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I have since graduated and I am currently at the Minnesota Supercomputing Institute. You can
still reach me using the email adddress in this page
- Profile
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I am interested in leveraging the power of computers to understand disease development.
Specifically, I work to develop tools that will analyze and help make sense of the large amounts of data
generated by high-throughput genomics and proteomics techniques.
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I believe bioinformatics research is well served not when computer scientist with no knowledge of intricasies
and complexities of biological systems apply computational techniques to biological problems, but when these
techniques are developed with an understanding of the complex nature of biological systems. It is unlikely
bioinformaticians will poses the same knowledge of protein structure as biochemist do but it is important
to at least understand basic principles when developing protein structure prediction algorithms. To this end,
I have taken several biological sciences courses such as Genetics, Biochemistry, Cell Biology and Protein Sequence
to better understand basic principles of biological systems. Taking these courses has proved invaluable in my
thesis work being co-advised by Dr. Timothy J. Griffin, in the Department of Biochemistry, Molecular Biology
and Biophysics and Dr. John V. Carlis in the Department of Computer Science and Engineering.
My scholarly contributions include:
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- Developed a technique for accurate protein quantification of isobaric
tagged peptide data from LTQ type tandem mass spectrometers.
- This technique was
implemented in a freely available open source software (
LTQ-iQuant). The software has several advantages over existing software which includes:
1) compatibility with centroided LTQ MS/MS data; 2) accounts for errors introduced by low reporter ion
intensities; and 3) flexible and gives users ability to customize the software to individual instruments.
Publication resulting from this work
Onsongo, G., Stone, M. D., Van Riper, S.K., Chilton ,J., Wu, B., Higgins, L.,
Lund, T.C., Carlis, J.V., and Griffin, T.G. (2010). LTQ-iQuant: A freely-available
software pipeline for automated and accurate protein quantification of isobaric tagged
peptide data from LTQ instruments , journal Proteomics (in press)
- Designed and implemented relational database operators for prioritizing
candidate disease biomarkers to identify the most promising candidate biomarkers
worth of follow up validation studies.
- High-throughput technologies used to identify
candidate diagnostic biomarkers for disease progression often lead to hundreds of
candidate biomarkers which must be validated before their specificity can be tested.
Because of the nature of techniques used to validate these candidate biomarkers (expensive and
time consuming) it is practically not feasible to validate each candidate biomarkers. These
operators help identify the most promising candidate biomarkers for additional analyses.
Publication resulting from this work
Onsongo, G., Xie, H., Griffin, T.J., and Carlis, J.V. (2010).
Relational operators for prioritizing candidate biomarkers in high-throughput
differential expression data, ACM BCB Aug 2-4, 2010. Niagara Falls, New York.
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- Developed relational operators for Gene Ontology (GO) database to dynamically generate
GO Slim version of the GO database.
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The Gene Ontology database consists of terms used to standardize the naming of genes and gene products.
These terms are a set of controlled vocabulary that can be applied to all eukaryotes even as
the knowledge of gene and protein roles in cells accumulates and changes. A GO Slim is a
variant of the Gene Ontology database that contains a small portion of the database. For some tasks,
such as analyzing results of an experiment, use of a GO Slim relevant to the experiment is preferred
to using the complete Gene Ontology database. Prior to this work, users had to rely on GO Slims provided
by The Gene Ontology consortium or use of a perl script to generate a static GO Slim which had to be
regenerated each time the GO database changed. This work makes it possible to dynamically create GO Slims
and as a result, users can generate their own custom GO Slims without relying on the generic GO slims
provided by the consortium. Additionally, there will be no need to update a GO Slim when a new version
of the GO database is released.
Publication resulting from this work
Onsongo, G., Xie, H., Griffin, T.J., and Carlis, J.V. (2010).
Generating GO Slim Using Relational Database Management Systems to Support Proteomics
Analysis, IEEE Symposium on Computer-Based Medical Systems 2008: 215 - 217.
- I have also collaborated in several other interdisciplinary projects that have led to
publications in competitive peer reviewed journals. Further details of these collaborations together
with contribution summaries can be found here
(publications)
If you have any questions, feel free to e-mail me at onsongo@cs.umn.edu.
