Jay Snoddy

Department of Biomedical Informatics (Started Nov 1, 2005)

Contact Information

• Email: j.snoddy@vanderbilt.edu
• Phone Number: (to be determined)
• Lab Web Site (to be established)
• Personal Web Site (to be established)
• I will often attend the "Omics" biostatistics clinics and will also have office hours. Also feel free to email me.

Research Interests

Overview

• My research interests include a comparative and integrative bioinformatics that can be used to study human and model organism data. In addition, I am also interested in a collaborative bioinformatics that can assist large groups to work together or acquire large data sets.
• My research group and collaborators are particularly interested in collaborations with researchers that wish to exploit and analyze molecular and cellular data from different species, different tissues, or different biological conditions. (Note well, even if you only work in human or one model organism, we might be able to find data from other species or conditions. It would also be helpful if you could point to available and similar data of interest to you from other species (perhaps data sets from collaborators or large data sets shared on the web).

Background and Motivation

Until we had completely sequenced genomes, several interesting research questions were not possible to pursue. However these data sets have opened up new research vistas. My research group is interested in working on new opportunities that are created by combining the entire genome and proteome with large-scale systems biology data sets--such as gene expression or protein data sets.

Now that we have sequence from multiple species, we may be able to exploit evolutionary reasoning in systems biology. We should, for example, be able to better appreciate the macroevolutionary and microevolutionary processes that helped create the characteristics of the human genome, the networked complexity of biological systems, and the present range of human variation.

However, we will need a few specific application areas to test ideas and approaches. We will likely need experimental collaborators with expertise and data sets in one or more areas to make progress. We will also need to engineeer practical bioinformatics tools to enable manipulation and integration in large genome, transcriptosome, and proteome data sets for ourselves and our collaborators. To achieve this we may also have to engineer practical information systems that enable the collaborative acquisition of large data sets.

Comparative and Integrative Bioinformatics

Comparative and Integrative Bioinformatics can enable the integrated analysis of genome sequence, gene expression, protein sequence, and the systems biology of biological networks. In particular, comparative biology and model organism data can be exploited for human biomedical research. This leads to both foundational research interests and practical interests in bioinformation systems engineering.

• A long-standing foundational and theoretical interest of mine, for example, has been the evolution of developmental and physiological mechanisms. If we wish to engineer biomedical interventions to assist in human health, it may be important to understand the foundational processes of how these systems were engineered by evolution. If genome sequence analysis (e.g. BLAST) has taught us anything, it has made clear the importance of evolution-based reasoning in trying to understand biological systems and their complexity. We need to develop a general conceptual and computational foundation to compare biological networks, not just biological sequences. It is an important to understand how biological sequence, system functions, and networked interactions of orthologs and paralogs changed or stayed the same during the expansion of the metazoa. Both speciation events and gene duplication events allowed genes and the networked interactions among gene products to diverge, drift, or be conserved.
• A practical bioinformatics system engineering interest related to this is in building data mining and visualization systems that can help users integrate and analyze large sets of human and non-human data.

Collaborative Bioinformatics

Collaborative Bioinformatics can build information systems to support large or complex projects.

• The web was originally designed, in part, to be a "read-write" mechanism to share and collaborate with scientific resources accessible from a unique pointer of a web address. Large scale projects might benefit from several approaches that can make the web a more productive place to collaborate and share resources. Databases can also be built to assist large collaborations or large projects, including the construction of laboratory information management systems.
• From the genome project and subsequent research, I have some practical experience in the building of these systems. The interest of my research group would be highest if potential collaborations involve getting insights from large systems biology data sets from several different tissues or in different species, or both. Our interest in building these practical systems is, in part, driven by the opportunity to get more biomedical value out of different data sets by comparing and inetegrating data sets from different species and different collaborating research groups.

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