Text mining and expert curation to develop a database on psychiatric diseases and their genes

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.