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The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

D. Szklarczyk, A. Franceschini, M. Kuhn, M. Simonovic, A. Roth, P. Minguez, T. Doerks, M. Stark, J. Muller, P. Bork, L. J. Jensen, C. v. Mering
2010 Nucleic Acids Research  
ACKNOWLEDGEMENTS The authors wish to thank the PSICQUIC consortium for early access to their standardization effort, and Dr Gary Bader for technical help with the Cytoscape plugin.  ...  Upon querying the database with four yeast proteins, the resource first reports a raw network consisting of the highest scoring interaction partners (upper left corner).  ...  The main strengths of STRING lie in its unique comprehensiveness, its confidence scoring and its interactive and intuitive user interface.  ... 
doi:10.1093/nar/gkq973 pmid:21045058 pmcid:PMC3013807 fatcat:yxw3fm3dazgmjcyefq3x74dac4

Phospho.ELM: a database of phosphorylation sites--update 2011

H. Dinkel, C. Chica, A. Via, C. M. Gould, L. J. Jensen, T. J. Gibson, F. Diella
2010 Nucleic Acids Research  
Finally, special emphasis has been put on linking to external resources such as interaction networks and other databases.  ...  The Phospho.ELM resource (http://phospho.elm.eu .org) is a relational database designed to store in vivo and in vitro phosphorylation data extracted from the scientific literature and phosphoproteomic  ...  We are grateful to Norman Davey and Kim Van Roey for critical reading of the article. Conflict of interest statement. None declared.  ... 
doi:10.1093/nar/gkq1104 pmid:21062810 pmcid:PMC3013696 fatcat:erspd7fnbfa2dolurfj4v2uzje

The BioGRID Interaction Database: 2011 update

C. Stark, B.-J. Breitkreutz, A. Chatr-aryamontri, L. Boucher, R. Oughtred, M. S. Livstone, J. Nixon, K. Van Auken, X. Wang, X. Shi, T. Reguly, J. M. Rust (+3 others)
2010 Nucleic Acids Research  
The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org  ...  BioGRID currently holds 347 966 interactions (170 162 genetic, 177 804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23 000 publications in the  ...  Judy Blake for support and helpful discussions.  ... 
doi:10.1093/nar/gkq1116 pmid:21071413 pmcid:PMC3013707 fatcat:5e7qa7hecfbnfj7o3dvxiw4yum

Generation and Analysis of Large-Scale Data-DrivenMycobacterium tuberculosisFunctional Networks for Drug Target Identification

Gaston K. Mazandu, Nicola J. Mulder
2011 Advances in Bioinformatics  
As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens.  ...  Here we have integrated functional genomics data to generate functional interaction networks betweenMycobacterium tuberculosisproteins and carried out computational analyses to dissect the functional interaction  ...  Many thanks to the authors of the freely available libraries for making this work possible. This work has been supported by the National Bioinformatics Network (NBN) in South  ... 
doi:10.1155/2011/801478 pmid:22190924 pmcid:PMC3235424 fatcat:hbo3epi4uzfvpoq6cy66jayqeq

Scoring Protein Relationships in Functional Interaction Networks Predicted from Sequence Data

Gaston K. Mazandu, Nicola J. Mulder, Christophe Herman
2011 PLoS ONE  
We use the network for predicting functions of uncharacterised proteins.  ...  In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches  ...  Many thanks to the authors of the freely available libraries for making this work possible. Author Contributions  ... 
doi:10.1371/journal.pone.0018607 pmid:21526183 pmcid:PMC3079720 fatcat:vr3oyjqutjcwfdjxirga3kwu2a

Network-based methods for human disease gene prediction

X. Wang, N. Gulbahce, H. Yu
2011 Briefings in Functional Genomics  
The majority of the methods are based on protein interactome networks, with integration of other large-scale genomic data or disease phenotype information, to infer how likely it is that a gene is associated  ...  Genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.  ...  Acknowledgements We thank Siu Sylvia Lee and Elliot John Kahen for their valuable comments on the manuscript.  ... 
doi:10.1093/bfgp/elr024 pmid:21764832 fatcat:gj426e72krbcze4pjdhdiqe55i

MicroRNA Expression Analysis: Clinical Advantage of Propranolol Reveals Key MicroRNAs in Myocardial Infarction

Wenliang Zhu, Lei Yang, Hongli Shan, Yong Zhang, Rui Zhou, Zhe Su, Zhimin Du, Oliver Hofmann
2011 PLoS ONE  
Conclusion: Our study illustrates how a combination of microarray technology and functional protein network analysis can be used to identify disease-related key miRNAs.  ...  Our network analysis identified that, among these miRNAs, the prime players in MI were miR-1, miR-29b and miR-98.  ...  In the present study, an innovative integrative analysis of protein-protein interaction and miRNA expression data was undertaken to search for the miRNAs that might be key regulators of MI, a common cause  ... 
doi:10.1371/journal.pone.0014736 pmid:21386882 pmcid:PMC3046111 fatcat:2ybb6zt7sbhl3lvmybo5aherse

Landscape Mapping of Functional Proteins in Insulin Signal Transduction and Insulin Resistance: A Network-Based Protein-Protein Interaction Analysis

Chiranjib Chakraborty, Sanjiban S. Roy, Minna J. Hsu, Govindasamy Agoramoorthy, Timothy Ravasi
2011 PLoS ONE  
We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network.  ...  The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4.  ...  Acknowledgments We would like to thank the senior management of VIT University (Vellore, India) for their kind support and encouragement towards our research work. Author Contributions  ... 
doi:10.1371/journal.pone.0016388 pmid:21305025 pmcid:PMC3031563 fatcat:yqpru6ss4fcglfqjcp2n4do5ae

Ranking Candidate Disease Genes from Gene Expression and Protein Interaction: A Katz-Centrality Based Approach

Jing Zhao, Ting-Hong Yang, Yongxu Huang, Petter Holme, Matjaz Perc
2011 PLoS ONE  
other disease-gene candidates in its protein interaction network.  ...  In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes.  ...  Our protein interaction network was constructed from the STRING database, which includes both physical and functional interactions integrated from numerous sources, including experimental repositories,  ... 
doi:10.1371/journal.pone.0024306 pmid:21912686 pmcid:PMC3166320 fatcat:eik2b4nqnfhi5crnamnv7tohxe

Building Protein-Protein Interaction Networks with Proteomics and Informatics Tools

Mihaela E. Sardiu, Michael P. Washburn
2011 Journal of Biological Chemistry  
In this minireview, we survey the most common methods for the systematic identification of protein interactions and exemplify different strategies for the generation of protein interaction networks.  ...  In this minireview, we will survey the major experimental methods applied for the systematic analysis of protein interactions and examine the alternative approaches to deduce protein interaction networks  ...  In database mining, protein interaction databases can use indirect evidence such as functional associations or co-appearance in the same MEDLINE abstract to build networks.  ... 
doi:10.1074/jbc.r110.174052 pmid:21566121 pmcid:PMC3129144 fatcat:k244duhdzbcwjcgzschwl3diyq

Network-based function prediction and interactomics: The case for metabolic enzymes

S.C. Janga, J. Javier Díaz-Mejía, G. Moreno-Hagelsieb
2011 Metabolic Engineering  
Most recent network-based approaches aim to integrate diverse kinds of functional interactions to boost both coverage and confidence level.  ...  In this article we review the state of the art in network-based function prediction and describe some of the underlying difficulties and successes.  ...  GM-H acknowledges research support from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Institutes of Health Research (CIHR) and computational facilities from the  ... 
doi:10.1016/j.ymben.2010.07.001 pmid:20654726 fatcat:yn3ksrj2ore2rlmh34rvzbqeju

GPS-Prot: A web-based visualization platform for integrating host-pathogen interaction data

Marie E Fahey, Melanie J Bennett, Cathal Mahon, Stefanie Jäger, Lars Pache, Dhiraj Kumar, Alex Shapiro, Kanury Rao, Sumit K Chanda, Charles S Craik, Alan D Frankel, Nevan J Krogan
2011 BMC Bioinformatics  
The software has the ability to group proteins into functional modules or protein complexes, generating more intuitive network representations and also allows for the uploading of user-generated data.  ...  Because these host-pathogen interactions are extensive and interactions between human proteins are found within many different databases, it is difficult to generate integrated HIV-human interaction networks  ...  Acknowledgements and Funding Vif DNA was obtained through the NIH AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH from Dr. Stephan Bour and Dr.  ... 
doi:10.1186/1471-2105-12-298 pmid:21777475 pmcid:PMC3213248 fatcat:cx2my7mg2jgcpiidepzj7u4a2y

Large-scaleDe NovoPrediction of Physical Protein-Protein Association

Antigoni Elefsinioti, Ömer Sinan Saraç, Anna Hegele, Conrad Plake, Nina C. Hubner, Ina Poser, Mihail Sarov, Anthony Hyman, Matthias Mann, Michael Schroeder, Ulrich Stelzl, Andreas Beyer
2011 Molecular & Cellular Proteomics  
Thus we identified 462 new protein-protein interactions and confirmed the predictive power of the network.  ...  The network provides a high-quality resource for the analysis of genomic data sets and genetic association studies in particular.  ...  We recalculated the STRING combined score after eliminating the experimental and database features in order to exclude any experimental evidence.  ... 
doi:10.1074/mcp.m111.010629 pmid:21836163 pmcid:PMC3226409 fatcat:d2ukaphdmvgpjndtadkesgwioa

Protein function prediction: towards integration of similarity metrics

Serkan Erdin, Andreas Martin Lisewski, Olivier Lichtarge
2011 Current Opinion in Structural Biology  
Although high sensitivity is elusive, network analyses that integrate these metrics together hold the promise of rapid gains in function prediction specificity.  ...  To fill this gap, function prediction algorithms apply metrics of similarity between proteins on the premise that those sufficiently alike in sequence, or structure, will perform identical functions.  ...  Acknowledgements We thank Eric Venner for contributing Figure 2 and gratefully acknowledge grant support from the National Institute of Health, NIH GM079656 and GM066099, and from the National Science  ... 
doi:10.1016/j.sbi.2011.02.001 pmid:21353529 pmcid:PMC3120633 fatcat:2y5fgugnyza25b3qm2jnd76lwa

Comparative interactomics with Funcoup 2.0

A. Alexeyenko, T. Schmitt, A. Tjarnberg, D. Guala, O. Frings, E. L. L. Sonnhammer
2011 Nucleic Acids Research  
FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput  ...  As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks.  ...  Thus, the score increases with (i) the number of individual published reports on the interaction between proteins A and B and (ii) the number of separate experiments that validated interaction between  ... 
doi:10.1093/nar/gkr1062 pmid:22110034 pmcid:PMC3245127 fatcat:tch6cfwsq5bxrfebbz5jlhzava
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