biography
The key challenge in bioinformatics today is not the development of new algorithms, but truly understanding the available data. - Bjoern Peters, Ph.D.
Bjoern
Peters is an Assistant Member in the Vaccine Discovery Division. Dr.
Peters' research focus is on the analysis of immunological information
using statistical and computational methods, with a particular interest
in modeling the recognition of immune epitopes. In 2000, Dr. Peters received his Diploma from the University of Hamburg in Germany, for a thesis in Laser Physics and Quantum Optics. Dr. Peters then undertook graduate work at the Humboldt University in Berlin,
where he became interested in applying quantitative methods commonly
used in physics to immunological questions. He earned his PhD in
Theoretical Biophysics in 2003 with summa cum laude, writing his thesis
on modeling the MHC class I antigen processing and presentation pathway. In 2004, Dr. Peters came to LIAI for
a postdoctoral training position in Dr. Sette's lab. From the start, he
was heavily involved in the Immune Epitope Database project (http://www.immuneepitope.org),
and became its Co-PI in charge of bioinformatics in 2005. Between 2006
and 2007 he was a Research Scientist, before being appointed Assistant Member at LIAI at the start of 2008.
research focus
Dr.
Peters and his team are developing tools to analyze and predict what
parts of a pathogen or allergen are targeted by immune responses.
Several of the molecular mechanisms involved in these processes have
been well characterized experimentally. By analyzing patterns in the
experimental data, it is possible to create predictive computational
models. These models can be applied to scan allergens or pathogens
in silico for
likely immune response targets. Identifying these targets aids in the
rational development of treatments and diagnostics. The resulting
computational tools are made freely available as part of the Immune
Epitope Database Analysis Resource (
http://tools.immuneepitope.org) The
Immune Epitope Database itself catalogs and organizes immune epitope
data, which requires transforming free text information from journal
publications into a structured format. To make optimal use of the
stored information, it is desirable to connect it with information
stored elsewhere. For example, one could ask what the variability of an
immune response target in different strains of a pathogen is. This
requires connecting the IEDB data to other resources storing genomic
information. Doing this efficiently requires a community consensus on
knowledge representation standards. Dr. Peters team is contributing to
such consensus building and standardization efforts through active work
on scientific community initiatives: The Ontology of Biomedical
Investigations (OBI,
http://obi.sourceforge.net/), and the NIAID data interoperability working group.
selected publications
Kinetic analysis of a complete poxvirus
transcriptome reveals an immediate-early class of genes. Proc Natl Acad Sci U S A. 2008
Automating document classification for the
Immune Epitope Database. BMC Bioinformatics. 2007
Integrating epitope data into the emerging web
of biomedical knowledge resources. Nat Rev Immunol. 2007
Towards a consensus on datasets and evaluation
metrics for developing B-cell epitope prediction tools. J Mol Recognit. 2007
Immune epitope mapping in the post-genomic era:
lessons for vaccine development. Curr Opin Immunol. 2007
A community resource benchmarking predictions of
peptide binding to MHC-I molecules. PLoS Comput Biol. 2006
A consensus epitope prediction approach
identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol. 2006
A computational resource for the prediction of
peptide binding to Indian rhesus macaque MHC class I molecules. Vaccine. 2005
Generating quantitative models describing the
sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 2005
The immune epitope database and analysis
resource: from vision to blueprint. PLoS Biol. 2005
Examining the independent binding assumption for
binding of peptide epitopes to MHC-I molecules. Bioinformatics. 2003
Identifying MHC class I epitopes by predicting
the TAP transport efficiency of epitope precursors. J Immunol. 2003
Assessment of proteasomal cleavage probabilities
from kinetic analysis of time-dependent product formation. J Mol Biol. 2002
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