Abstract
Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET (http://www.psygenet.org/) has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype–phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators’ tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene–disease associations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL: http://www.psygenet.org PsyGeNET corpus: http://www.psygenet.org/ds/PsyGeNET/results/psygenetCorpus.tar
Highlights
Psychiatric disorders pose a substantial burden to the society with a high impact on morbidity and mortality [1,2]
BeFree was used to identify gene–disease associations (GDAs) from the literature based on the above Unified Medical Language System (UMLS) concepts selection and a set of GDCAs focused on the disorders of interest was identified
The 2507 genes identified by BeFree as associated with the disease categories (DCs) were submitted to expert curation
Summary
Psychiatric disorders pose a substantial burden to the society with a high impact on morbidity and mortality [1,2]. Despite the advances achieved in the field in the past 10 years, the large number of publications on the genetics of psychiatric disorders and the lack of suitable tools to efficiently explore this information prevent scientists from leveraging such a large volume of data. The PsyGeNET database has been developed by applying text mining tools to extract information from the scientific literature, which is subsequently validated by experts in psychiatry and neurosciences. In the past few years, text mining approaches have increasingly been adopted to assist the development and curation of knowledge resources [8]. The biocuration community has recognized the need to incorporate text mining solutions at different stages of the curation process to support the curators’ tasks. In PsyGeNET, we have incorporated text mining tools to assist the curation tasks of the experts
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