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Octavio Martinez de la Vega

Computational Biology

Octavio Martinez de la Vega

Computational Biology

We are interested in designing methods for gaining biological knowledge from the analysis of high-throughput data, mainly from genomics and transcriptomics. The philosophy of the group is that biological discovery is preceded, or at least run in parallel, to the design of proper methods of data analysis. This is particularly true now that Biology is flood with quantitative data arising from the new sequencing technologies, microarrays, etc. We have worked on methods for the estimation of quantitative trait loci, as well as the estimation of genetic diversity using molecular markers, automatic pipelines for processing DNA sequences (ESTs), algorithms for the assembling and annotation of genomes and currently we are interested in the application of Shannon’s information theory to estimate transcriptome properties.

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Papers related with current research

  • Nathalia M.V. Flórez-Zapata, M. Humberto Reyes-Valdés, Fernando Hernandez- Godínez and Octavio Martínez (2014). Transcriptomic landscape of prophase I sunflower male meiocytes. Front. Plant Sci. - Plant Genetics and Genomics Front Plant Sci.; 5: 277. Published online 2014 Jun 16. doi: 10.3389/fpls.2014.00277

  • Luis A Martínez-López, Neftalí Ochoa-Alejo and Octavio Martínez (2014). Dynamics of the chili pepper transcriptome during fruit development. BMC Genomics 15:143 doi:10.1186/1471-2164-15-143

  • Luis Fernando García-Ortega and Octavio Martínez (2015). How many genes are expressed in a transcriptome? Estimation and results for RNA-seq. PLoS One Accepted, May 2015.

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