1
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Lin Q, Zhang M, Kong Y, Huang Z, Zou Z, Xiong Z, Xie X, Cao Z, Situ W, Dong J, Li S, Zhu X, Huang Y. Risk score = LncRNAs associated with doxorubicin metabolism can be used as molecular markers for immune microenvironment and immunotherapy in non-small cell lung cancer. Heliyon 2023; 9:e13811. [PMID: 36879965 PMCID: PMC9984793 DOI: 10.1016/j.heliyon.2023.e13811] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/09/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
Doxorubicin is the most effective anthracycline chemotherapy drug in the treatment of cancer, and it is an effective single agent in the treatment of non-small cell lung cancer (NSCLC). There is a lack of studies on the differentially expressed doxorubicin metabolism-related lncRNAs in NSCLC. In this study, we extracted related genes from the TCGA database and matched them with lncRNAs. Doxorubicin metabolism-related lncRNA-based gene signatures (DMLncSig) were gradually screened from univariate regression, LASSO regression, and multivariate regression analysis, and the risk score model was constructed. These DMLncSig were subjected to a GO/KEGG analysis. We then used the risk model to construct the TME model and analyze drug sensitivity. The IMvigor 210 immunotherapy model was cited for validation. Eventually, we performed tumor stemness index differences, survival, and clinical correlation analyses.
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Affiliation(s)
- Qianyi Lin
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Ming Zhang
- Department of Physical Medicine and Rehabilitation, Zibo Central Hospital, Zibo 255000, China
| | - Ying Kong
- Department of Clinical Laboratory, Hubei Province No.3 People's Hospital, Wuhan 430030, China
| | - Ziyuan Huang
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Zhuoheng Zou
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Zhuolong Xiong
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Xiaolin Xie
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Zitong Cao
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Wanyi Situ
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Jiaxin Dong
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Shufang Li
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
| | - Yongmei Huang
- The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China
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2
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Zinski AL, Carrion S, Michal JJ, Gartstein MA, Quock RM, Davis JF, Jiang Z. Genome-to-phenome research in rats: progress and perspectives. Int J Biol Sci 2021; 17:119-133. [PMID: 33390838 PMCID: PMC7757052 DOI: 10.7150/ijbs.51628] [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: 08/06/2020] [Accepted: 10/06/2020] [Indexed: 01/07/2023] Open
Abstract
Because of their relatively short lifespan (<4 years), rats have become the second most used model organism to study health and diseases in humans who may live for up to 120 years. First-, second- and third-generation sequencing technologies and platforms have produced increasingly greater sequencing depth and accurate reads, leading to significant advancements in the rat genome assembly during the last 20 years. In fact, whole genome sequencing (WGS) of 47 strains have been completed. This has led to the discovery of genome variants in rats, which have been widely used to detect quantitative trait loci underlying complex phenotypes based on gene, haplotype, and sweep association analyses. DNA variants can also reveal strain, chromosome and gene functional evolutions. In parallel, phenome programs have advanced significantly in rats during the last 15 years and more than 10 databases host genome and/or phenome information. In order to discover the bridges between genome and phenome, systems genetics and integrative genomics approaches have been developed. On the other hand, multiple level information transfers from genome to phenome are executed by differential usage of alternative transcriptional start (ATS) and polyadenylation (APA) sites per gene. We used our own experiments to demonstrate how alternative transcriptome analysis can lead to enrichment of phenome-related causal pathways in rats. Development of advanced genome-to-phenome assays will certainly enhance rats as models for human biomedical research.
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Affiliation(s)
- Amy L. Zinski
- Department of Animal Sciences, Washington State University, Pullman, WA 99164-7620
| | - Shane Carrion
- Department of Animal Sciences, Washington State University, Pullman, WA 99164-7620
| | - Jennifer J. Michal
- Department of Animal Sciences, Washington State University, Pullman, WA 99164-7620
| | - Maria A. Gartstein
- Department of Psychology, Washington State University, Pullman, WA 99164-4820
| | - Raymond M. Quock
- Department of Psychology, Washington State University, Pullman, WA 99164-4820
| | - Jon F. Davis
- Department of Integrative Physiology and Neuroscience, Washington State University, Pullman, WA 99164-7620
| | - Zhihua Jiang
- Department of Animal Sciences, Washington State University, Pullman, WA 99164-7620
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3
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Smith JR, Hayman GT, Wang SJ, Laulederkind SJF, Hoffman MJ, Kaldunski ML, Tutaj M, Thota J, Nalabolu HS, Ellanki SLR, Tutaj MA, De Pons JL, Kwitek AE, Dwinell MR, Shimoyama ME. The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform. Nucleic Acids Res 2020; 48:D731-D742. [PMID: 31713623 PMCID: PMC7145519 DOI: 10.1093/nar/gkz1041] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/21/2019] [Accepted: 10/24/2019] [Indexed: 12/13/2022] Open
Abstract
Formed in late 1999, the Rat Genome Database (RGD, https://rgd.mcw.edu) will be 20 in 2020, the Year of the Rat. Because the laboratory rat, Rattus norvegicus, has been used as a model for complex human diseases such as cardiovascular disease, diabetes, cancer, neurological disorders and arthritis, among others, for >150 years, RGD has always been disease-focused and committed to providing data and tools for researchers doing comparative genomics and translational studies. At its inception, before the sequencing of the rat genome, RGD started with only a few data types localized on genetic and radiation hybrid (RH) maps and offered only a few tools for querying and consolidating that data. Since that time, RGD has expanded to include a wealth of structured and standardized genetic, genomic, phenotypic, and disease-related data for eight species, and a suite of innovative tools for querying, analyzing and visualizing this data. This article provides an overview of recent substantial additions and improvements to RGD's data and tools that can assist researchers in finding and utilizing the data they need, whether their goal is to develop new precision models of disease or to more fully explore emerging details within a system or across multiple systems.
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Affiliation(s)
- Jennifer R Smith
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- To whom correspondence should be addressed. Tel: +1 414 955 8871; Fax: +1 414 955 6595;
| | - G Thomas Hayman
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shur-Jen Wang
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stanley J F Laulederkind
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Matthew J Hoffman
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary L Kaldunski
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Monika Tutaj
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jyothi Thota
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Harika S Nalabolu
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Santoshi L R Ellanki
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Marek A Tutaj
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jeffrey L De Pons
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Anne E Kwitek
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Melinda R Dwinell
- Genomic Sciences and Precision Medicine Center and Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary E Shimoyama
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Jackson SJ, Andrews N, Ball D, Bellantuono I, Gray J, Hachoumi L, Holmes A, Latcham J, Petrie A, Potter P, Rice A, Ritchie A, Stewart M, Strepka C, Yeoman M, Chapman K. Does age matter? The impact of rodent age on study outcomes. Lab Anim 2017; 51:160-169. [PMID: 27307423 PMCID: PMC5367550 DOI: 10.1177/0023677216653984] [Citation(s) in RCA: 192] [Impact Index Per Article: 27.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Rodent models produce data which underpin biomedical research and non-clinical drug trials, but translation from rodents into successful clinical outcomes is often lacking. There is a growing body of evidence showing that improving experimental design is key to improving the predictive nature of rodent studies and reducing the number of animals used in research. Age, one important factor in experimental design, is often poorly reported and can be overlooked. The authors conducted a survey to assess the age used for a range of models, and the reasoning for age choice. From 297 respondents providing 611 responses, researchers reported using rodents most often in the 6-20 week age range regardless of the biology being studied. The age referred to as 'adult' by respondents varied between six and 20 weeks. Practical reasons for the choice of rodent age were frequently given, with increased cost associated with using older animals and maintenance of historical data comparability being two important limiting factors. These results highlight that choice of age is inconsistent across the research community and often not based on the development or cellular ageing of the system being studied. This could potentially result in decreased scientific validity and increased experimental variability. In some cases the use of older animals may be beneficial. Increased scientific rigour in the choice of the age of rodent may increase the translation of rodent models to humans.
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Affiliation(s)
- Samuel J Jackson
- National Centre for the Replacement, Refinement and Reduction of Animals in Research, London, UK
| | - Nick Andrews
- Division of Neurology, Kirby Center for Neurobiology, Boston Children’s Hospital, Boston, US
| | - Doug Ball
- Immunoinflammation TAU, GlaxoSmithKline, Stevenage, UK
| | - Ilaria Bellantuono
- Centre for Integrated Research into Musculoskeletal Ageing, University of Sheffield, Sheffield, UK
| | - James Gray
- Immunoinflammation TAU, GlaxoSmithKline, Stevenage, UK
| | - Lamia Hachoumi
- Pharmacy and Biomolecular Sciences, University of Brighton, Brighton, UK
| | - Alan Holmes
- Centre for Rheumatology, UCL Division of Medicine, Royal Free Campus, London, UK
| | - Judy Latcham
- Laboratory Animal Science, GlaxoSmithKline, Stevenage, UK
| | - Anja Petrie
- Rowett Institute of Nutrition & Health, University of Aberdeen, Aberdeen, UK
| | - Paul Potter
- Disease Models and Translation, Mammalian Genetics Unit, MRC Harwell, Harwell, UK
| | - Andrew Rice
- Pain Research, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Alison Ritchie
- Division of Cancer & Stem Cells, University of Nottingham, Nottingham, UK
| | | | | | - Mark Yeoman
- Pharmacy and Biomolecular Sciences, University of Brighton, Brighton, UK
| | - Kathryn Chapman
- National Centre for the Replacement, Refinement and Reduction of Animals in Research, London, UK
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5
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Martínez-Micaelo N, González-Abuín N, Terra X, Ardévol A, Pinent M, Petretto E, Behmoaras J, Blay M. Identification of a nutrient-sensing transcriptional network in monocytes by using inbred rat models on a cafeteria diet. Dis Model Mech 2016; 9:1231-1239. [PMID: 27483348 PMCID: PMC5087837 DOI: 10.1242/dmm.025528] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2016] [Accepted: 07/04/2016] [Indexed: 01/21/2023] Open
Abstract
Obesity has reached pandemic levels worldwide. The current models of diet-induced obesity in rodents use predominantly high-fat based diets that do not take into account the consumption of variety of highly palatable, energy-dense foods that are prevalent in Western society. We and others have shown that the cafeteria (CAF) diet is a robust and reproducible model of human metabolic syndrome with tissue inflammation in the rat. We have previously shown that inbred rat strains such as Wistar Kyoto (WKY) and Lewis (LEW) show different susceptibilities to CAF diets with distinct metabolic and morphometric profiles. Here, we show a difference in plasma MCP-1 levels and investigate the effect of the CAF diet on peripheral blood monocyte transcriptome, as powerful stress-sensing immune cells, in WKY and LEW rats. We found that 75.5% of the differentially expressed transcripts under the CAF diet were upregulated in WKY rats and were functionally related to the activation of the immune response. Using a gene co-expression network constructed from the genes differentially expressed between CAF diet-fed LEW and WKY rats, we identified acyl-CoA synthetase short-chain family member 2 (Acss2) as a hub gene for a nutrient-sensing cluster of transcripts in monocytes. The Acss2 genomic region is significantly enriched for previously established metabolism quantitative trait loci in the rat. Notably, monocyte expression levels of Acss2 significantly correlated with plasma glucose, triglyceride, leptin and non-esterified fatty acid (NEFA) levels as well as morphometric measurements such as body weight and the total fat following feeding with the CAF diet in the rat. These results show the importance of the genetic background in nutritional genomics and identify inbred rat strains as potential models for CAF-diet-induced obesity. Summary: Feeding with a cafeteria diet (CAF) is a reproducible model of human metabolic syndrome in the rat. By using inbred rat models of nutrigenomics, we have studied the effect of CAF on monocyte transcriptome.
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Affiliation(s)
- Neus Martínez-Micaelo
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
| | - Noemi González-Abuín
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
| | - Ximena Terra
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
| | - Ana Ardévol
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
| | - Montserrat Pinent
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
| | - Enrico Petretto
- MRC Clinical Sciences Centre, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK Duke-NUS Graduate Medical School Singapore, 8 College Road, Singapore 169857, Republic of Singapore
| | - Jacques Behmoaras
- Centre of Complement and Inflammation Research, Imperial College London, Du Cane Road, London W12 0NN, UK
| | - Mayte Blay
- Mobiofood Research Group, Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili, 43003 Tarragona, Spain
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6
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Fernandez-Ricaud L, Kourtchenko O, Zackrisson M, Warringer J, Blomberg A. PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics. BMC Bioinformatics 2016; 17:249. [PMID: 27334112 PMCID: PMC4917999 DOI: 10.1186/s12859-016-1134-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 06/09/2016] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Phenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology. RESULTS To automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics. Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis. CONCLUSIONS PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.
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Affiliation(s)
- Luciano Fernandez-Ricaud
- />Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Göteborg, Sweden
| | - Olga Kourtchenko
- />Department of Marine Sciences, University of Gothenburg, P.O. Box 461, SE 405 30 Göteborg, Sweden
| | - Martin Zackrisson
- />Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Göteborg, Sweden
| | - Jonas Warringer
- />Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Göteborg, Sweden
- />Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432 Ås, Norway
| | - Anders Blomberg
- />Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390 Göteborg, Sweden
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7
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Alonso R, Salavert F, Garcia-Garcia F, Carbonell-Caballero J, Bleda M, Garcia-Alonso L, Sanchis-Juan A, Perez-Gil D, Marin-Garcia P, Sanchez R, Cubuk C, Hidalgo MR, Amadoz A, Hernansaiz-Ballesteros RD, Alemán A, Tarraga J, Montaner D, Medina I, Dopazo J. Babelomics 5.0: functional interpretation for new generations of genomic data. Nucleic Acids Res 2015; 43:W117-21. [PMID: 25897133 PMCID: PMC4489263 DOI: 10.1093/nar/gkv384] [Citation(s) in RCA: 99] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 04/11/2015] [Indexed: 02/02/2023] Open
Abstract
Babelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.
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Affiliation(s)
- Roberto Alonso
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Computational Genomics Chair, Bull-CIPF, Valencia, 46012, Spain
| | - Francisco Salavert
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - Francisco Garcia-Garcia
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Jose Carbonell-Caballero
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Marta Bleda
- Department of Medicine, University of Cambridge, School of Clinical Medicine, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Luz Garcia-Alonso
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Alba Sanchis-Juan
- Fundación Investigación Clínico de Valencia-INCLIVA, Valencia, 46010, Spain
| | - Daniel Perez-Gil
- Fundación Investigación Clínico de Valencia-INCLIVA, Valencia, 46010, Spain
| | - Pablo Marin-Garcia
- Fundación Investigación Clínico de Valencia-INCLIVA, Valencia, 46010, Spain
| | - Ruben Sanchez
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Functional Genomics Node, (INB) at CIPF, Valencia, 46012, Spain
| | - Cankut Cubuk
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Marta R Hidalgo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Alicia Amadoz
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | | | - Alejandro Alemán
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
| | - Joaquin Tarraga
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - David Montaner
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
| | - Ignacio Medina
- HPC Services, University of Cambridge, Cambridge, CB3 0RB UK
| | - Joaquin Dopazo
- Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain Computational Genomics Chair, Bull-CIPF, Valencia, 46012, Spain Bioinformatics of Rare Diseases (BIER), CIBER de Enfermedades Raras (CIBERER), Valencia, 46012, Spain Functional Genomics Node, (INB) at CIPF, Valencia, 46012, Spain
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8
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van Triest HJW, Chen D, Ji X, Qi S, Li-Ling J. PhenOMIM: an OMIM-based secondary database purported for phenotypic comparison. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:3589-92. [PMID: 22255115 DOI: 10.1109/iembs.2011.6090600] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Phenotypic comparison may provide crucial information for obtaining insights into molecular interactions underlying various diseases. However, few attempts have been made to systematically analyze the phenotypes of hereditary disorders, mainly owing to the poor quality of text descriptions and lack of a unified system of descriptors. Here we present a secondary database, PHENOMIM, for translating the phenotypic data obtained from the Online Mendelian Inheritance in Man (OMIM) database into a structured form. Moreover, a web interface has also been developed for visualizing the data and related information from the OMIM and PhenOMIM databases. The data is freely available online for reviewing and commenting purposes and can be found at http://faculty.neu.edu.cn/bmie/han/PhenOMIM/.
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Affiliation(s)
- Han J W van Triest
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110003, China.
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9
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Bruckskotten M, Looso M, Reinhardt R, Braun T, Borchardt T. Newt-omics: a comprehensive repository for omics data from the newt Notophthalmus viridescens. Nucleic Acids Res 2011; 40:D895-900. [PMID: 22039101 PMCID: PMC3245081 DOI: 10.1093/nar/gkr873] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Notophthalmus viridescens, a member of the salamander family is an excellent model organism to study regenerative processes due to its unique ability to replace lost appendages and to repair internal organs. Molecular insights into regenerative events have been severely hampered by the lack of genomic, transcriptomic and proteomic data, as well as an appropriate database to store such novel information. Here, we describe ‘Newt-omics’ (http://newt-omics.mpi-bn.mpg.de), a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centred database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ∼50 000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13 810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise.
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Affiliation(s)
- Marc Bruckskotten
- Department of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231 Bad Nauheim, Germany
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10
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Suzuki S, Pitchakarn P, Takeshita K, Asamoto M, Takahashi S, Sato S, Shirai T. Roles for rat hepatocyte malignant transforming factor (HMTF) in late stage of hepatocarcinogenesis. Toxicol Pathol 2011; 39:1084-90. [PMID: 21934139 DOI: 10.1177/0192623311422077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In a previous study, to identify genes of importance for hepatocellular carcinogenesis, and especially for processes involved in malignant transformation, the authors investigated differences in gene expression between adenomas and carcinomas by DNA microarray. In the present study, the authors investigated AW434047, one of the sequences that was upregulated in carcinomas. The investigation led to the identification of a novel gene, which the authors named hepatocyte malignant transforming factor (HMTF), of unknown function whose expression was increased in hepatocellular carcinomas. Northern blot and in situ hybridization also demonstrated high levels of HMTF in rat hepatocellular carcinoma (HCC) cell lines, lymphocytes in the spleen, colon mucosal epithelia, spermatocytes, and granule cells of the hippocampus. Reduction of HMTF by RNA interference (RNAi) in N1 cells, an HCC cell line, caused suppression of cell proliferation, invasion, and migration. Suppression of proliferation appeared to be due to cell cycle arrest without increased apoptosis. Decreased HMTF expression resulted in down-regulation of STAT3, PCNA, and cyclin D1 and upregulation of p27. These results suggest that HMTF is a new marker for rat HCC and is involved in HCC cell proliferation and may also be linked to cell proliferation in the spleen, colon, brain, and testis.
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Affiliation(s)
- Shugo Suzuki
- Nagoya City University Graduate School of Medical Sciences, Aichi, Japan
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Inselman AL, Hansen DK, Lee HY, Nakamura N, Ning B, Monteiro JP, Varma V, Kaput J. Assessment of research models for testing gene-environment interactions. Eur J Pharmacol 2011; 668 Suppl 1:S108-16. [PMID: 21816149 DOI: 10.1016/j.ejphar.2011.05.084] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Revised: 05/16/2011] [Accepted: 05/22/2011] [Indexed: 12/16/2022]
Abstract
Throughout the last century, possible effects of exposure to toxicants, nutrients or drugs were examined primarily by studies of groups or populations. Individual variation in responses was acknowledged but could not be analyzed due to lack of information or tools to analyze individual genetic make-ups and lifestyle factors such as diet and activity. The Human Genome, Haplotype Map, 1000Genomes, and Human Variome Projects are identifying and cataloging the variation found within humans. Advances in DNA sequencing technologies will soon permit the characterization of individual genomes in clinical and basic research studies, thus allowing associations to be made between an individual genotype and the response to a particular exposure. Such knowledge and tools have generated a significant challenge for scientists: to design and conduct research studies that account for individual genetic variation. However, before these studies are done in humans, they will be performed in various in vivo and in vitro models. The advantages and disadvantages of some of the model test systems that are being used or developed in relation to individual genetic make-up and responses to xenobiotics are discussed.
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Affiliation(s)
- Amy L Inselman
- Division of Personalized Nutrition and Medicine, NCTR/FDA, 3900 NCTR Rd., Jefferson, AR 72079, United States.
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Pérez-Gracia JL, Gúrpide A, Ruiz-Ilundain MG, Alfaro Alegría C, Colomer R, García-Foncillas J, Melero Bermejo I. Selection of extreme phenotypes: the role of clinical observation in translational research. Clin Transl Oncol 2010; 12:174-80. [PMID: 20231122 DOI: 10.1007/s12094-010-0487-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Systematic collection of phenotypes and their correlation with molecular data has been proposed as a useful method to advance in the study of disease. Although some databases for animal species are being developed, progress in humans is slow, probably due to the multifactorial origin of many human diseases and to the intricacy of accurately classifying phenotypes, among other factors. An alternative approach has been to identify and to study individuals or families with very characteristic, clinically relevant phenotypes. This strategy has shown increased efficiency to identify the molecular features underlying such phenotypes. While on most occasions the subjects selected for these studies presented harmful phenotypes, a few studies have been performed in individuals with very favourable phenotypes. The consistent results achieved suggest that it seems logical to further develop this strategy as a methodology to study human disease, including cancer. The identification and the study with high-throughput techniques of individuals showing a markedly decreased risk of developing cancer or of cancer patients presenting either an unusually favourable prognosis or striking responses following a specific treatment, might be promising ways to maximize the yield of this approach and to reveal the molecular causes that explain those phenotypes and thus highlight useful therapeutic targets. This manuscript reviews the current status of selection of extreme phenotypes in cancer research and provides directions for future development of this methodology.
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Affiliation(s)
- José Luis Pérez-Gracia
- Medical Oncology Department, Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain.
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Kutlu B, Burdick D, Baxter D, Rasschaert J, Flamez D, Eizirik DL, Welsh N, Goodman N, Hood L. Detailed transcriptome atlas of the pancreatic beta cell. BMC Med Genomics 2009; 2:3. [PMID: 19146692 PMCID: PMC2635377 DOI: 10.1186/1755-8794-2-3] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2008] [Accepted: 01/15/2009] [Indexed: 01/21/2023] Open
Abstract
Background Gene expression patterns provide a detailed view of cellular functions. Comparison of profiles in disease vs normal conditions provides insights into the processes underlying disease progression. However, availability and integration of public gene expression datasets remains a major challenge. The aim of the present study was to explore the transcriptome of pancreatic islets and, based on this information, to prepare a comprehensive and open access inventory of insulin-producing beta cell gene expression, the Beta Cell Gene Atlas (BCGA). Methods We performed Massively Parallel Signature Sequencing (MPSS) analysis of human pancreatic islet samples and microarray analyses of purified rat beta cells, alpha cells and INS-1 cells, and compared the information with available array data in the literature. Results MPSS analysis detected around 7600 mRNA transcripts, of which around a third were of low abundance. We identified 2000 and 1400 transcripts that are enriched/depleted in beta cells compared to alpha cells and INS-1 cells, respectively. Microarray analysis identified around 200 transcription factors that are differentially expressed in either beta or alpha cells. We reanalyzed publicly available gene expression data and integrated these results with the new data from this study to build the BCGA. The BCGA contains basal (untreated conditions) gene expression level estimates in beta cells as well as in different cell types in human, rat and mouse pancreas. Hierarchical clustering of expression profile estimates classify cell types based on species while beta cells were clustered together. Conclusion Our gene atlas is a valuable source for detailed information on the gene expression distribution in beta cells and pancreatic islets along with insulin producing cell lines. The BCGA tool, as well as the data and code used to generate the Atlas are available at the T1Dbase website (T1DBase.org).
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Affiliation(s)
- Burak Kutlu
- Institute for Systems Biology, Seattle, WA, USA.
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14
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Dwinell MR, Worthey EA, Shimoyama M, Bakir-Gungor B, DePons J, Laulederkind S, Lowry T, Nigram R, Petri V, Smith J, Stoddard A, Twigger SN, Jacob HJ. The Rat Genome Database 2009: variation, ontologies and pathways. Nucleic Acids Res 2008; 37:D744-9. [PMID: 18996890 PMCID: PMC2686558 DOI: 10.1093/nar/gkn842] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The Rat Genome Database (RGD, http://rgd.mcw.edu) was developed to provide a core resource for rat researchers combining genetic, genomic, pathway, phenotype and strain information with a focus on disease. RGD users are provided with access to structured and curated data from the molecular level through to the level of the whole organism, including the variations associated with disease phenotypes. To fully support use of the rat as a translational model for biological systems and human disease, RGD continues to curate these datasets while enhancing and developing tools to allow efficient and effective access to the data in a variety of formats including linear genome viewers, pathway diagrams and biological ontologies. To support pathophysiological analysis of data, RGD Disease Portals provide an entryway to integrated gene, QTL and strain data specific to a particular disease. In addition to tool and content development and maintenance, RGD promotes rat research and provides user education by creating and disseminating tutorials on the curated datasets, submission processes, and tools available at RGD. By curating, storing, integrating, visualizing and promoting rat data, RGD ensures that the investment made into rat genomics and genetics can be leveraged by all interested investigators.
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Affiliation(s)
- Melinda R Dwinell
- Department of Physiology and Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA.
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15
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Affiliation(s)
- Xosé M Fernández-Suárez
- European Bioinformatics Institute, Ensembl Group, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
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16
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Agrawal S, Dimitrova N, Nathan P, Udayakumar K, Lakshmi SS, Sriram S, Manjusha N, Sengupta U. T2D-Db: an integrated platform to study the molecular basis of Type 2 diabetes. BMC Genomics 2008; 9:320. [PMID: 18605991 PMCID: PMC2491641 DOI: 10.1186/1471-2164-9-320] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2008] [Accepted: 07/07/2008] [Indexed: 11/18/2022] Open
Abstract
Background Type 2 Diabetes Mellitus (T2DM) is a non insulin dependent, complex trait disease that develops due to genetic predisposition and environmental factors. The advanced stage in type 2 diabetes mellitus leads to several micro and macro vascular complications like nephropathy, neuropathy, retinopathy, heart related problems etc. Studies performed on the genetics, biochemistry and molecular biology of this disease to understand the pathophysiology of type 2 diabetes mellitus has led to the generation of a surfeit of data on candidate genes and related aspects. The research is highly progressive towards defining the exact etiology of this disease. Results T2D-Db (Type 2 diabetes Database) is a comprehensive web resource, which provides integrated and curated information on almost all known molecular components involved in the pathogenesis of type 2 diabetes mellitus in the three widely studied mammals namely human, mouse and rat. Information on candidate genes, SNPs (Single Nucleotide Polymorphism) in candidate genes or candidate regions, genome wide association studies (GWA), tissue specific gene expression patterns, EST (Expressed Sequence Tag) data, expression information from microarray data, pathways, protein-protein interactions and disease associated risk factors or complications have been structured in this on line resource. Conclusion Information available in T2D-Db provides an integrated platform for the better molecular level understanding of type 2 diabetes mellitus and its pathogenesis. Importantly, the resource facilitates graphical presentation of the gene/genome wide map of SNP markers and protein-protein interaction networks, besides providing the heat map diagram of the selected gene(s) in an organism across microarray expression experiments from either single or multiple studies. These features aid to the data interpretation in an integrative way. T2D-Db is to our knowledge the first publicly available resource that can cater to the needs of researchers working on different aspects of type 2 diabetes mellitus.
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Affiliation(s)
- Shipra Agrawal
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India.
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17
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Tárraga J, Medina I, Carbonell J, Huerta-Cepas J, Minguez P, Alloza E, Al-Shahrour F, Vegas-Azcárate S, Goetz S, Escobar P, Garcia-Garcia F, Conesa A, Montaner D, Dopazo J. GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Res 2008; 36:W308-14. [PMID: 18508806 PMCID: PMC2447723 DOI: 10.1093/nar/gkn303] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.
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Affiliation(s)
- Joaquín Tárraga
- Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Autopista del Saler 16, E46013, Valencia, Spain
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18
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Bryda EC, Riley LK. Multiplex microsatellite marker panels for genetic monitoring of common rat strains. JOURNAL OF THE AMERICAN ASSOCIATION FOR LABORATORY ANIMAL SCIENCE : JAALAS 2008; 47:37-41. [PMID: 18459711 PMCID: PMC2654014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 09/05/2007] [Revised: 09/26/2007] [Accepted: 10/08/2007] [Indexed: 05/26/2023]
Abstract
Because microsatellite markers have a high degree of genetic variability, they are an effective tool for genetic monitoring. We have developed a genotyping panel containing 87 microsatellite markers that are polymorphic among commonly used inbred rat strains, including ACI, Fischer 344, Lewis, Brown Norway, Wistar-Furth, and Wistar-Kyoto. The markers are located at approximately 15- to 20-cM intervals along each of the 20 autosomes. By using fluorescently labeled primers and multiplex PCR analysis, the entire genome can be assayed with only 8 reactions. The resulting amplicons from these reactions can be distinguished from one another by both their size and the fluorescent dye associated with them. Amplicons are analyzed and allele sizes are determined by using automated capillary-based instrumentation. These multiplex panels provide a cost-effective and rapid method for genetic monitoring for applications ranging from assessing genetic contamination in a rat colony to moving mutations from one genetic background to another by using a speed congenic approach.
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Affiliation(s)
- Elizabeth C Bryda
- Department of Veterinary Pathobiology, Research Animal Diagnostic Laboratory, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA.
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Kawazu M, Yamamoto G, Yoshimi M, Yamamoto K, Asai T, Ichikawa M, Seo S, Nakagawa M, Chiba S, Kurokawa M, Ogawa S. Expression profiling of immature thymocytes revealed a novel homeobox gene that regulates double-negative thymocyte development. THE JOURNAL OF IMMUNOLOGY 2007; 179:5335-45. [PMID: 17911620 DOI: 10.4049/jimmunol.179.8.5335] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Intrathymic development of CD4/CD8 double-negative (DN) thymocytes can be tracked by well-defined chronological subsets of thymocytes, and is an ideal target of gene expression profiling analysis to clarify the genetic basis of mature T cell production, by which differentiation of immature thymocytes is investigated in terms of gene expression profiles. In this study, we show that development of murine DN thymocytes is predominantly regulated by largely repressive rather than inductive activities of transcriptions, where lineage-promiscuous gene expression in immature thymocytes is down-regulated during their differentiation. Functional mapping of genes showing common temporal expression profiles implicates previously uncharacterized gene regulations that may be relevant to early thymocytes development. A small minority of genes is transiently expressed in the CD44(low)CD25(+) subset of DN thymocytes, from which we identified a novel homeobox gene, Duxl, whose expression is up-regulated by Runx1. Duxl promotes the transition from CD44(high)CD25(+) to CD44(low)CD25(+) in DN thymocytes, while constitutive expression of Duxl inhibits expression of TCR beta-chains and leads to impaired beta selection and greatly reduced production of CD4/CD8 double-positive thymocytes, indicating its critical roles in DN thymocyte development.
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Affiliation(s)
- Masahito Kawazu
- Department of Hematology and Oncology, University of Tokyo, Japan
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20
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Molecular and genetic association of interleukin-6 in tacrine-induced hepatotoxicity. Pharmacogenet Genomics 2007; 17:961-72. [DOI: 10.1097/fpc.0b013e3282f00919] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Affiliation(s)
- Dmitrij Frishman
- Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenchaftszentrum Weihenstephan, 85350 Freising, Germany
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22
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Thybaud V, Le Fevre AC, Boitier E. Application of toxicogenomics to genetic toxicology risk assessment. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2007; 48:369-79. [PMID: 17567850 DOI: 10.1002/em.20304] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.
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Affiliation(s)
- Véronique Thybaud
- Drug Safety Evaluation, Sanofi Aventis R&D, Vitry sur Seine, France.
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Abstract
The recent completion of the Human Genome Project has made possible a high-throughput "systems approach" for accelerating the elucidation of molecular underpinnings of human diseases, and subsequent derivation of molecular-based strategies to more effectively prevent, diagnose, and treat these diseases. Although altered phenotypes are among the most reliable manifestations of altered gene functions, research using systematic analysis of phenotype relationships to study human biology is still in its infancy. This article focuses on the emerging field of high-throughput phenotyping (HTP) phenomics research, which aims to capitalize on novel high-throughput computation and informatics technology developments to derive genomewide molecular networks of genotype-phenotype associations, or "phenomic associations." The HTP phenomics research field faces the challenge of technological research and development to generate novel tools in computation and informatics that will allow researchers to amass, access, integrate, organize, and manage phenotypic databases across species and enable genomewide analysis to associate phenotypic information with genomic data at different scales of biology. Key state-of-the-art technological advancements critical for HTP phenomics research are covered in this review. In particular, we highlight the power of computational approaches to conduct large-scale phenomics studies.
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Affiliation(s)
- Yves A Lussier
- Section of Genetic Medicine, Department of Medicine, University of Chicago,Chicago, Illinois 60637, USA.
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Thomas PD, Mi H, Lewis S. Ontology annotation: mapping genomic regions to biological function. Curr Opin Chem Biol 2007; 11:4-11. [PMID: 17208035 DOI: 10.1016/j.cbpa.2006.11.039] [Citation(s) in RCA: 51] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2006] [Accepted: 11/29/2006] [Indexed: 10/23/2022]
Abstract
With numerous whole genomes now in hand, and experimental data about genes and biological pathways on the increase, a systems approach to biological research is becoming essential. Ontologies provide a formal representation of knowledge that is amenable to computational as well as human analysis, an obvious underpinning of systems biology. Mapping function to gene products in the genome consists of two, somewhat intertwined enterprises: ontology building and ontology annotation. Ontology building is the formal representation of a domain of knowledge; ontology annotation is association of specific genomic regions (which we refer to simply as 'genes', including genes and their regulatory elements and products such as proteins and functional RNAs) to parts of the ontology. We consider two complementary representations of gene function: the Gene Ontology (GO) and pathway ontologies. GO represents function from the gene's eye view, in relation to a large and growing context of biological knowledge at all levels. Pathway ontologies represent function from the point of view of biochemical reactions and interactions, which are ordered into networks and causal cascades. The more mature GO provides an example of ontology annotation: how conclusions from the scientific literature and from evolutionary relationships are converted into formal statements about gene function. Annotations are made using a variety of different types of evidence, which can be used to estimate the relative reliability of different annotations.
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Affiliation(s)
- Paul D Thomas
- Evolutionary Systems Biology Group, Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA.
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Hulbert EM, Smink LJ, Adlem EC, Allen JE, Burdick DB, Burren OS, Cassen VM, Cavnor CC, Dolman GE, Flamez D, Friery KF, Healy BC, Killcoyne SA, Kutlu B, Schuilenburg H, Walker NM, Mychaleckyj J, Eizirik DL, Wicker LS, Todd JA, Goodman N. T1DBase: integration and presentation of complex data for type 1 diabetes research. Nucleic Acids Res 2006; 35:D742-6. [PMID: 17169983 PMCID: PMC1781218 DOI: 10.1093/nar/gkl933] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
T1DBase () [Smink et al. (2005) Nucleic Acids Res., 33, D544–D549; Burren et al. (2004) Hum. Genomics, 1, 98–109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599–1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498–2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in β cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in β cells. T1DMart is a query tool for markers and genotypes. PosterPages are ‘home pages’ about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.
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Wiwatwattana N, Landau CM, Cope GJ, Harp GA, Kumar A. Organelle DB: an updated resource of eukaryotic protein localization and function. Nucleic Acids Res 2006; 35:D810-4. [PMID: 17130152 PMCID: PMC1716721 DOI: 10.1093/nar/gkl1000] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Organelle DB () is a web-accessible relational database presenting a supplemented catalog of organelle-localized proteins and major protein complexes. Since its release in 2004, Organelle DB has grown by 20% to encompass over 30 000 proteins from 138 eukaryotic organisms. Each protein in Organelle DB is presented with its subcellular localization, primary sequence and a detailed description of its function, as available. All records in Organelle DB have been annotated using controlled vocabulary from the Gene Ontology consortium. Protein localization data are inherently visual, and Organelle DB is a significant repository of biological images, housing 1500 micrographs of yeast cells carrying stained proteins. Furthermore, we report here the development of Organelle View, an extension of Organelle DB for the interactive visualization of organelles and subcellular structures in the budding yeast Saccharomyces cerevisiae. Organelle View offers a dimensional representation of a yeast cell; users can search Organelle View for proteins of interest, and the organelles housing these proteins will be highlighted in the cell image. Among other applications, Organelle View may serve as an educational aid engaging introductory biology students through a visually ‘fun’ interface. Organelle View can be accessed from the Organelle DB home page or directly at .
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Affiliation(s)
| | | | | | | | - Anuj Kumar
- To whom correspondence should be addressed. Tel: +1 734 647 8060; Fax: +1 734 647 9702;
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Smith CM, Finger JH, Hayamizu TF, McCright IJ, Eppig JT, Kadin JA, Richardson JE, Ringwald M. The mouse Gene Expression Database (GXD): 2007 update. Nucleic Acids Res 2006; 35:D618-23. [PMID: 17130151 PMCID: PMC1716716 DOI: 10.1093/nar/gkl1003] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The Gene Expression Database (GXD) provides the scientific community with an extensive and easily searchable database of gene expression information about the mouse. Its primary emphasis is on developmental studies. By integrating different types of expression data, GXD aims to provide comprehensive information about expression patterns of transcripts and proteins in wild-type and mutant mice. Integration with the other Mouse Genome Informatics (MGI) databases places the gene expression information in the context of genetic, sequence, functional and phenotypic information, enabling valuable insights into the molecular biology that underlies developmental and disease processes. In recent years the utility of GXD has been greatly enhanced by a large increase in data content, obtained from the literature and provided by researchers doing large-scale in situ and cDNA screens. In addition, we have continued to refine our query and display features to make it easier for users to interrogate the data. GXD is available through the MGI web site at or directly at .
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Affiliation(s)
| | | | | | | | | | | | | | - Martin Ringwald
- To whom correspondence should be addressed. Tel: +1 207 288 6436; Fax: +1 207 288 6132;
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methBLAST and methPrimerDB: web-tools for PCR based methylation analysis. BMC Bioinformatics 2006; 7:496. [PMID: 17094804 PMCID: PMC1654196 DOI: 10.1186/1471-2105-7-496] [Citation(s) in RCA: 32] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2006] [Accepted: 11/09/2006] [Indexed: 12/24/2022] Open
Abstract
Background DNA methylation plays an important role in development and tumorigenesis by epigenetic modification and silencing of critical genes. The development of PCR-based methylation assays on bisulphite modified DNA heralded a breakthrough in speed and sensitivity for gene methylation analysis. Despite this technological advancement, these approaches require a cumbersome gene by gene primer design and experimental validation. Bisulphite DNA modification results in sequence alterations (all unmethylated cytosines are converted into uracils) and a general sequence complexity reduction as cytosines become underrepresented. Consequently, standard BLAST sequence homology searches cannot be applied to search for specific methylation primers. Results To address this problem we developed methBLAST, a sequence similarity search program, based on the original BLAST algorithm but querying in silico bisulphite modified genome sequences to evaluate oligonucleotide sequence similarities. Apart from the primer specificity analysis tool, we have also developed a public database termed methPrimerDB for the storage and retrieval of validated PCR based methylation assays. The web interface allows free public access to perform methBLAST searches or database queries and to submit user based information. Database records can be searched by gene symbol, nucleotide sequence, analytical method used, Entrez Gene or methPrimerDB identifier, and submitter's name. Each record contains a link to Entrez Gene and PubMed to retrieve additional information on the gene, its genomic context and the article in which the methylation assay was described. To assure and maintain data integrity and accuracy, the database is linked to other reference databases. Currently, the database contains primer records for the most popular PCR-based methylation analysis methods to study human, mouse and rat epigenetic modifications. methPrimerDB and methBLAST are available at and . Conclusion We have developed two integrated and freely available web-tools for PCR based methylation analysis. methBLAST allows in silico assessment of primer specificity in PCR based methylation assays that can be stored in the methPrimerDB database, which provides a search portal for validated methylation assays.
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Liu Y, Li J, Sam L, Goh CS, Gerstein M, Lussier YA. An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits. PLoS Comput Biol 2006; 2:e159. [PMID: 17112314 PMCID: PMC1636675 DOI: 10.1371/journal.pcbi.0020159] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2006] [Accepted: 10/10/2006] [Indexed: 11/18/2022] Open
Abstract
With mounting availability of genomic and phenotypic databases, data integration and mining become increasingly challenging. While efforts have been put forward to analyze prokaryotic phenotypes, current computational technologies either lack high throughput capacity for genomic scale analysis, or are limited in their capability to integrate and mine data across different scales of biology. Consequently, simultaneous analysis of associations among genomes, phenotypes, and gene functions is prohibited. Here, we developed a high throughput computational approach, and demonstrated for the first time the feasibility of integrating large quantities of prokaryotic phenotypes along with genomic datasets for mining across multiple scales of biology (protein domains, pathways, molecular functions, and cellular processes). Applying this method over 59 fully sequenced prokaryotic species, we identified genetic basis and molecular mechanisms underlying the phenotypes in bacteria. We identified 3,711 significant correlations between 1,499 distinct Pfam and 63 phenotypes, with 2,650 correlations and 1,061 anti-correlations. Manual evaluation of a random sample of these significant correlations showed a minimal precision of 30% (95% confidence interval: 20%-42%; n = 50). We stratified the most significant 478 predictions and subjected 100 to manual evaluation, of which 60 were corroborated in the literature. We furthermore unveiled 10 significant correlations between phenotypes and KEGG pathways, eight of which were corroborated in the evaluation, and 309 significant correlations between phenotypes and 166 GO concepts evaluated using a random sample (minimal precision = 72%; 95% confidence interval: 60%-80%; n = 50). Additionally, we conducted a novel large-scale phenomic visualization analysis to provide insight into the modular nature of common molecular mechanisms spanning multiple biological scales and reused by related phenotypes (metaphenotypes). We propose that this method elucidates which classes of molecular mechanisms are associated with phenotypes or metaphenotypes and holds promise in facilitating a computable systems biology approach to genomic and biomedical research.
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Affiliation(s)
- Yang Liu
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
- Center for Biomedical Informatics, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Jianrong Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
- Center for Biomedical Informatics, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Lee Sam
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
- Center for Biomedical Informatics, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Chern-Sing Goh
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America
- Department of Computer Science, Yale University, New Haven, Connecticut, United States of America
- * To whom correspondence should be addressed. E-mail: (MG); (YAL)
| | - Yves A Lussier
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
- Center for Biomedical Informatics, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
- * To whom correspondence should be addressed. E-mail: (MG); (YAL)
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Penkett CJ, Morris JA, Wood V, Bähler J. YOGY: a web-based, integrated database to retrieve protein orthologs and associated Gene Ontology terms. Nucleic Acids Res 2006; 34:W330-4. [PMID: 16845020 PMCID: PMC1538793 DOI: 10.1093/nar/gkl311] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We present YOGY a web-based resource for orthologous proteins from nine eukaryotic organisms: Homo sapiens, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, Drosophila melanogaster, Caenorhabditis elegans, Plasmodium falciparum, Schizosaccharomyces pombe and Saccharomyces cerevisiae. Using a gene name from any of these organisms as a query, this database provides comprehensive, combined information on orthologs in other species using data from five independent resources: KOGs, Inparanoid, HomoloGene, OrthoMCL and a table of curated fission and budding yeast orthologs. Associated Gene Ontology (GO) terms of orthologs can also be retrieved for functional inference. Integrating these different and complementary datasets provides a straightforward tool to identify known and predicted orthologs of proteins from a variety of species. This resource should be useful for bench scientists looking for functional clues for their genes of interest as well as for curators looking for information that can be transferred based on orthology and for rapidly identifying the relevant GO terms as an aid to literature curation. YOGY is accessible online at http://www.sanger.ac.uk/PostGenomics/S_pombe/YOGY/.
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Affiliation(s)
| | | | | | - Jürg Bähler
- To whom correspondence should be addressed. Tel: +44 0 1223 496948; Fax: +44 0 1223 496802;
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31
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Fundel K, Zimmer R. Gene and protein nomenclature in public databases. BMC Bioinformatics 2006; 7:372. [PMID: 16899134 PMCID: PMC1560172 DOI: 10.1186/1471-2105-7-372] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2006] [Accepted: 08/09/2006] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Frequently, several alternative names are in use for biological objects such as genes and proteins. Applications like manual literature search, automated text-mining, named entity identification, gene/protein annotation, and linking of knowledge from different information sources require the knowledge of all used names referring to a given gene or protein. Various organism-specific or general public databases aim at organizing knowledge about genes and proteins. These databases can be used for deriving gene and protein name dictionaries. So far, little is known about the differences between databases in terms of size, ambiguities and overlap. RESULTS We compiled five gene and protein name dictionaries for each of the five model organisms (yeast, fly, mouse, rat, and human) from different organism-specific and general public databases. We analyzed the degree of ambiguity of gene and protein names within and between dictionaries, to a lexicon of common English words and domain-related non-gene terms, and we compared different data sources in terms of size of extracted dictionaries and overlap of synonyms between those. The study shows that the number of genes/proteins and synonyms covered in individual databases varies significantly for a given organism, and that the degree of ambiguity of synonyms varies significantly between different organisms. Furthermore, it shows that, despite considerable efforts of co-curation, the overlap of synonyms in different data sources is rather moderate and that the degree of ambiguity of gene names with common English words and domain-related non-gene terms varies depending on the considered organism. CONCLUSION In conclusion, these results indicate that the combination of data contained in different databases allows the generation of gene and protein name dictionaries that contain significantly more used names than dictionaries obtained from individual data sources. Furthermore, curation of combined dictionaries considerably increases size and decreases ambiguity. The entries of the curated synonym dictionary are available for manual querying, editing, and PubMed- or Google-search via the ProThesaurus-wiki. For automated querying via custom software, we offer a web service and an exemplary client application.
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Affiliation(s)
- Katrin Fundel
- Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany
| | - Ralf Zimmer
- Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany
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N. Twigger S, S. Smith J, Zuniga-Meyer A, Bromberg SK. Exploring Phenotypic Data at the Rat Genome Database. ACTA ACUST UNITED AC 2006; Chapter 1:Unit 1.14. [DOI: 10.1002/0471250953.bi0114s14] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Simon N. Twigger
- Human and Molecular Genetics Center, Medical College of Wisconsin; Milwaukee Wisconsin
| | - Jennifer S. Smith
- Human and Molecular Genetics Center, Medical College of Wisconsin; Milwaukee Wisconsin
| | - Angela Zuniga-Meyer
- Human and Molecular Genetics Center, Medical College of Wisconsin; Milwaukee Wisconsin
| | - Susan K. Bromberg
- Human and Molecular Genetics Center, Medical College of Wisconsin; Milwaukee Wisconsin
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Star KV, Song Q, Zhu A, Böttinger EP. QTL MatchMaker: a multi-species quantitative trait loci (QTL) database and query system for annotation of genes and QTL. Nucleic Acids Res 2006; 34:D586-9. [PMID: 16381937 PMCID: PMC1347390 DOI: 10.1093/nar/gkj027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Identifying genes that underlie quantitative trait loci (QTL) is a challenging task. Here, we present a new QTL software system, named QTL MatchMaker. The system is designed to integrate and mine QTL information across human, mouse and rat genomes and to annotate functional genomic data. It combines and organizes information from relevant public databases and publications and integrates QTL, physical, genetic and cytogenetic maps across human, mouse and rat. To make this application available to the research community we have developed a website for high-throughput mapping of expressed sequences to QTL and for selection of candidate genes in the physiological genomics context of complex traits. QTL MatchMaker is accessible at
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Affiliation(s)
- Kremena V. Star
- Albert Einstein College of MedicineOne Gustave L. Levy Place, Box 1243, New York, NY 10029, USA
- Mount Sinai School of MedicineOne Gustave L. Levy Place, Box 1243, New York, NY 10029, USA
- To whom correspondence should be addressed. Tel: +1 212 241 1884; Fax: +1 212 849 2643;
| | - Quingbin Song
- Mount Sinai School of MedicineOne Gustave L. Levy Place, Box 1243, New York, NY 10029, USA
| | - Andy Zhu
- Mount Sinai School of MedicineOne Gustave L. Levy Place, Box 1243, New York, NY 10029, USA
| | - Erwin P. Böttinger
- Mount Sinai School of MedicineOne Gustave L. Levy Place, Box 1243, New York, NY 10029, USA
- To whom correspondence should be addressed. Tel: +1 212 241 1884; Fax: +1 212 849 2643;
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Krull M, Pistor S, Voss N, Kel A, Reuter I, Kronenberg D, Michael H, Schwarzer K, Potapov A, Choi C, Kel-Margoulis O, Wingender E. TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res 2006; 34:D546-51. [PMID: 16381929 PMCID: PMC1347469 DOI: 10.1093/nar/gkj107] [Citation(s) in RCA: 120] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
TRANSPATH is a database about signal transduction events. It provides information about signaling molecules, their reactions and the pathways these reactions constitute. The representation of signaling molecules is organized in a number of orthogonal hierarchies reflecting the classification of the molecules, their species-specific or generic features, and their post-translational modifications. Reactions are similarly hierarchically organized in a three-layer architecture, differentiating between reactions that are evidenced by individual publications, generalizations of these reactions to construct species-independent 'reference pathways' and the 'semantic projections' of these pathways. A number of search and browse options allow easy access to the database contents, which can be visualized with the tool PathwayBuildertrade mark. The module PathoSign adds data about pathologically relevant mutations in signaling components, including their genotypes and phenotypes. TRANSPATH and PathoSign can be used as encyclopaedia, in the educational process, for vizualization and modeling of signal transduction networks and for the analysis of gene expression data. TRANSPATH Public 6.0 is freely accessible for users from non-profit organizations under http://www.gene-regulation.com/pub/databases.html.
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Affiliation(s)
- Mathias Krull
- BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany.
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Paananen J, Wong G. Integration of genomic data for pharmacology and toxicology using Internet resources. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:25-36. [PMID: 16513550 DOI: 10.1080/10659360600562053] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Genome based technologies such as sequencing and gene expression profiling using microarrays are creating massive amounts of data. Results from these studies have provided unique insights into targets, biochemical pathways, and biological systems affected by drug or xenobiotic chemical treatments. Moreover, these genomic technologies offer the potential to identify biomarkers for pharmacological development or toxicological prediction. Nonetheless, microarray studies involving a single compound produce useful although limited data. To gain further power from these individual studies, the ability to combine datasets through integration schemes has become imperative. In the current study, we describe and analyze currently available Internet resources designed to address this problem. Many functionalities, such as ability to cross reference orthologous genes across species or to combine same technology platform data, are present in these resources. Nonetheless, these resources are limited in the number of technology platforms they can support. While the ability to integrate all currently existing gene expression datasets remains enigmatic, the current tools provide a partial solution that may still yield unique insights into the affects of exogenous molecules at the level of gene expression.
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Affiliation(s)
- J Paananen
- Department of Computer Science, University of Kuopio, Finland
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36
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Malek RL, Wang HY, Kwitek AE, Greene AS, Bhagabati N, Borchardt G, Cahill L, Currier T, Frank B, Fu X, Hasinoff M, Howe E, Letwin N, Luu TV, Saeed A, Sajadi H, Salzberg SL, Sultana R, Thiagarajan M, Tsai J, Veratti K, White J, Quackenbush J, Jacob HJ, Lee NH. Physiogenomic resources for rat models of heart, lung and blood disorders. Nat Genet 2006; 38:234-9. [PMID: 16415889 DOI: 10.1038/ng1693] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2005] [Accepted: 11/22/2005] [Indexed: 01/10/2023]
Abstract
Cardiovascular disorders are influenced by genetic and environmental factors. The TIGR rodent expression web-based resource (TREX) contains over 2,200 microarray hybridizations, involving over 800 animals from 18 different rat strains. These strains comprise genetically diverse parental animals and a panel of chromosomal substitution strains derived by introgressing individual chromosomes from normotensive Brown Norway (BN/NHsdMcwi) rats into the background of Dahl salt sensitive (SS/JrHsdMcwi) rats. The profiles document gene-expression changes in both genders, four tissues (heart, lung, liver, kidney) and two environmental conditions (normoxia, hypoxia). This translates into almost 400 high-quality direct comparisons (not including replicates) and over 100,000 pairwise comparisons. As each individual chromosomal substitution strain represents on average less than a 5% change from the parental genome, consomic strains provide a useful mechanism to dissect complex traits and identify causative genes. We performed a variety of data-mining manipulations on the profiles and used complementary physiological data from the PhysGen resource to demonstrate how TREX can be used by the cardiovascular community for hypothesis generation.
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Affiliation(s)
- Renae L Malek
- TREX, The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, Maryland 20850, USA
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37
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Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 2006; 34:D108-10. [PMID: 16381825 PMCID: PMC1347505 DOI: 10.1093/nar/gkj143] [Citation(s) in RCA: 1667] [Impact Index Per Article: 92.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2005] [Revised: 10/27/2005] [Accepted: 10/27/2005] [Indexed: 02/06/2023] Open
Abstract
The TRANSFAC database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match and Patch provides increased functionality for TRANSFAC. The list of databases which are linked to the common GENE table of TRANSFAC and TRANSCompel has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD and TRANSPRO. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC 7.0 and TRANSCompel 7.0, are accessible under http://www.gene-regulation.com/pub/databases.html.
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Affiliation(s)
- V Matys
- BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany.
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Abstract
The need to translate genes to function has positioned the rat as an invaluable animal model for genomic research. The significant increase in genomic resources in recent years has had an immediate functional application in the rat. Many of the resources for translational research are already in place and are ready to be combined with the years of physiological knowledge accumulated in numerous rat models, which is the subject of this perspective. Based on the successes to date and the research projects under way to further enhance the infrastructure of the rat, we also project where research in the rat will be in the near future. The impact of the rat genome project has just started, but it is an exciting time with tremendous progress.
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Affiliation(s)
- Jozef Lazar
- Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA
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39
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Abstract
UNLABELLED BioThesaurus is a web-based system designed to map a comprehensive collection of protein and gene names to protein entries in the UniProt Knowledgebase. Currently covering more than two million proteins, BioThesaurus consists of over 2.8 million names extracted from multiple molecular biological databases according to the database cross-references in iProClass. The BioThesaurus web site allows the retrieval of synonymous names of given protein entries and the identification of protein entries sharing the same names. AVAILABILITY BioThesaurus is accessible for online searching at http://pir.georgetown.edu/iprolink/biothesaurus
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Affiliation(s)
- Hongfang Liu
- Department of Information Systems, University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
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40
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Weinshenker D, Wilson MM, Williams KM, Weiss JM, Lamb NE, Twigger SN. A new method for identifying informative genetic markers in selectively bred rats. Mamm Genome 2005; 16:784-91. [PMID: 16261420 DOI: 10.1007/s00335-005-0047-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2005] [Accepted: 06/28/2005] [Indexed: 10/25/2022]
Abstract
Microsatellite length polymorphisms are useful for the mapping of heritable traits in rats. Over 4000 such microsatellites have been characterized for 48 inbred rat strains and used successfully to map phenotypes that differ between strains. At present, however, it is difficult to use this microsatellite database for mapping phenotypes in selectively bred rats of unknown genotype derived from outbred populations because it is not immediately obvious which markers might differ between strains and be informative. We predicted that markers represented by many alleles among the known inbred rat strains would also be most likely to differ between selectively bred strains derived from outbred populations. Here we describe the development and successful application of a new genotyping tool (HUMMER) that assigns "heterozygosity" (Het) and "uncertainty" (Unc) scores to each microsatellite marker that corresponds to its degree of heterozygosity among the 48 genotyped inbred strains. We tested the efficiency of HUMMER on two rat strains that were selectively bred from an outbred Sprague-Dawley stock for either high or low activity in the forced swim test (SwHi rats and SwLo rats, respectively). We found that the markers with high Het and Unc scores allowed the efficient selection of markers that differed between SwHi and SwLo rats, while markers with low Het and Unc scores typically identified markers that did not differ between strains. Thus, picking markers based on Het and Unc scores is a valuable method for identifying informative microsatellite markers in selectively bred rodent strains derived from outbred populations.
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Affiliation(s)
- David Weinshenker
- Department of Human Genetics, Emory University, Whitehead 301, 615 Michael Street, Atlanta, Georgia 30322, USA.
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41
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Twigger SN, Pasko D, Nie J, Shimoyama M, Bromberg S, Campbell D, Chen J, dela Cruz N, Fan C, Foote C, Harris G, Hickmann B, Ji Y, Jin W, Li D, Mathis J, Nenasheva N, Nigam R, Petri V, Reilly D, Ruotti V, Schauberger E, Seiler K, Slyper R, Smith J, Wang W, Wu W, Zhao L, Zuniga-Meyer A, Tonellato PJ, Kwitek AE, Jacob HJ. Tools and strategies for physiological genomics: the Rat Genome Database. Physiol Genomics 2005; 23:246-56. [PMID: 16106031 PMCID: PMC4505745 DOI: 10.1152/physiolgenomics.00040.2005] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The broad goal of physiological genomics research is to link genes to their functions using appropriate experimental and computational techniques. Modern genomics experiments enable the generation of vast quantities of data, and interpretation of this data requires the integration of information derived from many diverse sources. Computational biology and bioinformatics offer the ability to manage and channel this information torrent. The Rat Genome Database (RGD; http://rgd.mcw.edu) has developed computational tools and strategies specifically supporting the goal of linking genes to their functional roles in rat and, using comparative genomics, to human and mouse. We present an overview of the database with a focus on these unique computational tools and describe strategies for the use of these resources in the area of physiological genomics.
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Affiliation(s)
- Simon N Twigger
- Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Al-Shahrour F, Díaz-Uriarte R, Dopazo J. Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics 2005; 21:2988-93. [PMID: 15840702 DOI: 10.1093/bioinformatics/bti457] [Citation(s) in RCA: 78] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The analysis of genome-scale data from different high throughput techniques can be used to obtain lists of genes ordered according to their different behaviours under distinct experimental conditions corresponding to different phenotypes (e.g. differential gene expression between diseased samples and controls, different response to a drug, etc.). The order in which the genes appear in the list is a consequence of the biological roles that the genes play within the cell, which account, at molecular scale, for the macroscopic differences observed between the phenotypes studied. Typically, two steps are followed for understanding the biological processes that differentiate phenotypes at molecular level: first, genes with significant differential expression are selected on the basis of their experimental values and subsequently, the functional properties of these genes are analysed. Instead, we present a simple procedure which combines experimental measurements with available biological information in a way that genes are simultaneously tested in groups related by common functional properties. The method proposed constitutes a very sensitive tool for selecting genes with significant differential behaviour in the experimental conditions tested. RESULTS We propose the use of a method to scan ordered lists of genes. The method allows the understanding of the biological processes operating at molecular level behind the macroscopic experiment from which the list was generated. This procedure can be useful in situations where it is not possible to obtain statistically significant differences based on the experimental measurements (e.g. low prevalence diseases, etc.). Two examples demonstrate its application in two microarray experiments and the type of information that can be extracted.
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Affiliation(s)
- Fátima Al-Shahrour
- Department of Bioinformatics, and Functional Genomics Node (INB), Centro de Investigación Príncipe Felipe Autopista del Saler 16, 46013 Valencia, Spain
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