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Ehsani R, Drabløs F. Enhanced identification of significant regulators of gene expression. BMC Bioinformatics 2020; 21:134. [PMID: 32252623 PMCID: PMC7132893 DOI: 10.1186/s12859-020-3468-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 03/24/2020] [Indexed: 12/29/2022] Open
Abstract
Background Diseases like cancer will lead to changes in gene expression, and it is relevant to identify key regulatory genes that can be linked directly to these changes. This can be done by computing a Regulatory Impact Factor (RIF) score for relevant regulators. However, this computation is based on estimating correlated patterns of gene expression, often Pearson correlation, and an assumption about a set of specific regulators, normally transcription factors. This study explores alternative measures of correlation, using the Fisher and Sobolev metrics, and an extended set of regulators, including epigenetic regulators and long non-coding RNAs (lncRNAs). Data on prostate cancer have been used to explore the effect of these modifications. Results A tool for computation of RIF scores with alternative correlation measures and extended sets of regulators was developed and tested on gene expression data for prostate cancer. The study showed that the Fisher and Sobolev metrics lead to improved identification of well-documented regulators of gene expression in prostate cancer, and the sets of identified key regulators showed improved overlap with previously defined gene sets of relevance to cancer. The extended set of regulators lead to identification of several interesting candidates for further studies, including lncRNAs. Several key processes were identified as important, including spindle assembly and the epithelial-mesenchymal transition (EMT). Conclusions The study has shown that using alternative metrics of correlation can improve the performance of tools based on correlation of gene expression in genomic data. The Fisher and Sobolev metrics should be considered also in other correlation-based applications.
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Affiliation(s)
- Rezvan Ehsani
- Department of Mathematics, University of Zabol, Zabol, Iran. .,Department of Bioinformatics, University of Zabol, Zabol, Iran.
| | - Finn Drabløs
- Department of Cancer Research and Molecular Medicine, NTNU - Norwegian University of Science and Technology, NO-7491, Trondheim, Norway.
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2
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Agrahari R, Foroushani A, Docking TR, Chang L, Duns G, Hudoba M, Karsan A, Zare H. Applications of Bayesian network models in predicting types of hematological malignancies. Sci Rep 2018; 8:6951. [PMID: 29725024 PMCID: PMC5934387 DOI: 10.1038/s41598-018-24758-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 04/05/2018] [Indexed: 12/17/2022] Open
Abstract
Network analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.
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Affiliation(s)
- Rupesh Agrahari
- Department of Computer Science, Texas State University, San Marcos, Texas, 78666, USA
| | - Amir Foroushani
- Department of Computer Science, Texas State University, San Marcos, Texas, 78666, USA
| | - T Roderick Docking
- Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Linda Chang
- Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Gerben Duns
- Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Monika Hudoba
- Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Vancouver, British Columbia, V5Z 1M9, Canada
| | - Aly Karsan
- Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Habil Zare
- Department of Computer Science, Texas State University, San Marcos, Texas, 78666, USA. .,Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, Texas, 78229, USA.
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Marchi FA, Martins DC, Barros-Filho MC, Kuasne H, Busso Lopes AF, Brentani H, Trindade Filho JCS, Guimarães GC, Faria EF, Scapulatempo-Neto C, Lopes A, Rogatto SR. Multidimensional integrative analysis uncovers driver candidates and biomarkers in penile carcinoma. Sci Rep 2017; 7:6707. [PMID: 28751665 PMCID: PMC5532302 DOI: 10.1038/s41598-017-06659-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 06/19/2017] [Indexed: 01/24/2023] Open
Abstract
Molecular data generation and their combination in penile carcinomas (PeCa), a significant public health problem in poor and underdeveloped countries, remain virtually unexplored. An integrativemethodology combin ing genome-wide copy number alteration, DNA methylation, miRNA and mRNA expression analysis was performed in a set of 20 usual PeCa. The well-ranked 16 driver candidates harboring genomic alterations and regulated by a set of miRNAs, including hsa-miR-31, hsa-miR-34a and hsa-miR-130b, were significantly associated with over-represented pathways in cancer, such as immune-inflammatory system, apoptosis and cell cycle. Modules of co-expressed genes generated from expression matrix were associated with driver candidates and classified according to the over-representation of passengers, thus suggesting an alteration of the pathway dynamics during the carcinogenesis. This association resulted in 10 top driver candidates (AR, BIRC5, DNMT3B, ERBB4, FGFR1, PML, PPARG, RB1, TNFSF10 and STAT1) selected and confirmed as altered in an independent set of 33 PeCa samples. In addition to the potential driver genes herein described, shorter overall survival was associated with BIRC5 and DNMT3B overexpression (log-rank test, P = 0.026 and P = 0.002, respectively) highlighting its potential as novel prognostic marker for penile cancer.
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Affiliation(s)
| | - David Correa Martins
- Center of Mathematics, Computing and Cognition, Federal University of ABC - UFABC, Santo André, SP, Brazil
| | | | | | | | - Helena Brentani
- Department of Psychiatry, Medical School, University of Sao Paulo - USP, São Paulo, SP, Brazil
| | | | | | - Eliney F Faria
- Department of Urology, Barretos Cancer Hospital, Barretos, São Paulo, Brazil
| | | | - Ademar Lopes
- A.C.Camargo Cancer Center, São Paulo, SP, Brazil
| | - Silvia Regina Rogatto
- Department of Urology, Faculty of Medicine, Sao Paulo State University - UNESP, Botucatu, SP, Brazil.
- Department of Clinical Genetics, Vejle Hospital and Institute of Regional Health, University of Southern Denmark, Odense, Denmark.
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4
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Costa RL, Gadelha L, Ribeiro-Alves M, Porto F. GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis. PeerJ 2017; 5:e3509. [PMID: 28695067 PMCID: PMC5501156 DOI: 10.7717/peerj.3509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 06/06/2017] [Indexed: 12/28/2022] Open
Abstract
There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.
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Affiliation(s)
- Raquel L. Costa
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
- National Institute of Cancer (INCA), Rio de Janeiro, RJ, Brazil
| | - Luiz Gadelha
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
| | - Marcelo Ribeiro-Alves
- Laboratory of Clinical Research in DST- AIDS, National Institute of Infectology Evandro Chagas, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Fábio Porto
- DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil
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Al-Harazi O, Al Insaif S, Al-Ajlan MA, Kaya N, Dzimiri N, Colak D. Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network. J Genet Genomics 2015; 43:349-67. [PMID: 27318646 DOI: 10.1016/j.jgg.2015.11.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2015] [Revised: 10/22/2015] [Accepted: 11/20/2015] [Indexed: 12/16/2022]
Abstract
A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.
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Affiliation(s)
- Olfat Al-Harazi
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Sadiq Al Insaif
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Monirah A Al-Ajlan
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Namik Kaya
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Nduna Dzimiri
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Dilek Colak
- Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia.
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Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA. INsPeCT: INtegrative Platform for Cancer Transcriptomics. Cancer Inform 2014; 13:59-66. [PMID: 24653643 PMCID: PMC3956744 DOI: 10.4137/cin.s13630] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Revised: 01/08/2014] [Accepted: 01/08/2014] [Indexed: 01/21/2023] Open
Abstract
The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities. New user-friendly, integrative, and automated analytical approaches are required to make computational methods more generally useful to the research community. Here we present INsPeCT (INtegrative Platform for Cancer Transcriptomics), which allows users with basic computer skills to perform comprehensive in-silico analyses of microarray, ChIP-seq, and RNA-seq data. INsPeCT supports the selection of interesting genes for advanced functional analysis. Included in its automated workflows are (i) a novel analytical framework, RMaNI (regulatory module network inference), which supports the inference of cancer subtype-specific transcriptional module networks and the analysis of modules; and (ii) WGCNA (weighted gene co-expression network analysis), which infers modules of highly correlated genes across microarray samples, associated with sample traits, eg survival time. INsPeCT is available free of cost from Bioinformatics Resource Australia-EMBL and can be accessed at http://inspect.braembl.org.au.
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Affiliation(s)
- Piyush B Madhamshettiwar
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Stefan R Maetschke
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Melissa J Davis
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
| | - Antonio Reverter
- CSIRO Animal, Food and Health Sciences, St. Lucia, Brisbane, Queensland, Australia
| | - Mark A Ragan
- The University of Queensland, Institute for Molecular Bioscience, St. Lucia, Brisbane, Queensland, Australia. ; Australian Research Council Centre of Excellence in Bioinformatics, St. Lucia, Brisbane, Queensland, Australia
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