: IOB Scientists have developed database of Primary Immunodeficiency Disease genes known as 'Resource of Asian Primary Immunodeficiency Disease (RAPID)' in collabration with RIKEN, Japan. We have also developed algorithm for the prediction of candidate primary Immunodeficiency disease genes using support vector machine learning approach Results of these studies have been published in 'Nucleic Acids Research' and 'DNA Research' respectively.
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Molecular profiling of Embryonic Stem Cells and Embryonic Carcinoma Cells: Scientists from IOB and Pandey lab have identified key molecules that differentiate embryonic stem cells from embryonic carcinoma cells by quantitative proteomics. The findings from the above study was published in the journal Proteomics.
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Investigation of Molecular Markers of Neuronal Differentiation: Using advanced labeling technology, molecular markers of differentiation of motor neurons and astrocytes from embryonic stem cells were identified in a collaborative study between IOB, Pandey lab and Institute of Cell Engineering, Johns Hopkins University Findings from this study are published in Journal of Proteome Research.
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Quantitative Proteomic Investigation of Hepatocellular Carcinoma: IOB scientists unveil the first large scale quantitative proteomic profiling of hepatocellular carcinoma using robust discovery platforms such as iTRAQ labeling and high resolution mass spectrometry. The study revealed several novel and known upregulated proteins as potential biomarkers in hepatocellular carcinoma and results are published Journal of Proteome Research.
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Gene expression studies in Esophageal Squamous Cell Carcinoma: Scientists at IOB used a combination of DNA microarrays and immunohistochemical labeling of tissue microarrays to delineate gene expression patterns in cancer of esophagus and subsequently to validate molecular signatures specific to this cancer. Results of this investigation are published in Cancer Biology and Therapy.
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IOB scientists create the largest community resource of experimental data in human proteins.
It is a portal for sharing and integration of human protein data.
The international collaborative work is published in February issue of .
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The 'circuitry' in human cells was
analyzed by IOB scientists and published in .
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: IOB has successfully completed
analysis of the human X chromosome. This work was published
in and recently covered in a story
in
Highlights of this study included discovery of dozens of novel
genes using comparative genomics and experimental validation.
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, a first of its kind database that is
a comprehensive resource for all human plasma proteins along
with their isoforms. This database was featured
on the cover of the journal
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Two of the databases developed
by our Institute, and
were covered by the journal
as databases of special interest. Other news articles have
also appeared which cover ongoing projects in IOB.
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represents a centralized platform to visually depict and integrate
information pertaining to domain architecture, post-translational modifications, interaction networks and disease associations for each protein in the human proteome.
This database has been published in
, and .
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is a database for accessing cancer microarray studies to identify
characteristic cancer signatures. Oncomine was developed jointly
by IOB and the Chinnaiyan Lab at the University of Michigan.
This database has been published in
and .
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Analysis of protein tyrosine phosphatases encoded by the human genome was carried out using computational biology and experimental methods.
The IOB study identified novel members of the tyrosine phosphatases family and novel transcript variants. Experimentally validation was done using RT-PCR and Northern blot analysis.
This study was published in and the analysis is available at
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was carried out collaboratively
by scientists at IOB and at Johns Hopkins University.
These studies were published in , and ,
respectively.
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Protein-Protein interaction data in HPRD is made available
in Proteomics Standards Initiative - Molecular interaction
(PSI- MI) format which defines community standards for data
representation in proteomics and facilitates data comparision,
exchange and verification. The article has
been published in
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