Mining clinical attributes of genomic variants through assisted literature curation in Egas

Abstract

The veritable deluge of biological data over recent years has led to the establishment of a considerable number of knowledge resources that compile curated information extracted from the literature and store it in structured form, facilitating its use and exploitation. In this article, we focus on the curation of inherited genetic variants and associated clinical attributes, such as zygosity, penetrance or inheritance mode, and describe the use of Egas for this task. Egas is a web-based platform for text-mining assisted literature curation that focuses on usability through modern design solutions and simple user interactions. Egas offers a flexible and customizable tool that allows defining the concept types and relations of interest for a given annotation task, as well as the ontologies used for normalizing each concept type. Further, annotations may be performed on raw documents or on the results of automated concept identification and relation extraction tools. Users can inspect, correct or remove automatic text-mining results, manually add new annotations, and export the results to standard formats. Egas is compatible with the most recent versions of Google Chrome, Mozilla Firefox, Internet Explorer and Safari and is available for use at https://demo.bmd-software.com/egas/.Database URL: https://demo.bmd-software.com/egas/

Accelerating annotation of articles via automated approaches: evaluation of the neXtA5 curation-support tool by neXtProt

“…There is little data in the literature where inter-curator agreement was evaluated, so it is difficult to judge whether this is expected. A recent study, showing the mining of clinical attributes of genomic variants using Egas, a web-based platform for text-mining-assisted literature curation, presented an overall IAA of 74% (13), while 2 other studies investigating the text-mining assisted biocuration workflows in Argo exhibited an IAA of 68.12% or varying between 67% and 84% (9, 10). Looking at some events of divergent decisions by the two curators, it seems that in most cases there was a drift from the curation guidelines and that if we return to the guidelines we can more often agree on the decision.…”

“…We assessed Textpresso Central (4), PubMed (5), NextBio, PolySearch (6), GoPubMed (7) and PubTator (8) and evaluated all parameter listed in Table 1. We also looked at the workflow of other text-mining tools, such as Argo (9–12), Egas (13), EXTRACT (14), MetastasisWay (15), Ontogene (16) and RegulonDB (17), but because they are dedicated to specific biomedical fields (and not appropriate for our use cases), we didn’t include them in our comparative study. The functionalities important to the curation workflow must be close in quality to that of manual annotation.…”

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

“…Knowledge discovery uses techniques from a wide range of disciplines such as artificial intelligence, machine learning, pattern recognition, data mining, and statistics [ 61 ]. Both information extraction and knowledge discovery find their application in database curation [ 62 , 63 ] and pathway construction [ 64 , 65 ].…”

A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases

“…Knowledge discovery uses techniques from a wide range of disciplines such as artificial intelligence, machine learning, pattern recognition, data mining, and statistics [45]. Both information extraction and knowledge discovery find their application in database curation [46], [47] and pathway construction [48], [49].…”