|
Notes:
|
One way to initiate the reconstruction of molecular circuits is by using automated
text-mining techniques. Developing more efficient methods for such reconstruction
is a topic of active research, and those methods are typically included by bioinfor-
maticians in pipelines used to mine and curate large literature datasets. Nevertheless,
experimental biologists have a limited number of available user-friendly tools that
use text-mining for network reconstruction and require no programming skills to
use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly
analysis of documents contained in a number of web-based literature databases to
identify co-occurrence of proteins/genes. This approach ensured results that were
always up-to-date with the latest live version of the databases. However, this `up-to-
dateness' came at the cost of large execution times. Here we report an evolution of
the application Biblio-MetReS that permits constructing co-occurrence networks
for genes, GO processes, Pathways, or any combination of the three types of entities
and graphically represent those entities.We show that the performance of Biblio-
MetReS in identifying gene co-occurrence is as least as good as that of other com-
parable applications (STRING and iHOP). In addition, we also show that the iden-
tification of GO processes is on par to that reported in the latest BioCreAtIvE chal-
lenge. Finally, we also report the implementation of a new strategy that combines
on-the-fly analysis of new documents with preprocessed information from docu-
ments that were encountered in previous analyses. This combination simultaneously
decreases program run time and maintains `up-to-dateness' of the results.
RA was partially supported by the Ministerio de Ciencia e Innovación (MICINN, Spain through grant BFU2010-17704). FS was partially funded by the MICINN, with grants TIN2011-28689-C02-02. The authors are members of the research groups 2009SGR809 and 2009SGR145, funded by the “Generalitat de Catalunya”. AU is funded by a Generalitat de Catalunya (AGAUR) PhD fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
