Analyses and web interfaces for protein subcellular localization and gene expression data
Author
Bilen, Biter
Advisor
Çetin-Atalay, Rengül
Date
2007Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
In order to benefit maximally from large scale molecular biology data generated
by recent developments, it is important to proceed in an organized manner
by developing databases, interfaces, data visualization and data interpretation
tools. Protein subcellular localization and microarray gene expression are two
of such fields that require immense computational effort before being used as
a roadmap for the experimental biologist. Protein subcellular localization is important
for elucidating protein function. We developed an automatically updated
searchable and downloadable system called model organisms proteome subcellular
localization database (MEP2SL) that hosts predicted localizations and known
experimental localizations for nine eukaryotes. MEP2SL localizations highly correlated
with high throughput localization experiments in yeast and were shown
to have superior accuracies when compared with four other localization prediction
tools based on two different datasets. Hence, MEP2SL system may serve as
a reference source for protein subcellular localization information with its interface
that provides various search and download options together with links and
utilities for further annotations. Microarray gene expression technology enables
monitoring of whole genome simultaneously. We developed an online installable
searchable open source system called differentially expressed genes (DEG) that
includes analysis and retrieval interfaces for Affymetrix HG-U133 Plus 2.0 arrays.
DEG provides permanent data storage capabilities with its integration into
a database and being an installable online tool and is valuable for groups who
are not willing to submit their data on public servers.
Keywords
protein subcellular localization predictionproteome
web interface and database
eukaryotic model organisms
microarray gene expression