch，这是一种强大的功能，可以通过具有自动完成功能的直观的基于字符串的用户界面，无缝地检索基于专家知识和逻辑推理的形态数据。除了返回直接匹配搜索条件的数据外，OntoSearch 还通过考虑不完整的元数据注释来识别可能的命中池。
Neuronal morphology is extremely diverse across and within animal species, developmental stages, brain regions, and cell types. This diversity is functionally important because neuronal structure strongly affects synaptic integration, spiking dynamics, and network connectivity. Digital reconstructions of axonal and dendritic arbors are thus essential to quantify and model information processing in the nervous system. NeuroMorpho.Org is an established repository containing tens of thousands of digitally reconstructed neurons shared by several hundred laboratories worldwide. Each neuron is annotated with specific metadata based on the published references and additional details provided by data owners. The number of represented metadata concepts has grown over the years in parallel with the increase of available data. Until now, however, the lack of standardized terminologies and of an adequately structured metadata schema limited the effectiveness of user searches. Here we present a new organization of NeuroMorpho.Org metadata grounded on a set of interconnected hierarchies focusing on the main dimensions of animal species, anatomical regions, and cell types. We have comprehensively mapped each metadata term in NeuroMorpho.Org to this formal ontology, explicitly resolving all ambiguities caused by synonymy and homonymy. Leveraging this consistent framework, we introduce OntoSearch, a powerful functionality that seamlessly enables retrieval of morphological data based on expert knowledge and logical inferences through an intuitive string-based user interface with auto-complete capability. In addition to returning the data directly matching the search criteria, OntoSearch also identifies a pool of possible hits by taking into consideration incomplete metadata annotation.