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Nair SK, Eeles C, Ho C, Beri G, Yoo E, Tkachuk D, Tang A, Nijrabi P, Smirnov P, Seo H, Jennen D, Haibe-Kains B. ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets. Nucleic Acids Res 2020; 48:W455-W462. [PMID: 32421831 PMCID: PMC7319553 DOI: 10.1093/nar/gkaa390] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/27/2020] [Accepted: 05/04/2020] [Indexed: 11/12/2022] Open
Abstract
In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.
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Affiliation(s)
- Sisira Kadambat Nair
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Christopher Eeles
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Gangesh Beri
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Esther Yoo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Denis Tkachuk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Amy Tang
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Parwaiz Nijrabi
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Heewon Seo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School of Oncology and Development Biology, Maastricht University, Maastricht, The Netherlands
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada.,Department of Computer Science, University of Toronto, Toronto, ON M5T 3A1, Canada.,Ontario Institute for Cancer Research, Toronto, ON M5G 1L7, Canada.,Vector Institute for Artificial Intelligence, Toronto, ON M5G 1L7, Canada
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Allendes Osorio RS, Nyström-Persson JT, Nojima Y, Kosugi Y, Mizuguchi K, Natsume-Kitatani Y. Panomicon: A web-based environment for interactive, visual analysis of multi-omics data. Heliyon 2020; 6:e04618. [PMID: 32904262 PMCID: PMC7452437 DOI: 10.1016/j.heliyon.2020.e04618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 12/12/2022] Open
Abstract
Multi-omics analyses, combining transcriptomics, genomics, proteomics, and so on, have led to important insights in many areas of biology and medicine. To support these analyses, software that can handle the difficulties associated with multi-omics datasets is crucial. Here, we describe Panomicon, a web-based, interactive analysis environment for multi-omics data. Building on Toxygates, a tool previously created to study single-omics data that features interactive clustering, heatmaps, and user data uploads, Panomicon introduces improvements for the storage and handling of additional omics types, as well as tools for the generation and visualization of interaction networks between different types of omics data. Panomicon is a new type of environment for the collaborative study of multi-omics data, both for users uploading data to our server and for groups wishing to host their own deployment of Panomicon. We demonstrate Panomicon's capabilities by revisiting a microRNA-mRNA interaction networks study in a non-small cell lung cancer dataset.
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Affiliation(s)
- Rodolfo S Allendes Osorio
- Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan
| | - Johan T Nyström-Persson
- Lifematics Ltd., Sanshin Hatchobori bldg. 5F, 2-25-10 Hatchobori, Chuo-ku, Tokyo-to, 104-0032, Japan
| | - Yosui Nojima
- Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan
| | - Yuji Kosugi
- Lifematics Ltd., Sanshin Hatchobori bldg. 5F, 2-25-10 Hatchobori, Chuo-ku, Tokyo-to, 104-0032, Japan
| | - Kenji Mizuguchi
- Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan.,Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita-shi, Osaka, 565-0871, Japan
| | - Yayoi Natsume-Kitatani
- Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan.,Laboratory of In-silico Drug Design, Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki-shi, Osaka, 567-0085, Japan
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Serra A, Fratello M, Cattelani L, Liampa I, Melagraki G, Kohonen P, Nymark P, Federico A, Kinaret PAS, Jagiello K, Ha MK, Choi JS, Sanabria N, Gulumian M, Puzyn T, Yoon TH, Sarimveis H, Grafström R, Afantitis A, Greco D. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E708. [PMID: 32276469 PMCID: PMC7221955 DOI: 10.3390/nano10040708] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/30/2022]
Abstract
Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.
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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - My Kieu Ha
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Jang-Sik Choi
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Natasha Sanabria
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
| | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tae-Hyun Yoon
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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Hasan MN, Begum AA, Rahman M, Haque Mollah MN. Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart. Comput Biol Chem 2018; 78:375-381. [PMID: 30606695 DOI: 10.1016/j.compbiolchem.2018.12.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 12/25/2018] [Indexed: 10/27/2022]
Abstract
Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds. Therefore, in this paper, we have discussed two approaches namely moving range chart (MRC) and logistic moving range chart (LMRC) for the identification of significant up-regulatory (UpR) and down-regulatory (DnR) gene-compound interactions as well as toxicogenomic biomarkers and their regulatory chemical compounds/drugs. We have investigated the performance of both MRC and LMRC approaches using simulated datasets. Simulation results show that both approaches perform almost equally in absence of outliers. However, in presence of outliers, the LMRC shows much better performance than the MRC. In case of real life toxicogenomic data analysis, the proposed LMRC approach detected some important down-regulated biomarker genes those were not detected by other approaches. Therefore, in this paper, our proposal is to use LMRC for robust identification of significant interactions between genes and chemical compounds/drugs.
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Affiliation(s)
- Mohammad Nazmol Hasan
- Bioinformatics Lab., Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh; Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh.
| | - Anjuman Ara Begum
- Bioinformatics Lab., Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Moizur Rahman
- Department of Veterinary and Animal Sciences, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab., Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
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Furihata C, Suzuki T. Evaluation of 12 mouse marker genes in rat toxicogenomics public data, Open TG-GATEs: Discrimination of genotoxic from non-genotoxic hepatocarcinogens. MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2018; 838:9-15. [PMID: 30678831 DOI: 10.1016/j.mrgentox.2018.11.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 11/05/2018] [Accepted: 11/08/2018] [Indexed: 01/19/2023]
Abstract
Previously, we proposed 12 marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2 and Tubb4b) to discriminate mouse genotoxic hepatocarcinogens (GTHC) from non-genotoxic hepatocarcinogens (NGTHC). This was determined by qPCR and principal component analysis (PCA), as the aim of an in vivo short-term screening for genotoxic hepatocarcinogens. For this paper, we conducted an application study of the 12 mouse marker genes to rat data, Open TG-GATEs (public data). We analyzed five typical rat GTHC (2-acetamodofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine), and not only seven typical rat NGTHC (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital and WY-14643) but also 11 non-genotoxic non-hepatocarcinogens (NGTNHC; allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline and tolbutamide) from Open TG-GATEs. The analysis was performed at 3, 6, 9 and 24 h after a single administration and 4, 8, 15 and 29 days after repeated administrations. We transferred Open TG-GATEs DNA microarray data into log2 data using the "R Project for Statistical Computing". GTHC-specific dose-dependent gene expression changes were observed and significance assessed with the Williams test. Similar significant changes were observed during 3-24 h and 4-29 days, assessed with Welch's t-test, except not for NGTHC or NGTNHC. Significant differential changes in gene expression were observed between GTHC and NGTHC in 11 genes (except not Tubb4b) and between GTHC and NGTNHC in all 12 genes at 24 h and 10 genes (except Ccnf and Mbd1) at 29 days, per Tukey's test. PCA successfully discriminated GTHC from NGTHC and NGTNHC at 24 h and 29 days. The results demonstrate that 12 previously proposed mouse marker genes are useful for discriminating rat GTHC from NGTHC and NGTNHC from Open TG-GATEs.
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Affiliation(s)
- Chie Furihata
- Division of Molecular Target and Gene Therapy Products, National Institute of Health Sciences, 3-25-26, Tonomach, Kawasaki-ku, Kawasaki, 210-9501, Japan; School of Science and Engineering, Aoyama Gakuin University, Sagamihara, Kanagawa, 252-5258, Japan.
| | - Takayoshi Suzuki
- Division of Molecular Target and Gene Therapy Products, National Institute of Health Sciences, 3-25-26, Tonomach, Kawasaki-ku, Kawasaki, 210-9501, Japan
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Hasan MN, Rana MM, Begum AA, Rahman M, Mollah MNH. Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model. Front Genet 2018; 9:516. [PMID: 30450112 PMCID: PMC6225736 DOI: 10.3389/fgene.2018.00516] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 10/12/2018] [Indexed: 11/13/2022] Open
Abstract
Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs. Also it is sensitive to outlying observations. Thus, this platform may produce misleading results in some cases. The probabilistic hidden variable model (PHVM) is a more effective co-clustering approach that share two-way information simultaneously, but it is also sensitive to outlying observations. Therefore, in this paper we have proposed logistic probabilistic hidden variable model (LPHVM) for robust co-clustering between genes and DCCs, since gene expression data are often contaminated by outlying observations. We have investigated the performance of the proposed LPHVM co-clustering approach in a comparison with the conventional PHVM and Toxygates co-clustering approaches using simulated and real life TGP gene expression datasets, respectively. Simulation results show that the proposed method improved the performance over the conventional PHVM in presence of outliers; otherwise, it keeps equal performance. In the case of real life TGP data analysis, three DCCs (glibenclamide-low, perhexilline-low, and hexachlorobenzene-medium) for glutathione metabolism pathway dataset as well as two DCCs (acetaminophen-medium and methapyrilene-low) for PPAR signaling pathway dataset were incorrectly co-clustered by the Toxygates online platform, while only one DCC (hexachlorobenzene-low) for glutathione metabolism pathway was incorrectly co-clustered by the proposed LPHVM approach. Our findings from the real data analysis are also supported by the other findings in the literature.
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Affiliation(s)
- Mohammad Nazmol Hasan
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.,Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
| | - Md Masud Rana
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Anjuman Ara Begum
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Moizur Rahman
- Department of Veterinary and Animal Sciences, University of Rajshahi, Rajshahi, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Laboratory, Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
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Barel G, Herwig R. Network and Pathway Analysis of Toxicogenomics Data. Front Genet 2018; 9:484. [PMID: 30405693 PMCID: PMC6204403 DOI: 10.3389/fgene.2018.00484] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 09/28/2018] [Indexed: 12/20/2022] Open
Abstract
Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.
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Affiliation(s)
| | - Ralf Herwig
- Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany
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Darde TA, Chalmel F, Svingen T. Exploiting advances in transcriptomics to improve on human-relevant toxicology. CURRENT OPINION IN TOXICOLOGY 2018. [DOI: 10.1016/j.cotox.2019.02.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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