David Ochoa

Bioinformatics & Systems Biology

I'm a Postdoctoral Fellow in Pedro Beltrao's group at EMBL-EBI. My research is focused on disentangling the complexity of biological systems at different levels. The understanding of this complexity plays a key role in the generation of data-driven hypothesis that can reveal the fundamental basis of disease, development, variability, evolution, etc.

Research interests

Genome-wide techniques are opening a new age in the study of biological systems. The generation of large amounts of data makes completely necessary the research in a new generation of concepts in order to process and analyze the incoming results. The integration of all this information is even a more ambitious task in which genomic, transcriptomic, or proteomic information - among others - could be combined to obtain more robust hypothesis.

As part of my post-doctoral research, I'm particularly interested on the evolutionary dynamics, specificity and functional relevance of post-translational modifications (PTMs). Considering the growing proteomic resource available nowadays, the comparative analysis of genome-wide modification sites can provide more insight on the specificity and evolution of the regulatory mechanisms of protein activity.

During my PhD in Florencio Pazos' group at CNB-CSIC, my research was primary focused on improving computational methods for predicting protein-protein interactions based on coevolution. These methods are founded on the hypothesis that interacting or functionally related proteins tend to adapt to each other during the evolutionary process. On that way, we proposed different modifications to the standard methods in order to take advantage of the growing information on protein sequence data. Moreover, we developed a web server to allow non-expert users to easily explore the tree similarities in a taxonomic context.

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Publications.

Journals

  • Ochoa, D, García-Gutiérrez, P, Juan, D, Valencia, A & Pazos, F: Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions. Molecular bioSystems 2013, 9:70–76.
  • Herman D, Ochoa D, Juan D, Lopez D, Valencia A & Pazos F: Selection of organisms for the co-evolution-based study of protein interactions. BMC Bioinformatics 2011, 12:363.
  • Ochoa D & Pazos F: Studying the co-evolution of protein families with the Mirrortree web server.Bioinformatics 2010, 26:1370–1371.

Events

  • Ochoa D, García P, Juan D, Pazos F: Coevolution and Predicted Solvent Accessibility. ISMB/ECCB 2011 Vienna.
  • Ochoa D, Pazos F: MirrorTree: a web server to study the coevolution between protein families. Jornadas Bioinformática 2009 Lisbon.
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Thesis.

Improving Co-evolution Based Methods for Protein-Protein Interaction Prediction

As an alternative to complement the experimental methods, a set of computational approaches have tried to take advantage of the different evolutive landmarks that interacting proteins print on their genes. For instance, evidence suggests that functionally related and potentially interacting proteins tend to evolve in a coordinated way, thereby presenting similar phylogenetic trees. A particularly successful family of methods, known as mirrortree, has emerged to quantify this co-evolution at a sequence level as a sign of interaction at a molecular level. The main proposal of this thesis is to diagnose the problems limiting the full implementation of co-evolution-based methods, in order to offer possible solutions, potential applications and foreseeable developments. [pdf] [defense]

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Other activities.

Teaching

Assistant Professor on MsC on Bioinformatics and Computational Biology. Practical and theoretical lectures on Biological Networks and Systems Biology from 2010-2012.

Reviewer

Reviewer for Oxford University Press's Bioinformatics (Impact Factor: 5.468)

Web Developer

Web Developer for several Websites including: Systems Biology Program, MirrorTree Server, Fostering Systems and Synthetic Biology in Southern Europe.

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Contact Me.

Please contact me for information about my profile, research, collaborations, postdoc positions, etc.

Computational Systems Biology Group
Systems Biology Program
c/ Darwin 3 (Campus Cantoblanco)
28049 Madrid

Email: dochoa [@] cnb.csic.es

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