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Clarke DJB, Marino GB, Deng EZ, Xie Z, Evangelista JE, Ma'ayan A. Rummagene: massive mining of gene sets from supporting materials of biomedical research publications. Commun Biol 2024; 7:482. [PMID: 38643247 PMCID: PMC11032387 DOI: 10.1038/s42003-024-06177-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 04/10/2024] [Indexed: 04/22/2024] Open
Abstract
Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .
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Affiliation(s)
- Daniel J B Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Giacomo B Marino
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eden Z Deng
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhuorui Xie
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John Erol Evangelista
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Avi Ma'ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Sarmah D, Smith GR, Bouhaddou M, Stern AD, Erskine J, Birtwistle MR. Network inference from perturbation time course data. NPJ Syst Biol Appl 2022; 8:42. [PMID: 36316338 PMCID: PMC9622863 DOI: 10.1038/s41540-022-00253-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/18/2022] [Indexed: 11/05/2022] Open
Abstract
Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.
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Affiliation(s)
- Deepraj Sarmah
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Gregory R Smith
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mehdi Bouhaddou
- J. David Gladstone Institutes, San Francisco, CA, 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Alan D Stern
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - James Erskine
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
- Department of Bioengineering, Clemson University, Clemson, SC, USA.
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Edwards SL, Mergan L, Parmar B, Cockx B, De Haes W, Temmerman L, Schoofs L. Exploring neuropeptide signalling through proteomics and peptidomics. Expert Rev Proteomics 2018; 16:131-137. [DOI: 10.1080/14789450.2019.1559733] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
| | - Lucas Mergan
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
| | - Bhavesh Parmar
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
| | - Bram Cockx
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
| | - Wouter De Haes
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
| | - Liesbet Temmerman
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
| | - Liliane Schoofs
- Animal Physiology and Neurobiology, Department of Biology, KU Leuven, Leuven, Belgium
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Drew K, Müller CL, Bonneau R, Marcotte EM. Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets. PLoS Comput Biol 2017; 13:e1005625. [PMID: 29023445 PMCID: PMC5638211 DOI: 10.1371/journal.pcbi.1005625] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 06/06/2017] [Indexed: 12/21/2022] Open
Abstract
Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available. Proteins physically associate into complexes in order to carry out the essential functions of life. Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work. In principle, the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex. Here, we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets. Within our predictions, we recover many known protein-protein contacts, serving to validate our method, as well as unknown contacts that can inform future studies of these complexes. Specifically, we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes, two complexes that have incomplete structural information. Using our method, we further provide structural information for a previously missing subunit of the EKC/KEOPS complex. We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes.
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Affiliation(s)
- Kevin Drew
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (KD); (CLM); (EMM)
| | - Christian L. Müller
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America
- * E-mail: (KD); (CLM); (EMM)
| | - Richard Bonneau
- Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, NY, United States of America
- New York University Center for Genomics and Systems Biology, New York University, New York, NY, United States of America
| | - Edward M. Marcotte
- Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, United States of America
- * E-mail: (KD); (CLM); (EMM)
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Capitanio JS, Montpetit B, Wozniak RW. Human Nup98 regulates the localization and activity of DExH/D-box helicase DHX9. eLife 2017; 6. [PMID: 28221134 PMCID: PMC5338925 DOI: 10.7554/elife.18825] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 02/16/2017] [Indexed: 12/17/2022] Open
Abstract
Beyond their role at nuclear pore complexes, some nucleoporins function in the nucleoplasm. One such nucleoporin, Nup98, binds chromatin and regulates gene expression. To gain insight into how Nup98 contributes to this process, we focused on identifying novel binding partners and understanding the significance of these interactions. Here we report on the identification of the DExH/D-box helicase DHX9 as an intranuclear Nup98 binding partner. Various results, including in vitro assays, show that the FG/GLFG region of Nup98 binds to N- and C-terminal regions of DHX9 in an RNA facilitated manner. Importantly, binding of Nup98 stimulates the ATPase activity of DHX9, and a transcriptional reporter assay suggests Nup98 supports DHX9-stimulated transcription. Consistent with these observations, our analysis revealed that Nup98 and DHX9 bind interdependently to similar gene loci and their transcripts. Based on our results, we propose that Nup98 functions as a co-factor that regulates DHX9 and, potentially, other RNA helicases.
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Affiliation(s)
| | - Ben Montpetit
- Department of Cell Biology, University of Alberta, Edmonton, Canada.,Department of Viticulture and Enology, University of California, Davis, United states
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Smits AH, Vermeulen M. Characterizing Protein–Protein Interactions Using Mass Spectrometry: Challenges and Opportunities. Trends Biotechnol 2016; 34:825-834. [DOI: 10.1016/j.tibtech.2016.02.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 02/23/2016] [Accepted: 02/26/2016] [Indexed: 11/28/2022]
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Reprint of “Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction”. Comput Biol Chem 2015; 59 Pt B:123-38. [DOI: 10.1016/j.compbiolchem.2015.08.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 12/21/2022]
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Rouillard AD, Wang Z, Ma’ayan A. Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction. Comput Biol Chem 2015; 58:104-19. [PMID: 26101093 PMCID: PMC4675694 DOI: 10.1016/j.compbiolchem.2015.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 06/04/2015] [Accepted: 06/05/2015] [Indexed: 12/27/2022]
Abstract
With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.
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Affiliation(s)
- Andrew D. Rouillard
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
| | - Zichen Wang
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
| | - Avi Ma’ayan
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, NY 10029
- BD2K-LINCS Data Coordination and Integration Center
- Illuminating the Druggable Genome Knowledge Management Center
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Jing R, Sun J, Wang Y, Li M. Domain position prediction based on sequence information by using fuzzy mean operator. Proteins 2015; 83:1462-9. [PMID: 26009844 DOI: 10.1002/prot.24833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 04/23/2015] [Accepted: 05/17/2015] [Indexed: 11/09/2022]
Abstract
The prediction of protein domain region is an advantageous process on the study of protein structure and function. In this study, we proposed a new method, which is composed of fuzzy mean operator and region division, to predict the particular positions of domains in a target protein based on its sequence. The whole sequence is aligned and scored by using fuzzy mean operator, and the final determination of domain region position is realized by region division. A published benchmark is used for the comparison with previous researches. In addition, we generate two extra datasets to examine the stability of this method. Finally, the prediction accuracy of independent test dataset achieved by our method was up to 84.13%. We wish that this method could be useful for related researches.
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Affiliation(s)
- Runyu Jing
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Jing Sun
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Yuelong Wang
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Menglong Li
- Chemical Information Center (CIC), College of Chemistry, Sichuan University, Chengdu, 610064, China
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10
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Bolouri H. Modeling genomic regulatory networks with big data. Trends Genet 2014; 30:182-91. [PMID: 24630831 DOI: 10.1016/j.tig.2014.02.005] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Revised: 02/18/2014] [Accepted: 02/19/2014] [Indexed: 02/06/2023]
Abstract
High-throughput sequencing, large-scale data generation projects, and web-based cloud computing are changing how computational biology is performed, who performs it, and what biological insights it can deliver. I review here the latest developments in available data, methods, and software, focusing on the modeling and analysis of the gene regulatory interactions in cells. Three key findings are: (i) although sophisticated computational resources are increasingly available to bench biologists, tailored ongoing education is necessary to avoid the erroneous use of these resources. (ii) Current models of the regulation of gene expression are far too simplistic and need updating. (iii) Integrative computational analysis of large-scale datasets is becoming a fundamental component of molecular biology. I discuss current and near-term opportunities and challenges related to these three points.
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Affiliation(s)
- Hamid Bolouri
- Division of Human Biology, Fred Hutchinson Cancer Research Center (FHCRC), 1100 Fairview Avenue North, PO Box 19024, Seattle, WA 98109, USA.
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Woodsmith J, Stelzl U. Studying post-translational modifications with protein interaction networks. Curr Opin Struct Biol 2014; 24:34-44. [DOI: 10.1016/j.sbi.2013.11.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2013] [Revised: 11/15/2013] [Accepted: 11/22/2013] [Indexed: 12/14/2022]
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Mayne J, Starr AE, Ning Z, Chen R, Chiang CK, Figeys D. Fine Tuning of Proteomic Technologies to Improve Biological Findings: Advancements in 2011–2013. Anal Chem 2013; 86:176-95. [DOI: 10.1021/ac403551f] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Janice Mayne
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
| | - Amanda E. Starr
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
| | - Zhibin Ning
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
| | - Rui Chen
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
| | - Cheng-Kang Chiang
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
| | - Daniel Figeys
- Ottawa Institute of
Systems Biology, Department of Biochemistry, Microbiology
and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada K1H8M5
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Kamburov A, Grossmann A, Herwig R, Stelzl U. Cluster-based assessment of protein-protein interaction confidence. BMC Bioinformatics 2012; 13:262. [PMID: 23050565 PMCID: PMC3532186 DOI: 10.1186/1471-2105-13-262] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Accepted: 08/16/2012] [Indexed: 11/10/2022] Open
Abstract
Background Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity. Results We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring. Conclusions On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at
http://intscore.molgen.mpg.de.
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Affiliation(s)
- Atanas Kamburov
- Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195 Berlin, Ihnestr, 63-73, Germany.
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Clark NR, Dannenfelser R, Tan CM, Komosinski ME, Ma'ayan A. Sets2Networks: network inference from repeated observations of sets. BMC SYSTEMS BIOLOGY 2012; 6:89. [PMID: 22824380 PMCID: PMC3443648 DOI: 10.1186/1752-0509-6-89] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2012] [Accepted: 06/25/2012] [Indexed: 11/10/2022]
Abstract
BACKGROUND The skeleton of complex systems can be represented as networks where vertices represent entities, and edges represent the relations between these entities. Often it is impossible, or expensive, to determine the network structure by experimental validation of the binary interactions between every vertex pair. It is usually more practical to infer the network from surrogate observations. Network inference is the process by which an underlying network of relations between entities is determined from indirect evidence. While many algorithms have been developed to infer networks from quantitative data, less attention has been paid to methods which infer networks from repeated co-occurrence of entities in related sets. This type of data is ubiquitous in the field of systems biology and in other areas of complex systems research. Hence, such methods would be of great utility and value. RESULTS Here we present a general method for network inference from repeated observations of sets of related entities. Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data. The frequency of occurrence of a given link throughout this ensemble is interpreted as the probability that the link is present in the underlying real network conditioned on the data. Exponential random graphs are used to generate and sample the ensemble of consistent networks, and we take an algorithmic approach to numerically execute the inference method. The effectiveness of the method is demonstrated on synthetic data before employing this inference approach to problems in systems biology and systems pharmacology, as well as to construct a co-authorship collaboration network. We predict direct protein-protein interactions from high-throughput mass-spectrometry proteomics, integrate data from Chip-seq and loss-of-function/gain-of-function followed by expression data to infer a network of associations between pluripotency regulators, extract a network that connects 53 cancer drugs to each other and to 34 severe adverse events by mining the FDA's Adverse Events Reporting Systems (AERS), and construct a co-authorship network that connects Mount Sinai School of Medicine investigators. The predicted networks and online software to create networks from entity-set libraries are provided online at http://www.maayanlab.net/S2N. CONCLUSIONS The network inference method presented here can be applied to resolve different types of networks in current systems biology and systems pharmacology as well as in other fields of research.
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Affiliation(s)
- Neil R Clark
- Department of Pharmacology and Systems Therapeutics, Systems Biology Center of New York (SBCNY), Mount Sinai School of Medicine, One Gustave L, Levy Place, Box 1215, New York, NY 10029, USA
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