Reference : Preimplantation development regulatory pathway construction through a text-mining app...
Scientific journals : Article
Life sciences : Biotechnology
Systems Biomedicine
http://hdl.handle.net/10993/26644
Preimplantation development regulatory pathway construction through a text-mining approach.
English
Donnard, Elisa [> >]
Barbosa Da Silva, Adriano mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Guedes, Rafael L. M. [> >]
Fernandes, Gabriel R. [> >]
Velloso, Henrique [> >]
Kohn, Matthew J. [> >]
Andrade-Navarro, Miguel A. [> >]
Ortega, J. Miguel [> >]
2011
BMC genomics
12 Suppl 4
S3
Yes (verified by ORBilu)
1471-2164
1471-2164
England
[en] Animals ; Cluster Analysis ; Data Mining ; Databases, Factual ; Embryonic Development ; Gene Regulatory Networks ; Humans ; Information Storage and Retrieval ; Mice ; Software ; Stem Cell Transplantation
[en] BACKGROUND: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. RESULTS: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. CONCLUSIONS: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as "seeds" for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process.
http://hdl.handle.net/10993/26644

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