Fall 2009 Seminar Series
Wednesday, September 9
12noon - 1:00pm
106 Stanley Hall, UC Berkeley
Professor Ian Holmes
Department of Bioengineering
UC Berkeley
Evolutionary Bioinformatics and the RNA World
Abstract:
Evolution makes sense of biological diversity; the "RNA World"
hypothesis is a good example. This hypothesis proposes that life based
on RNA preceded the current world of life based on DNA. One prediction
of this hypothesis is that traces of the RNA World should be found in
the modern biosphere; this has been supported by the discovery (in the
40-some years since RNA-based life was first proposed) of an
increasingly broad menagerie of non-protein coding RNA genes including
ribozymes, riboswitches, and regulatory RNA.
Evolution is central to bioinformatics, too. My group develops
bioinformatics tools for problems ranging from the classical (such as
alignment, phylogeny, or gene discovery) to the emergent (such as
the reconstruction of ancient genomes, or the typing of recombinant
viruses). We make extensive use of statistical evolutionary models, as
well as other computational technologies such as automata theory,
natural language processing, Web 2.0 (e.g. wikis, AJAX), and parallel
computing.
The "RNA World" idea has been a consistent interest, and the evolution
of RNA is a recurrent theme in our work. Field-leading RNA analysis
tools developed by our group include software for efficient and
accurate multiple RNA alignment; phylogenetically-assisted prediction
of RNA secondary structure; and reconstruction of ancient RNA
genes.
In the near term, we aim to use these tools to reconstruct parts of
the "RNA World" using broad survey sequencing, statistical models and
direct DNA synthesis. As an example, the broad availability of
phylogenetically-diverse prokaryotic rRNA sequence suggests that it
may be possible soon to reconstruct the common ancestors of these
rRNAs and thereby test Crick's 1968 hypothesis that the earliest
ribosomes functioned in the absence of protein cofactors.
In this talk, I will describe the advances in computational
infrastructure that have brought such questions within direct reach,
and the remaining computational and bioengineering challenges to
supporting synthetic reconstruction of ancient genes and multi-gene
systems. I will also describe past and ongoing applications of these
techniques in the broader fields of computational biology and
genomics. Throughout, I will emphasize the statistical and engineering
principles informing our work, and the prominent role of evolutionary
theory among these.
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