1
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Köse TB, Li J, Ritz A. Growing Directed Acyclic Graphs: Optimization Functions for Pathway Reconstruction Algorithms. J Comput Biol 2023. [PMID: 36862510 DOI: 10.1089/cmb.2022.0376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
A major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existing k-shortest paths method for pathway reconstruction, and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step toward reconstructing pathways that provably optimize a specific cost function.
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Affiliation(s)
- Tunç Başar Köse
- Department of Computer Science and Reed College, Portland, Oregon, USA
| | - Jiarong Li
- Department of Computer Science and Reed College, Portland, Oregon, USA
| | - Anna Ritz
- Department of Biology, Reed College, Portland, Oregon, USA
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2
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Identification of key somatic oncogenic mutation based on a confounder-free causal inference model. PLoS Comput Biol 2022; 18:e1010529. [PMID: 36137089 PMCID: PMC9499235 DOI: 10.1371/journal.pcbi.1010529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 08/31/2022] [Indexed: 11/30/2022] Open
Abstract
Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations. Identifying key mutations of cancers is helpful to better understand the mechanisms of cancer cell transformation and is critical for therapeutic approaches. Besides sequence and structure-based computational approaches, some functional impact-based methods which consider the association between mutation events and the activity of cancer-related biological processes have also been developed to detect key mutations. However, these methods mainly consider the correlation but ignore that the correlation is far from causality due to the existence of observed and unobserved confounding factors. We develop a confounder-free machine learning-based causal inference framework to estimate the causal effect of mutations on abnormal cell proliferation and epithelial-mesenchymal transition (EMT). It fills a gap in the use of causal mechanisms to discover potential driver mutations in cancer biological systems. Applying our method to 10 cancer types, the identified key mutations are highly consistent with public well-verified ones. Additionally, some new key mutations have also been discovered.
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3
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Chen Z, Lu Y, Cao B, Zhang W, Edwards A, Zhang K. Driver gene detection through Bayesian network integration of mutation and expression profiles. Bioinformatics 2022; 38:2781-2790. [PMID: 35561191 PMCID: PMC9113331 DOI: 10.1093/bioinformatics/btac203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 03/12/2022] [Accepted: 04/06/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. RESULTS Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation. AVAILABILITY AND IMPLEMENTATION The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhong Chen
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - You Lu
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Bo Cao
- Division of Basic and Pharmaceutical Sciences, College of Pharmacy, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Wensheng Zhang
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
- Bioinformatics Core of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Andrea Edwards
- Department of Computer Science, Xavier University of Louisiana, New Orleans, LA 70125, USA
| | - Kun Zhang
- To whom correspondence should be addressed
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4
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Functional stratification of cancer drugs through integrated network similarity. NPJ Syst Biol Appl 2022; 8:11. [PMID: 35440787 PMCID: PMC9018743 DOI: 10.1038/s41540-022-00219-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 01/21/2022] [Indexed: 11/30/2022] Open
Abstract
Drugs not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored networks modulated by several drugs across multiple cancer cell lines by integrating their targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing a similar mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation and found literature evidence for a set of drug pairs. Overall, network-level exploration of drug-modulated pathways and their deep comparison may potentially help optimize treatment strategies and suggest new drug combinations.
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5
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Nudelman I, Kudrin D, Nudelman G, Deshpande R, Hartmann BM, Kleinstein SH, Myers CL, Sealfon SC, Zaslavsky E. Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses. Front Immunol 2021; 12:691758. [PMID: 34335598 PMCID: PMC8317020 DOI: 10.3389/fimmu.2021.691758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.
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Affiliation(s)
- Irina Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Division of General Internal Medicine, New York University Langone Medical Centre, New York, NY, United States
| | - Daniil Kudrin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Raamesh Deshpande
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Boris M Hartmann
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Steven H Kleinstein
- Department of Pathology, Yale University School of Medicine, New Haven, CT, United States
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, United States.,Program in Biomedical Informatics and Computational Biology, University of Minnesota - Twin Cities, Minneapolis, MN, United States
| | - Stuart C Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Advanced Research on Diagnostic Assays (CARDA), Icahn School of Medicine at Mount Sinai, New York, NY, United States
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6
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence. Sci Rep 2021; 11:385. [PMID: 33432018 PMCID: PMC7801429 DOI: 10.1038/s41598-020-79603-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/18/2020] [Indexed: 11/09/2022] Open
Abstract
Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .
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Affiliation(s)
- Xi Chen
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA ,grid.430264.7Center for Computational Biology, Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York, NY 10010 USA
| | - Jinghua Gu
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA
| | - Andrew F. Neuwald
- grid.411024.20000 0001 2175 4264Institute for Genome Sciences and Department Biochemistry and Molecular Biology, University of Maryland School of Medicine, 670 W. Baltimore Street, Baltimore, MD 21201 USA
| | - Leena Hilakivi-Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Robert Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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7
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Rubanova N, Pinna G, Kropp J, Campalans A, Radicella JP, Polesskaya A, Harel-Bellan A, Morozova N. MasterPATH: network analysis of functional genomics screening data. BMC Genomics 2020; 21:632. [PMID: 32928103 PMCID: PMC7491077 DOI: 10.1186/s12864-020-07047-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 09/01/2020] [Indexed: 12/18/2022] Open
Abstract
Background Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks. Results Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks – HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers – the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition. Conclusion The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.
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Affiliation(s)
- Natalia Rubanova
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France. .,Université Paris Diderot, Paris, France. .,Skolkovo Institute of Science and Technology, Skolkovo, Russia.
| | - Guillaume Pinna
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
| | - Jeremie Kropp
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France
| | - Anna Campalans
- Institute of Molecular and Cellular Radiobiology, Institut François Jacob, CEA, F-92265, Fontenay-aux-Roses, France.,INSERM, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France.,Université Paris Sud, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France
| | - Juan Pablo Radicella
- Institute of Molecular and Cellular Radiobiology, Institut François Jacob, CEA, F-92265, Fontenay-aux-Roses, France.,INSERM, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France.,Université Paris Sud, U967, bâtiment 56 PC 103 18 route du Panorama, BP6 92265, Fontenay-aux-Roses Cedex, France
| | - Anna Polesskaya
- Ecole Polytechnique, Université Paris-Saclay, CNRS UMR 7654, Laboratoire de Biochimie, Ecole Polytechnique, 91128, Palaiseau, France
| | - Annick Harel-Bellan
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France
| | - Nadya Morozova
- Institut des Hautes Etudes Scientifiques, Le Bois-Marie 35 rte de Chartres, 91440, Bures-sur-Yvette, France.,Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91198, Gif-sur-Yvette cedex, France
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8
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Jiang Y, Liang Y, Wang D, Xu D, Joshi T. A dynamic programing approach to integrate gene expression data and network information for pathway model generation. Bioinformatics 2020; 36:169-176. [PMID: 31168616 DOI: 10.1093/bioinformatics/btz467] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 05/15/2019] [Accepted: 05/31/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. RESULTS To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY AND IMPLEMENTATION IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuexu Jiang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA
| | - Yanchun Liang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Duolin Wang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA.,Informatics Institute and Christopher S. Bond Life Sciences Center, Columbia, MO 65211, USA
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA.,Informatics Institute and Christopher S. Bond Life Sciences Center, Columbia, MO 65211, USA.,Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
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9
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Basha O, Mauer O, Simonovsky E, Shpringer R, Yeger-Lotem E. ResponseNet v.3: revealing signaling and regulatory pathways connecting your proteins and genes across human tissues. Nucleic Acids Res 2020; 47:W242-W247. [PMID: 31114913 PMCID: PMC6602570 DOI: 10.1093/nar/gkz421] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/23/2019] [Accepted: 05/06/2019] [Indexed: 12/13/2022] Open
Abstract
ResponseNet v.3 is an enhanced version of ResponseNet, a web server that is designed to highlight signaling and regulatory pathways connecting user-defined proteins and genes by using the ResponseNet network optimization approach (http://netbio.bgu.ac.il/respnet). Users run ResponseNet by defining source and target sets of proteins, genes and/or microRNAs, and by specifying a molecular interaction network (interactome). The output of ResponseNet is a sparse, high-probability interactome subnetwork that connects the two sets, thereby revealing additional molecules and interactions that are involved in the studied condition. In recent years, massive efforts were invested in profiling the transcriptomes of human tissues, enabling the inference of human tissue interactomes. ResponseNet v.3 expands ResponseNet2.0 by harnessing ∼11,600 RNA-sequenced human tissue profiles made available by the Genotype-Tissue Expression consortium, to support context-specific analysis of 44 human tissues. Thus, ResponseNet v.3 allows users to illuminate the signaling and regulatory pathways potentially active in the context of a specific tissue, and to compare them with active pathways in other tissues. In the era of precision medicine, such analyses open the door for tissue- and patient-specific analyses of pathways and diseases.
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Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Omry Mauer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Eyal Simonovsky
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Rotem Shpringer
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry & Pharmacology, Faculty of Health Sciences.,National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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10
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. BICORN: An R package for integrative inference of de novo cis-regulatory modules. Sci Rep 2020; 10:7960. [PMID: 32409786 PMCID: PMC7224214 DOI: 10.1038/s41598-020-63043-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA
| | - Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX, 75204, USA
| | - Andrew F Neuwald
- Institute for Genome Sciences and Department Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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11
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Chiang S, Shinohara H, Huang JH, Tsai HK, Okada M. Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach. FEBS Lett 2020; 594:1477-1496. [PMID: 32052437 DOI: 10.1002/1873-3468.13757] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 12/26/2019] [Accepted: 01/20/2020] [Indexed: 11/10/2022]
Abstract
Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.
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Affiliation(s)
- Sufeng Chiang
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | - Jia-Hsin Huang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Huai-Kuang Tsai
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Mariko Okada
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Cell Systems, Institute for Protein Research, Osaka University, Suita, Japan
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12
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Bersanelli M, Mosca E, Milanesi L, Bazzani A, Castellani G. Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach. Sci Rep 2020; 10:2643. [PMID: 32060296 PMCID: PMC7021762 DOI: 10.1038/s41598-020-59036-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/22/2019] [Indexed: 11/13/2022] Open
Abstract
In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.
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Affiliation(s)
- Matteo Bersanelli
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy. .,National Institute for Nuclear Physics (INFN), Bologna, 40127, Italy.
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Segrate, Milan, 20090, Italy
| | - Armando Bazzani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
| | - Gastone Castellani
- Department of Physics and Astronomy, University of Bologna, Bologna, 40127, Italy
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13
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Lam I, Hallacli E, Khurana V. Proteome-Scale Mapping of Perturbed Proteostasis in Living Cells. Cold Spring Harb Perspect Biol 2020; 12:cshperspect.a034124. [PMID: 30910772 DOI: 10.1101/cshperspect.a034124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Proteinopathies are degenerative diseases in which specific proteins adopt deleterious conformations, leading to the dysfunction and demise of distinct cell types. They comprise some of the most significant diseases of aging-from Alzheimer's disease to Parkinson's disease to type 2 diabetes-for which not a single disease-modifying or preventative strategy exists. Here, we survey approaches in tractable cellular and organismal models that bring us toward a more complete understanding of the molecular consequences of protein misfolding. These include proteome-scale profiling of genetic modifiers, as well as transcriptional and proteome changes. We describe assays that can capture protein interactomes in situ and distinct protein conformational states. A picture of cellular drivers and responders to proteotoxicity emerges from this work, distinguishing general alterations of proteostasis from cellular events that are deeply tied to the intrinsic function of the misfolding protein. These distinctions have consequences for the understanding and treatment of proteinopathies.
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Affiliation(s)
- Isabel Lam
- Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115
| | - Erinc Hallacli
- Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115
| | - Vikram Khurana
- Ann Romney Center for Neurologic Disease, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.,Harvard Stem Cell Institute, Cambridge, Massachusetts 02138.,New York Stem Cell Foundation - Robertson Investigator
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14
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IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation. Methods 2020; 173:16-23. [DOI: 10.1016/j.ymeth.2019.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 06/08/2019] [Accepted: 06/13/2019] [Indexed: 01/18/2023] Open
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15
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Jeggari A, Alekseenko Z, Petrov I, Dias JM, Ericson J, Alexeyenko A. EviNet: a web platform for network enrichment analysis with flexible definition of gene sets. Nucleic Acids Res 2019; 46:W163-W170. [PMID: 29893885 PMCID: PMC6030852 DOI: 10.1093/nar/gky485] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2018] [Accepted: 05/29/2018] [Indexed: 12/18/2022] Open
Abstract
The new web resource EviNet provides an easily run interface to network enrichment analysis for exploration of novel, experimentally defined gene sets. The major advantages of this analysis are (i) applicability to any genes found in the global network rather than only to those with pathway/ontology term annotations, (ii) ability to connect genes via different molecular mechanisms rather than within one high-throughput platform, and (iii) statistical power sufficient to detect enrichment of very small sets, down to individual genes. The users’ gene sets are either defined prior to upload or derived interactively from an uploaded file by differential expression criteria. The pathways and networks used in the analysis can be chosen from the collection menu. The calculation is typically done within seconds or minutes and the stable URL is provided immediately. The results are presented in both visual (network graphs) and tabular formats using jQuery libraries. Uploaded data and analysis results are kept in separated project directories not accessible by other users. EviNet is available at https://www.evinet.org/.
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Affiliation(s)
- Ashwini Jeggari
- Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Zhanna Alekseenko
- Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Iurii Petrov
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden
| | - José M Dias
- Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Johan Ericson
- Department of Cell and Molecular Biology, Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Andrey Alexeyenko
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden.,National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Box 1031, 171 21 Solna, Sweden
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16
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Iacovella MG, Bremang M, Basha O, Giacò L, Carotenuto W, Golfieri C, Szakal B, Dal Maschio M, Infantino V, Beznoussenko GV, Joseph CR, Visintin C, Mironov AA, Visintin R, Branzei D, Ferreira-Cerca S, Yeger-Lotem E, De Wulf P. Integrating Rio1 activities discloses its nutrient-activated network in Saccharomyces cerevisiae. Nucleic Acids Res 2019; 46:7586-7611. [PMID: 30011030 PMCID: PMC6125641 DOI: 10.1093/nar/gky618] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 06/28/2018] [Indexed: 12/14/2022] Open
Abstract
The Saccharomyces cerevisiae kinase/adenosine triphosphatase Rio1 regulates rDNA transcription and segregation, pre-rRNA processing and small ribosomal subunit maturation. Other roles are unknown. When overexpressed, human ortholog RIOK1 drives tumor growth and metastasis. Likewise, RIOK1 promotes 40S ribosomal subunit biogenesis and has not been characterized globally. We show that Rio1 manages directly and via a series of regulators, an essential signaling network at the protein, chromatin and RNA levels. Rio1 orchestrates growth and division depending on resource availability, in parallel to the nutrient-activated Tor1 kinase. To define the Rio1 network, we identified its physical interactors, profiled its target genes/transcripts, mapped its chromatin-binding sites and integrated our data with yeast’s protein–protein and protein–DNA interaction catalogs using network computation. We experimentally confirmed network components and localized Rio1 also to mitochondria and vacuoles. Via its network, Rio1 commands protein synthesis (ribosomal gene expression, assembly and activity) and turnover (26S proteasome expression), and impinges on metabolic, energy-production and cell-cycle programs. We find that Rio1 activity is conserved to humans and propose that pathological RIOK1 may fuel promiscuous transcription, ribosome production, chromosomal instability, unrestrained metabolism and proliferation; established contributors to cancer. Our study will advance the understanding of numerous processes, here revealed to depend on Rio1 activity.
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Affiliation(s)
- Maria G Iacovella
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Michael Bremang
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy.,Current address: Proteome Sciences Plc, Hamilton House, Mabledon Place, London, United Kingdom
| | - Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, POB 653, Beer-Sheva 84105, Israel
| | - Luciano Giacò
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Walter Carotenuto
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy
| | - Cristina Golfieri
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Barnabas Szakal
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy
| | - Marianna Dal Maschio
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Valentina Infantino
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Galina V Beznoussenko
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy
| | - Chinnu R Joseph
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy
| | - Clara Visintin
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Alexander A Mironov
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy
| | - Rosella Visintin
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy
| | - Dana Branzei
- The FIRC Institute of Molecular Oncology (IFOM), Via Adamello 16, 20139 Milan, Italy.,Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (CNR), Via Abbiategrasso 207, 27100 Pavia, Italy
| | - Sébastien Ferreira-Cerca
- Lehrstuhl für Biochemie III, Universität Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences and the National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, POB 653, Beer-Sheva 84105, Israel
| | - Peter De Wulf
- Department of Experimental Oncology, European Institute of Oncology, Via Adamello 16, 20139 Milan, Italy.,Centre for Integrative Biology (CIBIO), University of Trento, Via Sommarive 9, 38123 Trento, Italy
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17
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Shafi A, Nguyen T, Peyvandipour A, Nguyen H, Draghici S. A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures. Front Genet 2019; 10:159. [PMID: 30941158 PMCID: PMC6434849 DOI: 10.3389/fgene.2019.00159] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 02/14/2019] [Indexed: 12/20/2022] Open
Abstract
Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.
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Affiliation(s)
- Adib Shafi
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Azam Peyvandipour
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States.,Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United States
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18
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Nguyen H, Shrestha S, Tran D, Shafi A, Draghici S, Nguyen T. A Comprehensive Survey of Tools and Software for Active Subnetwork Identification. Front Genet 2019; 10:155. [PMID: 30891064 PMCID: PMC6411791 DOI: 10.3389/fgene.2019.00155] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 02/13/2019] [Indexed: 12/13/2022] Open
Abstract
A recent focus of computational biology has been to integrate the complementary information available in molecular profiles as well as in multiple network databases in order to identify connected regions that show significant changes under different conditions. This allows for capturing dynamic and condition-specific mechanisms of the underlying phenomena and disease stages. Here we review 22 such integrative approaches for active module identification published over the last decade. This article only focuses on tools that are currently available for use and are well-maintained. We compare these methods focusing on their primary features, integrative abilities, network structures, mathematical models, and implementations. We also provide real-world scenarios in which these methods have been successfully applied, as well as highlight outstanding challenges in the field that remain to be addressed. The main objective of this review is to help potential users and researchers to choose the best method that is suitable for their data and analysis purpose.
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Sangam Shrestha
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
| | - Adib Shafi
- Department of Computer Science, Wayne State University, Detroit, MI, United States
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, United States
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, United States
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV, United States
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19
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Höllerhage M, Bickle M, Höglinger GU. Unbiased Screens for Modifiers of Alpha-Synuclein Toxicity. Curr Neurol Neurosci Rep 2019; 19:8. [PMID: 30739256 DOI: 10.1007/s11910-019-0925-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW We provide an overview about unbiased screens to identify modifiers of alpha-synuclein (αSyn)-induced toxicity, present the models and the libraries that have been used for screening, and describe how hits from primary screens were selected and validated. RECENT FINDINGS Screens can be classified as either genetic or chemical compound modifier screens, but a few screens do not fit this classification. Most screens addressing αSyn-induced toxicity, including genome-wide overexpressing and deletion, were performed in yeast. More recently, newer methods such as CRISPR-Cas9 became available and were used for screening purposes. Paradoxically, given that αSyn-induced toxicity plays a role in neurological diseases, there is a shortage of human cell-based models for screening. Moreover, most screens used mutant or fluorescently tagged forms of αSyn and only very few screens investigated wild-type αSyn. Particularly, no genome-wide αSyn toxicity screen in human dopaminergic neurons has been published so far. Most unbiased screens for modifiers of αSyn toxicity were performed in yeast, and there is a lack of screens performed in human and particularly dopaminergic cells.
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Affiliation(s)
- Matthias Höllerhage
- Department of Translational Neurodegeneration, German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany
- Department of Neurology, Technical University of Munich (TUM), 81675, Munich, Germany
| | - Marc Bickle
- HT-Technology Development Studio, Max Planck Institute of Molecular Cell Biology and Genetics, 01307, Dresden, Germany
| | - Günter U Höglinger
- Department of Translational Neurodegeneration, German Center for Neurodegenerative Diseases (DZNE), 81377, Munich, Germany.
- Department of Neurology, Technical University of Munich (TUM), 81675, Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Ludwig Maximilians University (LMU), 81377, Munich, Germany.
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20
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Network integration of multi-tumour omics data suggests novel targeting strategies. Nat Commun 2018; 9:4514. [PMID: 30375513 PMCID: PMC6207774 DOI: 10.1038/s41467-018-06992-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Accepted: 10/04/2018] [Indexed: 12/16/2022] Open
Abstract
We characterize different tumour types in search for multi-tumour drug targets, in particular aiming for drug repurposing and novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allows us to define three cluster-specific signatures, with genes belonging to NF-κB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we define new pharmacological strategies validated by in vitro experiments that show inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways that could possibly be used, singularly or in combination, for the design of novel treatment strategies. Tumours of different tissues can show similarities in genomic alterations. Here, the authors combine tumour transcriptome and protein interaction data in a network-based analysis of 11 tumours types, and identify clusters of tumours with specific signatures for multi-tumour drug targeting and survival prognosis.
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21
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Hutt DM, Loguercio S, Roth DM, Su AI, Balch WE. Correcting the F508del-CFTR variant by modulating eukaryotic translation initiation factor 3-mediated translation initiation. J Biol Chem 2018; 293:13477-13495. [PMID: 30006345 DOI: 10.1074/jbc.ra118.003192] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 07/05/2018] [Indexed: 12/31/2022] Open
Abstract
Inherited and somatic rare diseases result from >200,000 genetic variants leading to loss- or gain-of-toxic function, often caused by protein misfolding. Many of these misfolded variants fail to properly interact with other proteins. Understanding the link between factors mediating the transcription, translation, and protein folding of these disease-associated variants remains a major challenge in cell biology. Herein, we utilized the cystic fibrosis transmembrane conductance regulator (CFTR) protein as a model and performed a proteomics-based high-throughput screen (HTS) to identify pathways and components affecting the folding and function of the most common cystic fibrosis-associated mutation, the F508del variant of CFTR. Using a shortest-path algorithm we developed, we mapped HTS hits to the CFTR interactome to provide functional context to the targets and identified the eukaryotic translation initiation factor 3a (eIF3a) as a central hub for the biogenesis of CFTR. Of note, siRNA-mediated silencing of eIF3a reduced the polysome-to-monosome ratio in F508del-expressing cells, which, in turn, decreased the translation of CFTR variants, leading to increased CFTR stability, trafficking, and function at the cell surface. This finding suggested that eIF3a is involved in mediating the impact of genetic variations in CFTR on the folding of this protein. We posit that the number of ribosomes on a CFTR mRNA transcript is inversely correlated with the stability of the translated polypeptide. Polysome-based translation challenges the capacity of the proteostasis environment to balance message fidelity with protein folding, leading to disease. We suggest that this deficit can be corrected through control of translation initiation.
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Affiliation(s)
| | | | | | - Andrew I Su
- Integrative Structural and Computational Biology and
| | - William E Balch
- From the Departments of Molecular Medicine and .,the Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037
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22
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Knowledge-Based Neuroendocrine Immunomodulation (NIM) Molecular Network Construction and Its Application. Molecules 2018; 23:molecules23061312. [PMID: 29848990 PMCID: PMC6099962 DOI: 10.3390/molecules23061312] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 05/24/2018] [Accepted: 05/25/2018] [Indexed: 01/23/2023] Open
Abstract
Growing evidence shows that the neuroendocrine immunomodulation (NIM) network plays an important role in maintaining and modulating body function and the homeostasis of the internal environment. The disequilibrium of NIM in the body is closely associated with many diseases. In the present study, we first collected a core dataset of NIM signaling molecules based on our knowledge and obtained 611 NIM signaling molecules. Then, we built a NIM molecular network based on the MetaCore database and analyzed the signaling transduction characteristics of the core network. We found that the endocrine system played a pivotal role in the bridge between the nervous and immune systems and the signaling transduction between the three systems was not homogeneous. Finally, employing the forest algorithm, we identified the molecular hub playing an important role in the pathogenesis of rheumatoid arthritis (RA) and Alzheimer’s disease (AD), based on the NIM molecular network constructed by us. The results showed that GSK3B, SMARCA4, PSMD7, HNF4A, PGR, RXRA, and ESRRA might be the key molecules for RA, while RARA, STAT3, STAT1, and PSMD14 might be the key molecules for AD. The molecular hub may be a potentially druggable target for these two complex diseases based on the literature. This study suggests that the NIM molecular network in this paper combined with the forest algorithm might provide a useful tool for predicting drug targets and understanding the pathogenesis of diseases. Therefore, the NIM molecular network and the corresponding online tool will not only enhance research on complex diseases and system biology, but also promote the communication of valuable clinical experience between modern medicine and Traditional Chinese Medicine (TCM).
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23
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Bentham RB, Bryson K, Szabadkai G. MCbiclust: a novel algorithm to discover large-scale functionally related gene sets from massive transcriptomics data collections. Nucleic Acids Res 2017; 45:8712-8730. [PMID: 28911113 PMCID: PMC5587796 DOI: 10.1093/nar/gkx590] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 07/01/2017] [Indexed: 12/16/2022] Open
Abstract
The potential to understand fundamental biological processes from gene expression data has grown in parallel with the recent explosion of the size of data collections. However, to exploit this potential, novel analytical methods are required, capable of discovering large co-regulated gene networks. We found current methods limited in the size of correlated gene sets they could discover within biologically heterogeneous data collections, hampering the identification of multi-gene controlled fundamental cellular processes such as energy metabolism, organelle biogenesis and stress responses. Here we describe a novel biclustering algorithm called Massively Correlated Biclustering (MCbiclust) that selects samples and genes from large datasets with maximal correlated gene expression, allowing regulation of complex networks to be examined. The method has been evaluated using synthetic data and applied to large bacterial and cancer cell datasets. We show that the large biclusters discovered, so far elusive to identification by existing techniques, are biologically relevant and thus MCbiclust has great potential in the analysis of transcriptomics data to identify large-scale unknown effects hidden within the data. The identified massive biclusters can be used to develop improved transcriptomics based diagnosis tools for diseases caused by altered gene expression, or used for further network analysis to understand genotype-phenotype correlations.
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Affiliation(s)
- Robert B Bentham
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London WC1E 6BT, UK.,The Francis Crick Institute, London NW1 1AT, UK
| | - Kevin Bryson
- Department of Computer Sciences, University College London, London WC1E 6BT, UK
| | - Gyorgy Szabadkai
- Department of Cell and Developmental Biology, Consortium for Mitochondrial Research, University College London, London WC1E 6BT, UK.,The Francis Crick Institute, London NW1 1AT, UK.,Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy
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24
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Ruffalo M, Stojanov P, Pillutla VK, Varma R, Bar-Joseph Z. Reconstructing cancer drug response networks using multitask learning. BMC SYSTEMS BIOLOGY 2017; 11:96. [PMID: 29017547 PMCID: PMC5635550 DOI: 10.1186/s12918-017-0471-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 10/02/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. RESULTS The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. CONCLUSIONS Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.
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Affiliation(s)
- Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Petar Stojanov
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Venkata Krishna Pillutla
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Rohan Varma
- Electrical and Computer Engineering, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. .,Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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25
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Shrestha R, Hodzic E, Sauerwald T, Dao P, Wang K, Yeung J, Anderson S, Vandin F, Haffari G, Collins CC, Sahinalp SC. HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology. Genome Res 2017; 27:1573-1588. [PMID: 28768687 PMCID: PMC5580716 DOI: 10.1101/gr.221218.117] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 07/06/2017] [Indexed: 12/12/2022]
Abstract
Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.
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Affiliation(s)
- Raunak Shrestha
- Bioinformatics Training Program, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4.,Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Ermin Hodzic
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6
| | - Thomas Sauerwald
- Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom
| | - Phuong Dao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA
| | - Kendric Wang
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Jake Yeung
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Shawn Anderson
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6
| | - Fabio Vandin
- Department of Information Engineering, University of Padova, 35131 Padova, Italy
| | - Gholamreza Haffari
- Faculty of Information Technology, Monash University, Melbourne 3800, Australia
| | - Colin C Collins
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6.,Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada V5Z 1M9
| | - S Cenk Sahinalp
- Laboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, British Columbia, Canada V6H 3Z6.,School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6.,School of Informatics and Computing, Indiana University, Bloomington, Indiana 47408, USA
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26
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Surguchev AA, Surguchov A. Synucleins and Gene Expression: Ramblers in a Crowd or Cops Regulating Traffic? Front Mol Neurosci 2017; 10:224. [PMID: 28751856 PMCID: PMC5508120 DOI: 10.3389/fnmol.2017.00224] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Accepted: 06/29/2017] [Indexed: 01/09/2023] Open
Abstract
Synuclein family consists of three members, α, β, and γ-synuclein. Due to their involvement in human diseases, they have been thoroughly investigated for the last 30 years. Since the first synuclein identification and description, members of this family are found in all vertebrates. Sequencing of their genes indicates high evolutionary conservation suggesting important function(s) of these proteins. They are small naturally unfolded proteins prone to aggregate, easily change their conformation, and bind to the membranes. The genes for α, β, and γ-synuclein have different chromosomal localization and a well preserved general organization composed of five coding exons of similar size. Three genes encoding synucleins are present in the majority of vertebrates, however, a variable number of synuclein genes are described in fishes of different species. An important question concerns their normal function in cells and tissues. α-Synuclein is implicated in the regulation of synaptic activity through regulation of synaptic vesicle release, while the physiological functions of two other members of the family is understood less clearly. Here we discuss recent results describing their role in the regulation of gene expression.
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Affiliation(s)
- Alexei A Surguchev
- Department of Surgery, Section of Otolaryngology, Yale School of Medicine, Yale University, New HavenCT, United States
| | - Andrei Surguchov
- Department of Neurology, University of Kansas Medical Center, Kansas CityKS, United States
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27
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Amar D, Izraeli S, Shamir R. Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications. Oncogene 2017; 36:3375-3383. [PMID: 28092680 PMCID: PMC5485176 DOI: 10.1038/onc.2016.489] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 11/20/2016] [Accepted: 11/22/2016] [Indexed: 02/07/2023]
Abstract
Large cancer projects measure somatic mutations in thousands of samples, gradually assembling a catalog of recurring mutations in cancer. Many methods analyze these data jointly with auxiliary information with the aim of identifying subtype-specific results. Here, we show that somatic gene mutations alone can reliably and specifically predict cancer subtypes. Interpretation of the classifiers provides useful insights for several biomedical applications. We analyze the COSMIC database, which collects somatic mutations from The Cancer Genome Atlas (TCGA) as well as from many smaller scale studies. We use multi-label classification techniques and the Disease Ontology hierarchy in order to identify cancer subtype-specific biomarkers. Cancer subtype classifiers based on TCGA and the smaller studies have comparable performance, and the smaller studies add a substantial value in terms of validation, coverage of additional subtypes, and improved classification. The gene sets of the classifiers are used for threefold contribution. First, we refine the associations of genes to cancer subtypes and identify novel compelling candidate driver genes. Second, using our classifiers we successfully predict the primary site of metastatic samples. Third, we provide novel hypotheses regarding detection of subtype-specific synthetic lethality interactions. From the cancer research community perspective, our results suggest that curation efforts, such as COSMIC, have great added and complementary value even in the era of large international cancer projects.
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Affiliation(s)
- D Amar
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - S Izraeli
- Department of Pediatric Hematology-Oncology, Safra Children’s Hospital, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- Sackler School of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | - R Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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28
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Abstract
De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. De novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules.
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29
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Qin J, Yan B, Hu Y, Wang P, Wang J. Applications of integrative OMICs approaches to gene regulation studies. QUANTITATIVE BIOLOGY 2016. [DOI: 10.1007/s40484-016-0085-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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30
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Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules. Sci Rep 2016; 6:34841. [PMID: 27731320 PMCID: PMC5059623 DOI: 10.1038/srep34841] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 08/19/2016] [Indexed: 11/08/2022] Open
Abstract
A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD.
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31
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Dorier J, Crespo I, Niknejad A, Liechti R, Ebeling M, Xenarios I. Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method. BMC Bioinformatics 2016; 17:410. [PMID: 27716031 PMCID: PMC5053080 DOI: 10.1186/s12859-016-1287-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 09/29/2016] [Indexed: 12/20/2022] Open
Abstract
Background Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs. Results We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses. Conclusions Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic PKNs. These contextualized models have improved utility for hypothesis generation and experimental design. The general applicability of this methodological approach makes it suitable for a variety of biological systems and of general interest for biological and medical research. Our method was implemented in the software optimusqual, available online at http://www.vital-it.ch/software/optimusqual/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1287-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Julien Dorier
- Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
| | - Isaac Crespo
- Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Anne Niknejad
- Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Robin Liechti
- Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Martin Ebeling
- Pharmaceutical Sciences/Translational Technologies and Bioinformatics, Roche Innovation Center Basel, 124 Grenzacherstrasse, 4070, Basel, Switzerland
| | - Ioannis Xenarios
- Vital-IT, Systems biology and medicine department, SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
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32
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Tuncbag N, Gosline SJC, Kedaigle A, Soltis AR, Gitter A, Fraenkel E. Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package. PLoS Comput Biol 2016; 12:e1004879. [PMID: 27096930 PMCID: PMC4838263 DOI: 10.1371/journal.pcbi.1004879] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 03/23/2016] [Indexed: 02/07/2023] Open
Abstract
High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.
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Affiliation(s)
- Nurcan Tuncbag
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Sara J. C. Gosline
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Amanda Kedaigle
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Anthony R. Soltis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Anthony Gitter
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail:
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33
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Kim M, Hwang D. Network-Based Protein Biomarker Discovery Platforms. Genomics Inform 2016; 14:2-11. [PMID: 27103885 PMCID: PMC4838525 DOI: 10.5808/gi.2016.14.1.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 01/06/2016] [Accepted: 01/07/2016] [Indexed: 02/06/2023] Open
Abstract
The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.
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Affiliation(s)
- Minhyung Kim
- Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea
| | - Daehee Hwang
- Department of New Biology and Center for Plant Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea
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34
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De Maeyer D, Weytjens B, De Raedt L, Marchal K. Network-Based Analysis of eQTL Data to Prioritize Driver Mutations. Genome Biol Evol 2016; 8:481-94. [PMID: 26802430 PMCID: PMC4825419 DOI: 10.1093/gbe/evw010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html
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Affiliation(s)
- Dries De Maeyer
- Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
| | - Bram Weytjens
- Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
| | - Luc De Raedt
- Department of Computer Science, KU Leuven, Celestijnenlaan 200A, B-3001 Leuven, Belgium
| | - Kathleen Marchal
- Deptartment of Information Technology (INTEC, iMINDS), UGent, 9052 Ghent, Belgium Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Ghent, Belgium Department of Genetics, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
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35
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Creixell P, Reimand J, Haider S, Wu G, Shibata T, Vazquez M, Mustonen V, Gonzalez-Perez A, Pearson J, Sander C, Raphael BJ, Marks DS, Ouellette BFF, Valencia A, Bader GD, Boutros PC, Stuart JM, Linding R, Lopez-Bigas N, Stein LD. Pathway and network analysis of cancer genomes. Nat Methods 2015; 12:615-621. [PMID: 26125594 DOI: 10.1038/nmeth.3440] [Citation(s) in RCA: 218] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Accepted: 04/27/2015] [Indexed: 12/26/2022]
Abstract
Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.
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Affiliation(s)
- Pau Creixell
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark
| | - Jüri Reimand
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Syed Haider
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Guanming Wu
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Tatsuhiro Shibata
- Division of Cancer Genomics, National Cancer Center, Chuo-ku, Tokyo, Japan
| | - Miguel Vazquez
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Ville Mustonen
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Abel Gonzalez-Perez
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain
| | - John Pearson
- Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, Queensland, Australia
| | - Chris Sander
- Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Benjamin J Raphael
- Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA USA
| | - B F Francis Ouellette
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Alfonso Valencia
- Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre, Madrid, Spain
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Paul C Boutros
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, California, USA.,Center for Biomolecular Science and Engineering, University of California, Santa Cruz, California, USA
| | - Rune Linding
- Cellular Signal Integration Group (C-SIG), Technical University of Denmark, Lyngby, Denmark.,Biotech Research & Innovation Centre (BRIC), University of Copenhagen (UCPH), DK-2200 Copenhagen, Denmark
| | - Nuria Lopez-Bigas
- Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - Lincoln D Stein
- Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
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36
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Terfve CDA, Wilkes EH, Casado P, Cutillas PR, Saez-Rodriguez J. Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nat Commun 2015; 6:8033. [PMID: 26354681 PMCID: PMC4579397 DOI: 10.1038/ncomms9033] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 07/09/2015] [Indexed: 12/27/2022] Open
Abstract
Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data. Phosphoproteomics can offer significant insight into cell signalling and how signalling is modified in response to perturbations. Here the authors develop a new tool for the analysis of high-content phosphoproteomics in the context of kinase/phosphatase-substrate knowledge, which is used to train logic models.
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Affiliation(s)
- Camille D A Terfve
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Edmund H Wilkes
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Pedro Casado
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Pedro R Cutillas
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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37
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Rodriguez A, Crespo I, Fournier A, del Sol A. Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET. PLoS One 2015; 10:e0127216. [PMID: 26058016 PMCID: PMC4461287 DOI: 10.1371/journal.pone.0127216] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Accepted: 04/13/2015] [Indexed: 01/09/2023] Open
Abstract
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.
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Affiliation(s)
- Ana Rodriguez
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Isaac Crespo
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Anna Fournier
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg
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De Maeyer D, Weytjens B, Renkens J, De Raedt L, Marchal K. PheNetic: network-based interpretation of molecular profiling data. Nucleic Acids Res 2015; 43:W244-50. [PMID: 25878035 PMCID: PMC4489255 DOI: 10.1093/nar/gkv347] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 04/03/2015] [Indexed: 12/17/2022] Open
Abstract
Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.
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Affiliation(s)
- Dries De Maeyer
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium
| | - Bram Weytjens
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium
| | - Joris Renkens
- Dept. of Computer Science, KULeuven, Leuven, 3000, Belgium
| | - Luc De Raedt
- Dept. of Computer Science, KULeuven, Leuven, 3000, Belgium
| | - Kathleen Marchal
- Dept. of Microbial and Molecular Systems, KULeuven, Leuven, 3000, Belgium Dept. of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, 9052, Belgium Dept. of Plant Biotechnology and Bioinformatics, U.Ghent, Ghent, 9052, Belgium
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39
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Belinky F, Nativ N, Stelzer G, Zimmerman S, Iny Stein T, Safran M, Lancet D. PathCards: multi-source consolidation of human biological pathways. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav006. [PMID: 25725062 PMCID: PMC4343183 DOI: 10.1093/database/bav006] [Citation(s) in RCA: 174] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The study of biological pathways is key to a large number of systems analyses. However, many relevant tools consider a limited number of pathway sources, missing out on many genes and gene-to-gene connections. Simply pooling several pathways sources would result in redundancy and the lack of systematic pathway interrelations. To address this, we exercised a combination of hierarchical clustering and nearest neighbor graph representation, with judiciously selected cutoff values, thereby consolidating 3215 human pathways from 12 sources into a set of 1073 SuperPaths. Our unification algorithm finds a balance between reducing redundancy and optimizing the level of pathway-related informativeness for individual genes. We show a substantial enhancement of the SuperPaths’ capacity to infer gene-to-gene relationships when compared with individual pathway sources, separately or taken together. Further, we demonstrate that the chosen 12 sources entail nearly exhaustive gene coverage. The computed SuperPaths are presented in a new online database, PathCards, showing each SuperPath, its constituent network of pathways, and its contained genes. This provides researchers with a rich, searchable systems analysis resource.Database URL:http://pathcards.genecards.org/
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Affiliation(s)
- Frida Belinky
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noam Nativ
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Gil Stelzer
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Shahar Zimmerman
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tsippi Iny Stein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Marilyn Safran
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Doron Lancet
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
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40
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Gosline SJC, Oh C, Fraenkel E. SAMNetWeb: identifying condition-specific networks linking signaling and transcription. Bioinformatics 2014; 31:1124-6. [PMID: 25414365 DOI: 10.1093/bioinformatics/btu748] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Accepted: 11/07/2014] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once. SUMMARY We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment 'share' underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments. AVAILABILITY AND IMPLEMENTATION SAMNetWeb is freely available at http://fraenkel.mit.edu/samnetweb.
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Affiliation(s)
- Sara J C Gosline
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Coyin Oh
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Chasman D, Ho YH, Berry DB, Nemec CM, MacGilvray ME, Hose J, Merrill AE, Lee MV, Will JL, Coon JJ, Ansari AZ, Craven M, Gasch AP. Pathway connectivity and signaling coordination in the yeast stress-activated signaling network. Mol Syst Biol 2014; 10:759. [PMID: 25411400 PMCID: PMC4299600 DOI: 10.15252/msb.20145120] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet
knowledge of the complete stress-activated regulatory network as well as design principles for
signal integration remains incomplete. We developed an experimental and computational approach to
integrate available protein interaction data with gene fitness contributions, mutant transcriptome
profiles, and phospho-proteome changes in cells responding to salt stress, to infer the
salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions
by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and
pointing to previously unknown ‘hubs’ of signal integration. We exploited these
predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of
RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related
transcripts. We find that the orthologous human network is enriched for cancer-causing genes,
underscoring the importance of the subnetwork's predictions in understanding stress
biology.
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Affiliation(s)
- Deborah Chasman
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Yi-Hsuan Ho
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - David B Berry
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Corey M Nemec
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - James Hose
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna E Merrill
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - M Violet Lee
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jessica L Will
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biological Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Aseem Z Ansari
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
| | - Mark Craven
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Audrey P Gasch
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA Genome Center of Wisconsin, University of Wisconsin-Madison, Madison, WI, USA
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42
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Osmanbeyoglu HU, Pelossof R, Bromberg JF, Leslie CS. Linking signaling pathways to transcriptional programs in breast cancer. Genome Res 2014; 24:1869-80. [PMID: 25183703 PMCID: PMC4216927 DOI: 10.1101/gr.173039.114] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cancer cells acquire genetic and epigenetic alterations that often lead to dysregulation of oncogenic signal transduction pathways, which in turn alters downstream transcriptional programs. Numerous methods attempt to deduce aberrant signaling pathways in tumors from mRNA data alone, but these pathway analysis approaches remain qualitative and imprecise. In this study, we present a statistical method to link upstream signaling to downstream transcriptional response by exploiting reverse phase protein array (RPPA) and mRNA expression data in The Cancer Genome Atlas (TCGA) breast cancer project. Formally, we use an algorithm called affinity regression to learn an interaction matrix between upstream signal transduction proteins and downstream transcription factors (TFs) that explains target gene expression. The trained model can then predict the TF activity, given a tumor sample’s protein expression profile, or infer the signaling protein activity, given a tumor sample’s gene expression profile. Breast cancers are comprised of molecularly distinct subtypes that respond differently to pathway-targeted therapies. We trained our model on the TCGA breast cancer data set and identified subtype-specific and common TF regulators of gene expression. We then used the trained tumor model to predict signaling protein activity in a panel of breast cancer cell lines for which gene expression and drug response data was available. Correlations between inferred protein activities and drug responses in breast cancer cell lines grouped several drugs that are clinically used in combination. Finally, inferred protein activity predicted the clinical outcome within the METABRIC Luminal A cohort, identifying high- and low-risk patient groups within this heterogeneous subtype.
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Affiliation(s)
- Hatice U Osmanbeyoglu
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Raphael Pelossof
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Jacqueline F Bromberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, New York, New York 10065, USA
| | - Christina S Leslie
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA;
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43
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Mazza A, Gat-Viks I, Sharan R. Elucidating influenza inhibition pathways via network reconstruction. J Comput Biol 2014; 21:394-404. [PMID: 24450433 PMCID: PMC4010177 DOI: 10.1089/cmb.2013.0147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Viruses evade detection by the host immune system through the suppression of antiviral pathways. These pathways are thus obscured when measuring the host response to viral infection and cannot be inferred by current network reconstruction methodology. Here we aim to close this gap by providing a novel computational framework for the inference of such inhibited pathways as well as the proteins targeted by the virus to achieve this inhibition. We demonstrate the power of our method by testing it on the response to influenza infection in humans, with and without the viral inhibitory protein NS1, revealing its direct targets and their inhibitory effects.
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Affiliation(s)
- Arnon Mazza
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Irit Gat-Viks
- Department of Cell Research and Immunology, Tel Aviv University, Tel Aviv, Israel
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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44
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Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 2013; 14:719-32. [PMID: 24045689 DOI: 10.1038/nrg3552] [Citation(s) in RCA: 351] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
A central goal of systems biology is to elucidate the structural and functional architecture of the cell. To this end, large and complex networks of molecular interactions are being rapidly generated for humans and model organisms. A recent focus of bioinformatics research has been to integrate these networks with each other and with diverse molecular profiles to identify sets of molecules and interactions that participate in a common biological function - that is, 'modules'. Here, we classify such integrative approaches into four broad categories, describe their bioinformatic principles and review their applications.
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45
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Knapp B, Kaderali L. Reconstruction of cellular signal transduction networks using perturbation assays and linear programming. PLoS One 2013; 8:e69220. [PMID: 23935958 PMCID: PMC3728289 DOI: 10.1371/journal.pone.0069220] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2012] [Accepted: 06/06/2013] [Indexed: 12/23/2022] Open
Abstract
Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4+ T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
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Affiliation(s)
- Bettina Knapp
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- ViroQuant Research Group Modeling, BioQuant, Heidelberg University, Heidelberg, Germany
| | - Lars Kaderali
- Institute for Medical Informatics and Biometry, Medical Faculty Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- ViroQuant Research Group Modeling, BioQuant, Heidelberg University, Heidelberg, Germany
- * E-mail:
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Tuncbag N, Braunstein A, Pagnani A, Huang SSC, Chayes J, Borgs C, Zecchina R, Fraenkel E. Simultaneous reconstruction of multiple signaling pathways via the prize-collecting steiner forest problem. J Comput Biol 2013; 20:124-36. [PMID: 23383998 DOI: 10.1089/cmb.2012.0092] [Citation(s) in RCA: 90] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Signaling and regulatory networks are essential for cells to control processes such as growth, differentiation, and response to stimuli. Although many "omic" data sources are available to probe signaling pathways, these data are typically sparse and noisy. Thus, it has been difficult to use these data to discover the cause of the diseases and to propose new therapeutic strategies. We overcome these problems and use "omic" data to reconstruct simultaneously multiple pathways that are altered in a particular condition by solving the prize-collecting Steiner forest problem. To evaluate this approach, we use the well-characterized yeast pheromone response. We then apply the method to human glioblastoma data, searching for a forest of trees, each of which is rooted in a different cell-surface receptor. This approach discovers both overlapping and independent signaling pathways that are enriched in functionally and clinically relevant proteins, which could provide the basis for new therapeutic strategies. Although the algorithm was not provided with any information about the phosphorylation status of receptors, it identifies a small set of clinically relevant receptors among hundreds present in the interactome.
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Affiliation(s)
- Nurcan Tuncbag
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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47
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Basha O, Tirman S, Eluk A, Yeger-Lotem E. ResponseNet2.0: Revealing signaling and regulatory pathways connecting your proteins and genes--now with human data. Nucleic Acids Res 2013; 41:W198-203. [PMID: 23761447 PMCID: PMC3692079 DOI: 10.1093/nar/gkt532] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome sequencing and transcriptomic profiling are two widely used approaches for the identification of human disease pathways. However, each approach typically provides a limited view of disease pathways: Genome sequencing can identify disease-related mutations but rarely reveals their mode-of-action, while transcriptomic assays do not reveal the series of events that lead to the transcriptomic change. ResponseNet is an integrative network-optimization approach that we developed to fill these gaps by highlighting major signaling and regulatory molecular interaction paths that connect disease-related mutations and genes. The ResponseNet web-server provides a user-friendly interface to ResponseNet. Specifically, users can upload weighted lists of proteins and genes and obtain a sparse, weighted, molecular interaction subnetwork connecting them, that is biased toward regulatory and signaling pathways. ResponseNet2.0 enhances the functionality of the ResponseNet web-server in two important ways. First, it supports analysis of human data by offering a human interactome composed of proteins, genes and micro-RNAs. Second, it offers a new informative view of the output, including a randomization analysis, to help users assess the biological relevance of the output subnetwork. ResponseNet2.0 is available at http://netbio.bgu.ac.il/respnet .
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Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry & Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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48
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Abstract
High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
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Affiliation(s)
- Bonnie Berger
- Department of Mathematics and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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49
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Sales G, Calura E, Martini P, Romualdi C. Graphite Web: Web tool for gene set analysis exploiting pathway topology. Nucleic Acids Res 2013; 41:W89-97. [PMID: 23666626 PMCID: PMC3977659 DOI: 10.1093/nar/gkt386] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Graphite web is a novel web tool for pathway analyses and network visualization for gene
expression data of both microarray and RNA-seq experiments. Several pathway analyses have
been proposed either in the univariate or in the global and multivariate context to tackle
the complexity and the interpretation of expression results. These methods can be further
divided into ‘topological’ and ‘non-topological’ methods according
to their ability to gain power from pathway topology. Biological pathways are, in fact,
not only gene lists but can be represented through a network where genes and connections
are, respectively, nodes and edges. To this day, the most used approaches are
non-topological and univariate although they miss the relationship among genes. On the
contrary, topological and multivariate approaches are more powerful, but difficult to be
used by researchers without bioinformatic skills. Here we present Graphite web, the first
public web server for pathway analysis on gene expression data that combines topological
and multivariate pathway analyses with an efficient system of interactive network
visualizations for easy results interpretation. Specifically, Graphite web implements five
different gene set analyses on three model organisms and two pathway databases. Graphite
Web is freely available at http://graphiteweb.bio.unipd.it/.
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Affiliation(s)
- Gabriele Sales
- Department of Biology, University of Padova, Via U. Bassi 58/B, 35121 Padova, Italy
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50
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De Maeyer D, Renkens J, Cloots L, De Raedt L, Marchal K. PheNetic: network-based interpretation of unstructured gene lists in E. coli. MOLECULAR BIOSYSTEMS 2013; 9:1594-603. [PMID: 23591551 DOI: 10.1039/c3mb25551d] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .
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Affiliation(s)
- Dries De Maeyer
- Center of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium
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