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PaxDb, a database of protein abundance averages across all three domains of life


Wang, Mingcong; Weiss, Manuel; Simonovic, Milan; Haertinger, Gabriele; Schrimpf, Sabine P; Hengartner, Michael O; von Mering, Christian (2012). PaxDb, a database of protein abundance averages across all three domains of life. Molecular & Cellular Proteomics, 11(8):492-500.

Abstract

Although protein expression is regulated both temporally and spatially, most proteins have an intrinsic, typical range of functionally effective abundance levels. These extend from a few molecules per cell for signaling proteins, to millions of molecules for structural proteins. When addressing fundamental questions related to protein evolution, translation and folding, but also in routine laboratory work, a simple rough estimate of the average wild type abundance of each detectable protein in an organism is often desirable. Here, we introduce a meta-resource dedicated to integrating information on absolute protein abundance levels; we place particular emphasis on deep coverage, consistent post-processing and comparability across different organisms. Publicly available experimental data are mapped onto a common namespace and, in the case of tandem mass spectrometry data, re-processed using a standardized spectral counting pipeline. By aggregating and averaging over the various samples, conditions and cell-types, the resulting integrated dataset achieves increased coverage and a high dynamic range. We score and rank each contributing, individual dataset by assessing its consistency against externally provided protein-network information, and demonstrate that our weighted integration exhibits more consistency than the datasets individually.The current PaxDb-release 2.1 (at http://pax-db.org/) presents whole-organism data as well as tissue-resolved data, and covers 85000 proteins in 12 model organisms. All values can be seamlessly compared across organisms via pre-computed orthology relationships.

Abstract

Although protein expression is regulated both temporally and spatially, most proteins have an intrinsic, typical range of functionally effective abundance levels. These extend from a few molecules per cell for signaling proteins, to millions of molecules for structural proteins. When addressing fundamental questions related to protein evolution, translation and folding, but also in routine laboratory work, a simple rough estimate of the average wild type abundance of each detectable protein in an organism is often desirable. Here, we introduce a meta-resource dedicated to integrating information on absolute protein abundance levels; we place particular emphasis on deep coverage, consistent post-processing and comparability across different organisms. Publicly available experimental data are mapped onto a common namespace and, in the case of tandem mass spectrometry data, re-processed using a standardized spectral counting pipeline. By aggregating and averaging over the various samples, conditions and cell-types, the resulting integrated dataset achieves increased coverage and a high dynamic range. We score and rank each contributing, individual dataset by assessing its consistency against externally provided protein-network information, and demonstrate that our weighted integration exhibits more consistency than the datasets individually.The current PaxDb-release 2.1 (at http://pax-db.org/) presents whole-organism data as well as tissue-resolved data, and covers 85000 proteins in 12 model organisms. All values can be seamlessly compared across organisms via pre-computed orthology relationships.

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148 citations in Web of Science®
154 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > PhosphoNetX
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:24 April 2012
Deposited On:04 Jul 2012 07:35
Last Modified:05 Apr 2016 15:51
Publisher:American Society for Biochemistry and Molecular Biology
ISSN:1535-9476
Publisher DOI:https://doi.org/10.1074/mcp.O111.014704
PubMed ID:22535208

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