This article is part of the supplement: Proceedings of the 6th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-meeting 2010)
Preimplantation development regulatory pathway construction through a text-mining approach
1 Laboratório Biodados, Dept. de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte - MG, Brazil
2 Departamento de Bioquímica, Universidade de São Paulo - SP, Brazil
3 Computational Biology and Data Mining Group, Max-Delbrück Center for Molecular Medicine, Robert-Rössle-Strasse 10, D-13125, Berlin, Germany
4 Bioinformatics Graduate Program, Federal University of Paraná - UFPR (SEPT). Rua Dr. Alcides Vieira Arcoverde 1225, CEP 81520-260. Curitiba-PR, Brazil
5 New York State Stem Cell Science, New York State Department of Health Wadsworth Center, Rm C345, New York, USA
Citation and License
BMC Genomics 2011, 12(Suppl 4):S3 doi:10.1186/1471-2164-12-S4-S3Published: 22 December 2011
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.
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.
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.