Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations

Abstract

Technological advancements in the field of genetics have led not only to an abundance of experimental data, but also caused an exponential increase of the number of published biomolecular studies. Text mining is widely accepted as a promising technique to help researchers in the life sciences deal with the amount of available literature. This paper presents a freely available web application built on top of 21.3 million detailed biomolecular events extracted from all PubMed abstracts. These text mining results were generated by a state-of-the-art event extraction system and enriched with gene family associations and abstract generalizations, accounting for lexical variants and synonymy. The EVEX resource locates relevant literature on phosphorylation, regulation targets, binding partners, and several other biomolecular events and assigns confidence values to these events. The search function accepts official gene/protein symbols as well as common names from all species. Finally, the web application is a powerful tool for generating homology-based hypotheses as well as novel, indirect associations between genes and proteins such as coregulators.

Figure 1

Event representation of the statement IL-2 acts by enhancing binding activity of NF-κB to p55, illustrating recursive nesting of events where the (T)heme of the positive regulation event is the binding event. The (C)ause argument is the gene symbol IL-2 (figure adapted from [10]).

Figure 2

Evaluation of predicted binding events, measured against the gold-standard data of the ST'09 development set. By sorting the events according to their confidence values, a tradeoff between precision and recall is obtained.

Figure 3

Search results for Mec1 on the canonical generalization. An overview of directly associated genes is presented, grouped by event type. In the screenshot, only the box with regulation targets is shown, but the other event types may also be expanded. At the bottom, relevant links to additional sentences and articles are provided.

Figure 4

Detailed representation of all evidence supporting the regulation of RAD9 by Mec1. Regulatory mechanisms can have a certain polarity (positive/negative) and may involve physical events such as phosphorylation or protein-DNA binding.

Figure 5

All events linking Mec1 and RAD9 through either direct or indirect associations. In the screenshot, only the regulation boxes are shown in detail, but the other event types may also be expanded. This page enables a quick overview of the mechanisms through which two genes interact, while at the same time highlighting false positive text mining results which can be identified by comparing confidence values and the evidence found in the sentences.

Figure 6

Visualization of a specific event occurrence by the stav text annotation visualiser. Genes and gene products (“GGPs”) are marked, as well as the trigger words that refer to specific event types. Finally, arrows denote the roles of each argument in the event (e.g. Theme or Cause). This visualization corresponds to the formal bracketed format of the event: Positive-regulation(C: Mec1, T:Phosphorylation(T:RAD9)).