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BMC Med Genomics. 2008 Apr 25;1:12. doi: 10.1186/1755-8794-1-12.

HIP2: an online database of human plasma proteins from healthy individuals.

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1
School of Informatics, Indiana University - Purdue University, Indianapolis, USA. sahas@iupui.edu

Abstract

BACKGROUND:

With the introduction of increasingly powerful mass spectrometry (MS) techniques for clinical research, several recent large-scale MS proteomics studies have sought to characterize the entire human plasma proteome with a general objective for identifying thousands of proteins leaked from tissues in the circulating blood. Understanding the basic constituents, diversity, and variability of the human plasma proteome is essential to the development of sensitive molecular diagnosis and treatment monitoring solutions for future biomedical applications. Biomedical researchers today, however, do not have an integrated online resource in which they can search for plasma proteins collected from different mass spectrometry platforms, experimental protocols, and search software for healthy individuals. The lack of such a resource for comparisons has made it difficult to interpret proteomics profile changes in patients' plasma and to design protein biomarker discovery experiments.

DESCRIPTION:

To aid future protein biomarker studies of disease and health from human plasma, we developed an online database, HIP2 (Healthy Human Individual's Integrated Plasma Proteome). The current version contains 12,787 protein entries linked to 86,831 peptide entries identified using different MS platforms.

CONCLUSION:

This web-based database will be useful to biomedical researchers involved in biomarker discovery research. This database has been developed to be the comprehensive collection of healthy human plasma proteins, and has protein data captured in a relational database schema built to contain mappings of supporting peptide evidence from several high-quality and high-throughput mass-spectrometry (MS) experimental data sets. Users can search for plasma protein/peptide annotations, peptide/protein alignments, and experimental/sample conditions with options for filter-based retrieval to achieve greater analytical power for discovery and validation.

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