IMR Press / FBL / Volume 12 / Issue 13 / DOI: 10.2741/2449

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Integration of bioinformatics resources for functional analysis of gene expression and proteomic data
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1 Protein Information Resource (PIR), Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, 3300 Whitehaven Street NW, Suite 1200, Washington, DC 20007, USA
Academic Editor:Robert Clarke
Front. Biosci. (Landmark Ed) 2007, 12(13), 5071–5088;
Published: 1 September 2007
(This article belongs to the Special Issue Classification and prediction in genomics and proteomics)

In the post-genome era, researchers are systematically tackling gene functions and complex regulatory processes by studying organisms on a global scale; however, a major challenge lies in the voluminous, complex, and dynamic data being maintained in heterogeneous sources, especially from proteomics experiments. Advanced computational methods are needed for integration, mining, comparative analysis, and functional interpretation of high-throughput proteomic data. In the first part of this review, we discuss aspects of data integration important for capturing all data relevant to functional analysis. We provide a list of databases commonly used in genomics and proteomics and explain strategies to connect the source data, with especial emphasis on our ID mapping service. Next, we describe iProClass, a central data infrastructure that supports both data integration and functional annotation of proteins, and give a brief introduction to the data search/retrieval and analysis tools currently available at our website ( that researchers can use for large-scale functional analysis. In the last part, we introduce iProXpress (integrated Protein eXpression), an integrated research and discovery platform for large-scale expression data analysis, and we show a prototype that has been useful for organelle proteome analysis.

Data Integration
Dependence Network Modeling
Functional Annotation
Functional Profiling
Gene Ontology
Id Mapping
Lysosome-Related Organelle
Mass Spectrometry
Organelle Proteome
Protein Mapping
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