Strategies towards digital and semi-automated curation in RegulonDB

Abstract

Experimentally generated biological information needs to be organized and structured in order to become meaningful knowledge. However, the rate at which new information is being published makes manual curation increasingly unable to cope. Devising new curation strategies that leverage upon data mining and text analysis is, therefore, a promising avenue to help life science databases to cope with the deluge of novel information. In this article, we describe the integration of text mining technologies in the curation pipeline of the RegulonDB database, and discuss how the process can enhance the productivity of the curators.Specifically, a named entity recognition approach is used to pre-annotate terms referring to a set of domain entities which are potentially relevant for the curation process. The annotated documents are presented to the curator, who, thanks to a custom-designed interface, can select sentences containing specific types of entities, thus restricting the amount of text that needs to be inspected. Additionally, a module capable of computing semantic similarity between sentences across the entire collection of articles to be curated is being integrated in the system. We tested the module using three sets of scientific articles and six domain experts. All these improvements are gradually enabling us to obtain a high throughput curation process with the same quality as manual curation.

L-Regulon: A novel “soft-curation” approach supported by a semantic enriched reading for RegulonDB literature

“…The pace with which scientific literature is being produced is clearly 2 imposing new challenges to develop effective strategies that leverage the 3 knowledge contained within thousands of publications. Content curation 4 plays a crucial role in feeding pre-designed models of knowledge that 5 support databases and, thus, in facilitating the finding of the more relevant 6 objects [19]. It acts as a filter that separates the signal from the noise.…”

“…This is the case 17 of RegulonDB a database on transcriptional regulation in E. coli K-12. 18 RegulonDB 1 [7] is a manually curated standard resource, an organized 19 and computable database, about the regulation of gene expression in 20 Escherichia coli K-12. As a product of a continuous effort since 1991 it 21 aims at integrating in a single repository all the scattered information 22 about genetic regulation in this microorganism including elements about 23 transcriptional regulation, such as: promoters, transcription units (TUs) 24 and operons, transcription factors (TFs), effectors that affect TFs, active 25 and inactive conformations of TFs, TFs binding sites (TFBSs), regulatory 26 interactions (RIs) of TFs with their target genes/TUs, terminators, 27 riboswitches, small RNAs and their target genes.…”