CONICET | Buscador de Institutos y Recursos Humanos

ATGC Transcriptomics: An application to explore and analyze de novo transcriptomic data. Sergio Gonzalez1,2,, Bernardo Clavijo3, Patricio Moreno1, Máximo Rivarola1,2, Paula Fernandez1,2, Marisa Farber1,2 and Norma B Paniego1,2 1 Instituto Nacional de Tecnología Agropecuaria/Instituto de Biotecnología, Hurlingham, Argentina, 2 CONICET, Argentina. 3 The Genome Analysis Centre (TGAC), Norwich, UK Background The availability, affordability and extent of genomics and genetics research have been steadily increasing over the last years, as well as the need to provide accurate and reliable access to data so as to take advantage of the integration of genome and transcriptome analyses. Currently, massively parallelized RNA sequencing technology (RNA-seq) in organisms lacking of a complete sequenced genome become a valuable tool to characterize non-model species [1], providing with the possibility to evaluate not only transcriptional events but also differential gene expression by digital measurement along dissimilar assays. Transcriptome analysis of species with non-reference genome involves the integration of assembly together with structural and functional annotation. In the application presented here we combined the outputs coming from several in-silico analysis and merging results so as to check the reliability of the merged output. Results We performed set-up an ontology based database linked to a web interface to store, visualize, analyze and share de novo transcriptomic data. We also included information associated to each feature represented in the database and we generated new relationships among them. For example genetic markers (features) are associated to a transcript feature and information from expression assays and/or the presence of alternative splicing are all inter-connected. To accomplish this, we used the Chado schema from GMOD (Generic Model Organism Database, http://gmod.org) [2], an ontology driven relational database schema implemented in PostgreSQL. One of the main goals for ATGC transcriptomics was data integration and the ability to query such data utilizing controlled vocabulary and ontologies, i.e., Gene Ontology (GO) [3] and Sequence Ontology (SO) [4]. Data exploration and visualization are facilitated by using predefined relationships among ontologies terms, allowing users to move through graph structure and perform searches (i.e., combining functional annotation and sequence type information), conceding the creation of new hypothesis from data exploration. Conclusion ATGC transcriptomics is a user friendly flexible application to organize de novo transcriptomic data. The integration of assembly results, functional and structural annotation, expression analysis graph per assay/tissue and information associated to each feature through the use of ontologies enables easy exploration to organize data-driving analysis and generate new hypothesis. References [1] S. R. Strickler, A. Bombarely, and L. a Mueller, ?Designing a transcriptome next-generation sequencing project for a nonmodel plant species.,? American journal of botany, vol. 99, no. 2, pp. 257?66, Feb. 2012. [2] C. J. Mungall and D. B. Emmert, ?A Chado case study: an ontology-based modular schema for representing genome-associated biological information.,? Bioinformatics (Oxford, England), vol. 23, no. 13, pp. i337?46, Jul. 2007. [3] M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock, ?Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.,? Nature genetics, vol. 25, no. 1, pp. 25?9, May 2000. [4] K. Eilbeck, S. E. Lewis, C. J. Mungall, M. Yandell, L. Stein, R. Durbin, and M. Ashburner, ?The Sequence Ontology: a tool for the unification of genome annotations.,? Genome biology, vol. 6, no. 5, p. R44, Jan. 2005.

enviar mensaje