Enabling high-throughput data management for systems biology: The Bioinformatics Resource Manager

Shah, Anuj R.; Singhal, Mudita; Klicker, Kyle R.; Stephan, Eric G.; Wiley, H. Steven; Waters, Katrina M.

doi:10.1093/bioinformatics/btm031

Cited by 39 publications

(26 citation statements)

References 11 publications

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“…Human homologs were identified for 393 of the 400 differentially expressed macaque proteins by using INPARANOID (4, 41) and ScalaBlast (42), comparing identified macaque proteins against the human IPI 2009 protein collection list. For pathway analysis, human IPI protein accession numbers were converted to NCBI Entrez gene identifiers by using BRM (48). Human NCBI Entrez gene identifiers corresponding to 374 of the differentially expressed macaque proteins were imported into the Ingenuity Pathways Analysis (Redwood City, CA) and DAVID (5,20) programs.…”

Section: Macaque Viral Infectionsmentioning

confidence: 99%

Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections

Brown

Palermo

Baskin

et al. 2010

J Virol

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The host proteome response and molecular mechanisms that drive disease in vivo during infection by a human isolate of the highly pathogenic avian influenza virus (HPAI) and 1918 pandemic influenza virus remain poorly understood. This study presents a comprehensive characterization of the proteome response in cynomolgus macaque (Macaca fascicularis) lung tissue over 7 days of infection with HPAI (the most virulent), a reassortant virus containing 1918 hemagglutinin and neuraminidase surface proteins (intermediate virulence), or a human seasonal strain (least virulent). A high-sensitivity two-dimensional liquid chromatographytandem mass spectroscopy strategy and functional network analysis were implemented to gain insight into response pathways activated in macaques during influenza virus infection. A macaque protein database was assembled and used in the identification of 35,239 unique peptide sequences corresponding to approximately 4,259 proteins. Quantitative analysis identified an increase in expression of 400 proteins during viral infection. The abundance levels of a subset of these 400 proteins produced strong correlations with disease progression observed in the macaques, distinguishing a "core" response to viral infection from a "high" response specific to severe disease. Proteome expression profiles revealed distinct temporal response kinetics between viral strains, with HPAI inducing the most rapid response. While proteins involved in the immune response, metabolism, and transport were increased rapidly in the lung by HPAI, the other viruses produced a delayed response, characterized by an increase in proteins involved in oxidative phosphorylation, RNA processing, and translation. Proteomic results were integrated with previous genomic and pathological analysis to characterize the dynamic nature of the influenza virus infection process.

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Section: Macaque Viral Infectionsmentioning

confidence: 99%

Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections

Brown

Palermo

Baskin

et al. 2010

J Virol

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“…To allow experimenters to combine several individual tools, programs such as the Bioinformatic Resource Manager (27) and Prequips (28), which both use the program Gaggle (29) for data transfer, provide multifunctional platforms for data analysis. The Gaggle-based integrated solutions are powerful but particularly the divergent interfaces users are confronted with, might be challenging for nonspecialists.…”

mentioning

confidence: 99%

GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

Rigbolt

Vanselow

Blagoev

2011

Molecular & Cellular Proteomics

136

127

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Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)1 . The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. During the last decade, identification and quantitation of proteomes has been facilitated by the constant developments in mass spectrometry instrumentation, fractionation techniques, quantitation-strategies, and data analysis software. Using state-of-the-art technology it has become possible to quantify several thousands of proteins (1-10), and even complete proteomes within a single proteomics experiment (11, 12). Powerful software solutions for protein identification and quantitation have been developed that allow users to process the information stored in the raw mass spectrometry data. These software solutions have been developed by both the scientific community (13-16) and by instrument vendors, exemplified by PEAKS (Bioinformatics Solutions) and Proteome Discoverer (Thermo Scientific). In face of these advances in the field, we find that data analysis is currently the bottleneck of proteomics experiments. Familiarity with several advanced bioinformatics tools, and preferably programming skills, are nowadays essential to perform a comprehensive analysis of large proteomics data sets (17). So far, experimenters without familiarity with computer programming have typically been required to use spreadsheet applications that are not per se developed for analysis of biological data and are therefore of limited use for working with the large amount of data produced from modern proteomics experiments. Alternatively a number of software s...

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“…For pathway analysis, human Internation Protein Index protein accession numbers were converted to NCBI Entrez gene identifiers using the Bioinformatics Resource Manager (47). Human NCBI Entrez gene identifiers corresponding to differentially expressed macaque proteins were imported into DAVID (48,49), and the Kyoto Encyclopedia of Genes and Genomes was used to identify significantly altered functional pathways (50 -52).…”

Section: Lc-ms(/ms) Proteomementioning

confidence: 99%

Characterization of Macaque Pulmonary Fluid Proteome during Monkeypox Infection

Brown

Estep

López-Ferrer

et al. 2010

Molecular & Cellular Proteomics

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Enabling high-throughput data management for systems biology: The Bioinformatics Resource Manager

Abstract: BRM, sample data sets and a user manual can be downloaded from http://www.sysbio.org/dataresources/brm.stm.

Cited by 39 publications

References 11 publications

Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections

Macaque Proteome Response to Highly Pathogenic Avian Influenza and 1918 Reassortant Influenza Virus Infections

GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

Characterization of Macaque Pulmonary Fluid Proteome during Monkeypox Infection

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