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Sanchita, Sharma A. Computational gene expression profiling under salt stress reveals patterns of co-expression. GENOMICS DATA 2016; 7:214-21. [PMID: 26981411 PMCID: PMC4778677 DOI: 10.1016/j.gdata.2016.01.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Revised: 01/11/2016] [Accepted: 01/14/2016] [Indexed: 10/28/2022]
Abstract
Plants respond differently to environmental conditions. Among various abiotic stresses, salt stress is a condition where excess salt in soil causes inhibition of plant growth. To understand the response of plants to the stress conditions, identification of the responsible genes is required. Clustering is a data mining technique used to group the genes with similar expression. The genes of a cluster show similar expression and function. We applied clustering algorithms on gene expression data of Solanum tuberosum showing differential expression in Capsicum annuum under salt stress. The clusters, which were common in multiple algorithms were taken further for analysis. Principal component analysis (PCA) further validated the findings of other cluster algorithms by visualizing their clusters in three-dimensional space. Functional annotation results revealed that most of the genes were involved in stress related responses. Our findings suggest that these algorithms may be helpful in the prediction of the function of co-expressed genes.
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Affiliation(s)
- Sanchita
- Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Post Office CIMAP, Lucknow 226015, India
| | - Ashok Sharma
- Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Post Office CIMAP, Lucknow 226015, India
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Stanberry L, Mias GI, Haynes W, Higdon R, Snyder M, Kolker E. Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile. Metabolites 2013; 3:741-60. [PMID: 24958148 PMCID: PMC3901289 DOI: 10.3390/metabo3030741] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Revised: 07/30/2013] [Accepted: 08/05/2013] [Indexed: 12/23/2022] Open
Abstract
The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.
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Affiliation(s)
- Larissa Stanberry
- Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA.
| | - George I Mias
- Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA.
| | - Winston Haynes
- Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA.
| | - Roger Higdon
- Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA.
| | - Michael Snyder
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, 98101, USA.
| | - Eugene Kolker
- Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA.
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Leishi Zhang, Kuljis J, Xiaohui Liu. Information Visualization for DNA Microarray Data Analysis: A Critical Review. ACTA ACUST UNITED AC 2008. [DOI: 10.1109/tsmcc.2007.906065] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Linsen L, Löcherbach J, Berth M, Becher D, Bernhardt J. Visual analysis of gel-free proteome data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2006; 12:497-508. [PMID: 16805259 DOI: 10.1109/tvcg.2006.82] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We present a visual exploration system supporting protein analysis when using gel-free data acquisition methods. The data to be analyzed is obtained by coupling liquid chromatography (LC) with mass spectrometry (MS). LC-MS data have the properties of being nonequidistantly distributed in the time dimension (measured by LC) and being scattered in the mass-to-charge ratio dimension (measured by MS). We describe a hierarchical data representation and visualization method for large LC-MS data. Based on this visualization, we have developed a tool that supports various data analysis steps. Our visual tool provides a global understanding of the data, intuitive detection and classification of experimental errors, and extensions to LC-MS/MS, LC/LC-MS, and LC/LC-MS/MS data analysis. Due to the presence of randomly occurring rare isotopes within the same protein molecule, several intensity peaks may be detected that all refer to the same peptide. We have developed methods to unite such intensity peaks. This deisotoping step is visually documented by our system, such that misclassification can be detected intuitively. For differential protein expression analysis, we compute and visualize the differences in protein amounts between experiments. In order to compute the differential expression, the experimental data need to be registered. For registration, we perform a nonrigid warping step based on landmarks. The landmarks can be assigned automatically using protein identification methods. We evaluate our methods by comparing protein analysis with and without our interactive visualization-based exploration tool.
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Affiliation(s)
- Lars Linsen
- Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-Universität Greifswald, Germany.
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Dadzie AS, Burger A. Providing visualisation support for the analysis of anatomy ontology data. BMC Bioinformatics 2005; 6:74. [PMID: 15790390 PMCID: PMC1087473 DOI: 10.1186/1471-2105-6-74] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2004] [Accepted: 03/24/2005] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data. RESULTS A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis. CONCLUSION Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies.
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Affiliation(s)
- Aba-Sah Dadzie
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, Scotland
| | - Albert Burger
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, Scotland
- Medical Research Council, Human Genetics Unit, Western General Hospital, Edinburgh EH4 2XU, Scotland
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d-matrix - database exploration, visualization and analysis. BMC Bioinformatics 2004; 5:168. [PMID: 15511298 PMCID: PMC533865 DOI: 10.1186/1471-2105-5-168] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2004] [Accepted: 10/28/2004] [Indexed: 11/16/2022] Open
Abstract
Background Motivated by a biomedical database set up by our group, we aimed to develop a generic database front-end with embedded knowledge discovery and analysis features. A major focus was the human-oriented representation of the data and the enabling of a closed circle of data query, exploration, visualization and analysis. Results We introduce a non-task-specific database front-end with a new visualization strategy and built-in analysis features, so called d-matrix. d-matrix is web-based and compatible with a broad range of database management systems. The graphical outcome consists of boxes whose colors show the quality of the underlying information and, as the name suggests, they are arranged in matrices. The granularity of the data display allows consequent drill-down. Furthermore, d-matrix offers context-sensitive categorization, hierarchical sorting and statistical analysis. Conclusions d-matrix enables data mining, with a high level of interactivity between humans and computer as a primary factor. We believe that the presented strategy can be very effective in general and especially useful for the integration of distinct data types such as phenotypical and molecular data.
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Tefferi A, Bolander ME, Ansell SM, Wieben ED, Spelsberg TC. Primer on medical genomics. Part III: Microarray experiments and data analysis. Mayo Clin Proc 2002; 77:927-40. [PMID: 12233926 DOI: 10.4065/77.9.927] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Genomics has been defined as the comprehensive study of whole sets of genes, gene products, and their interactions as opposed to the study of single genes or proteins. Microarray technology is one of many novel tools that are allowing global and high-throughput analysis of genes and gene products. In addition to an introduction on underlying principles, the current review focuses on the use of both complementary DNA and oligodeoxynucleotide microarrays in gene expression analysis. Genome-wide experiments generate a massive amount of data points that require systematic methods of analysis to extract biologically useful information. Accordingly, the current educational communication discusses different methods of data analysis, including supervised and unsupervised clustering algorithms. Illustrative clinical examples show clinical applications, including (1) identification of candidate genes or pathological pathways (ie, elucidation of pathogenesis); (2) identification of "new" molecular classes of diseases that may be relevant in disease reclassification, prognostication, and treatment selection (ie, class discovery); and (3) use of expression profiles of known disease classes to predict diagnosis and classification of unknown samples (ie, class prediction). The current review should serve as an introduction to the subject for clinician investigators, physicians and medical scientists in training, practicing clinicians, and other students of medicine.
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Affiliation(s)
- Ayalew Tefferi
- Division of Hematology and Internal Medicine, Mayo Clinic, Rochester, Minn 55905, USA
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Warner EE, Dieckgraefe BK. Application of genome-wide gene expression profiling by high-density DNA arrays to the treatment and study of inflammatory bowel disease. Inflamm Bowel Dis 2002; 8:140-57. [PMID: 11854614 DOI: 10.1097/00054725-200203000-00012] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Identification of factors involved in the initiation, amplification, and perpetuation of the chronic immune response and the identification of markers for the characterization of patient subgroups remain critical objectives for ongoing research in inflammatory bowel disease (IBD). The Human Genome Project and the development of the expressed sequence tag (EST) clone collection and database have made possible a new revolution in gene expression analysis. Instead of measuring one or a few genes, parallel DNA microarrays are capable of simultaneously measuring expression of thousands of genes, providing a glimpse into the logic and functional grouping of gene programs encoded by our genome. Applied to clinical specimens from affected and normal individuals, this methodology has the potential to provide a new level of information about disease pathogenesis not previously possible. Two dominant platforms for the construction of high-density microarrays have emerged: cDNA arrays and GeneChips. The first involves robotic spotting of DNA molecules, often derived from EST clone collections, onto a suitable solid phase matrix such as a glass slide. The second involves direct in situ synthesis of sets of gene-specific oligonucleotides on a silicon wafer by an eloquent derivative of the photolithography process. Both cDNA and oligonucleotide arrays are interrogated by hybridization with a fluorescent-labeled cDNA or cRNA representation of the original tissue mRNA. This enables measurement of the expression levels for thousands of mucosal genes in a single experiment. These technologies have recently become less expensive and more widely accessible to all researchers. This review details the principles and methods behind DNA array technology, data analysis and mining, and potential application to research and treatment of IBD.
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Affiliation(s)
- Elaine E Warner
- Division of Gastroenterology, Washington University School of Medicine, 660 S. Euclid Ave., St. Louis, MO 63110, U.S.A
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Schroeder M, Gilbert D, van Helden J, Noy P. Approaches to visualisation in bioinformatics: from dendrograms to Space Explorer. Inf Sci (N Y) 2001. [DOI: 10.1016/s0020-0255(01)00156-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Application of Regulatory Sequence Analysis and Metabolic Network Analysis to the Interpretation of Gene Expression Data. COMPUTATIONAL BIOLOGY 2001. [DOI: 10.1007/3-540-45727-5_13] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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