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Tian D, Xu T, Kang H, Luo H, Wang Y, Chen M, Li R, Ma L, Wang Z, Hao L, Tang B, Zou D, Xiao J, Zhao W, Bao Y, Zhang Z, Song S. Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications. aBIOTECH 2024; 5:94-106. [PMID: 38576435 PMCID: PMC10987443 DOI: 10.1007/s42994-023-00134-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/18/2023] [Indexed: 04/06/2024]
Abstract
Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study. Supplementary Information The online version contains supplementary material available at 10.1007/s42994-023-00134-4.
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Affiliation(s)
- Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Tianyi Xu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Hailong Kang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Hong Luo
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Yanqing Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Meili Chen
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Rujiao Li
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Lina Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Zhonghuang Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Lili Hao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Bixia Tang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Dong Zou
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Wenming Zhao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences & China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
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Jiang W, Wang PY, Zhou Q, Lin QT, Yao Y, Huang X, Tan X, Yang S, Ye W, Yang Y, Bao YJ. Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine. J Transl Med 2023; 21:885. [PMID: 38057859 DOI: 10.1186/s12967-023-04773-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.
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Affiliation(s)
- Wei Jiang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Peng-Ying Wang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Qi Zhou
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Qiu-Tong Lin
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Yao Yao
- Wuxi Shengrui Bio-Pharmaceuticals Co., Ltd, Wuxi, 214174, Jiangsu, China
| | - Xun Huang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Xiaoming Tan
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Shihui Yang
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China
| | - Weicai Ye
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510000, China
- Guangdong Province Key Laboratory of Computational Science, and National Engineering Laboratory for Big Data Analysis and Application, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510000, China.
| | - Yun-Juan Bao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, China.
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Kaur D, Arora A, Patiyal S, Raghava GPS. Hmrbase2: a comprehensive database of hormones and their receptors. Hormones (Athens) 2023; 22:359-366. [PMID: 37291365 DOI: 10.1007/s42000-023-00455-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/19/2023] [Indexed: 06/10/2023]
Abstract
PURPOSE Hormones play a critical role in regulating various physiological processes and any hormonal imbalances can lead to major endocrine disorders. Thus, studying hormones is essential for both the therapeutics and the diagnostics of hormonal diseases. To facilitate this need, we have developed Hmrbase2, a comprehensive platform that provides extensive information on hormones. METHODS Hmrbase2 is a web-based database which is an update of a previously published database, Hmrbase ( http://crdd.osdd.net/raghava/hmrbase/ ). We collected a large amount of information on peptide and non-peptide hormones and hormone receptors, this information being sourced from Hmrbase, HMDB, UniProt, HORDB, ENDONET, PubChem, and the medical literature. RESULTS Hmrbase2 contains a total of 12,056 entries, which is more than twice the number of entries contained in the previous version Hmrbase. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones, and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties such as melting point and water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been supplied. Additionally, a similarity search module has been incorporated enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. CONCLUSIONS To make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Hmrbase2 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2 .
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Affiliation(s)
- Dashleen Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Akanksha Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
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4
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Dhasarathan C, Shanmugam M, Kumar M, Tripathi D, Khapre S, Shankar A. A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach. Multimed Tools Appl 2023:1-24. [PMID: 37362729 PMCID: PMC10241612 DOI: 10.1007/s11042-023-15363-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/07/2023] [Accepted: 04/15/2023] [Indexed: 06/28/2023]
Abstract
In recent years, there has been a surge in the use of deep learning systems for e-healthcare applications. While these systems can provide significant benefits regarding improved diagnosis and treatment, they also pose substantial privacy risks to patients' sensitive data. Privacy is a crucial issue in e-healthcare, and it is essential to keep patient information secure. A new approach based on multi-agent-based privacy metrics for e-healthcare deep learning systems has been proposed to address this issue. This approach uses a combination of deep learning and multi-agent systems to provide a more robust and secure method for e-healthcare applications. The multi-agent system is designed to monitor and control the access to patients' data by different agents in the system. Each agent is assigned a specific role and has specific data access permissions. The system employs a set of privacy metrics to a substantial privacy level of the data accessed by each agent. These metrics include confidentiality, integrity, and availability, evaluated in real-time and used to identify potential privacy violations. In addition to the multi-agent system, the deep learning component is also integrated into the system to improve the accuracy of diagnoses and treatment plans. The deep learning model is trained on a large dataset of medical records and can accurately predict the diagnosis and treatment plan based on the patient's symptoms and medical history. The multi-agent-based privacy metrics for the e-healthcare deep learning system approach have several advantages. It provides a more secure system for e-healthcare applications by ensuring only authorized agents can access patients' data. Privacy metrics enable the system to identify potential privacy violations in real-time, thereby reducing the risk of data breaches. Finally, integrating deep learning improves the accuracy of diagnoses and treatment plans, leading to better patient outcomes.
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Affiliation(s)
- Chandramohan Dhasarathan
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - M. Shanmugam
- Department of Computer Science, School of Engineering and Technology, Pondicherry University, Puducherry, India
| | - Manish Kumar
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab India
| | - Diwakar Tripathi
- Computer Science and Engineering Department, Indian Institute of Information Technology, Sonepat, Hariyana India
| | - Shailesh Khapre
- Department of Data Science & Artificial Intelligence, Dr. S. P. Mukherjee IIIT, Naya Raipur, Chhattisgarh India
| | - Achyut Shankar
- WMG, University of Warwick, Coventry, UK
- Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, 248002 India
- School of Computer Science Engineering, Lovely Professional University, Phagwara - 144411, Punjab India
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Loganathan L, Jeyakanthan J, Muthusamy K. HTNpedia: A Knowledge Base for Hypertension Research. Comb Chem High Throughput Screen 2023:CCHTS-EPUB-131917. [PMID: 37202885 DOI: 10.2174/1386207326666230518162439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/27/2023] [Accepted: 03/31/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Hypertension is notably a serious public health concern due to its high prevalence and strong association with cardiovascular disease and renal failure. It is reported to be the fourth leading disease that leads to death worldwide. OBJECTIVE Currently, there is no active operational knowledge base or database for hypertension or cardiovascular illness. METHOD The primary data source was retrieved from the research outputs obtained from our laboratory team working on hypertension research. We have presented a preliminary dataset and external links to the public repository for detailed analysis to readers. RESULT As a result, HTNpedia was created to provide information regarding hypertension-related proteins and genes. CONCLUSION The complete webpage is accessible via www.mkarthikeyan.bioinfoau.org/HTNpedia.
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Affiliation(s)
- Lakshmanan Loganathan
- Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
| | - Jeyaraman Jeyakanthan
- Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
| | - Karthikeyan Muthusamy
- Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
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Tang T, Liu X, Wu R, Shen L, Ren S, Shen B. CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs. Genomics Proteomics Bioinformatics 2023; 21:292-299. [PMID: 36265769 PMCID: PMC10626174 DOI: 10.1016/j.gpb.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 09/19/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Cancer therapy resistance and recurrence (CTRR) are the dominant causes of death in cancer patients. Recent studies have indicated that non-coding RNAs (ncRNAs) can not only reverse the resistance to cancer therapy but also are crucial biomarkers for the evaluation and prediction of CTRR. Herein, we developed CTRR-ncRNA, a knowledgebase of CTRR-associated ncRNAs, aiming to provide an accurate and comprehensive resource for research involving the association between CTRR and ncRNAs. Compared to most of the existing cancer databases, CTRR-ncRNA is focused on the clinical characterization of cancers, including cancer subtypes, as well as survival outcomes and responses to personalized therapy of cancer patients. Information pertaining to biomarker ncRNAs has also been documented for the development of personalized CTRR prediction. A user-friendly interface and several functional modules have been incorporated into the database. Based on the preliminary analysis of genotype-phenotype relationships, universal ncRNAs have been found to be potential biomarkers for CTRR. The CTRR-ncRNA is a translation-oriented knowledgebase and it provides a valuable resource for mechanistic investigations and explainable artificial intelligence-based modeling. CTRR-ncRNA is freely available to the public at http://ctrr.bioinf.org.cn/.
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Affiliation(s)
- Tong Tang
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xingyun Liu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Rongrong Wu
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China; West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Shumin Ren
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Centre for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
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Bhargav A, Gupta S, Seth S, James S, Fatima F, Chaurasia P, Ramachandran S. Knowledgebase of potential multifaceted solutions to antimicrobial resistance. Comput Biol Chem 2022; 101:107772. [PMID: 36155273 DOI: 10.1016/j.compbiolchem.2022.107772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/16/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022]
Abstract
Antimicrobial resistance (AMR), a top threat to global health, challenges preventive and treatment strategies of infections. AMR strains of microbial pathogens arise through multiple mechanisms. The underlying "antibiotic resistance genes" (ARGs) spread through various species by lateral gene transfer thereby causing global dissemination. Human methods also augment this process through inappropriate use, non-compliance to treatment schedule, and environmental waste. Worldwide significant efforts are being invested to discover novel therapeutic solutions for tackling resistant pathogens. Diverse therapeutic strategies have evolved over recent years. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. Therapeutic strategies against bacteria, virus, fungus and parasites were extracted from PubMed literature using text mining. We have used a subjective (sentimental) approach for data mining new strategies, resulting in broad coverage of novel entities and subsequently add objective data like entity name (including IUPAC), potency, and safety information. The extracted data was organized in a freely accessible web platform, KOMBAT. The KOMBAT comprises 1104 Chemical compounds, 220 of newly identified antimicrobial peptides, 42 bacteriophages, 242 phytochemicals, 106 nanocomposites, and 94 novel entities for phototherapy. Entities tested and evaluated on AMR pathogens are included. We envision that this database will be useful for developing future therapeutics against AMR pathogens. The database can be accessed through http://kombat.igib.res.in/.
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Trautman A, Linchangco R, Walstead R, Jay JJ, Brouwer C. The Aliment to Bodily Condition knowledgebase (ABCkb): a database connecting plants and human health. BMC Res Notes 2021; 14:433. [PMID: 34838100 PMCID: PMC8627056 DOI: 10.1186/s13104-021-05835-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 11/03/2021] [Indexed: 11/10/2022] Open
Abstract
Objective Overconsumption of processed foods has led to an increase in chronic diet-related diseases such obesity and type 2 diabetes. Although diets high in fresh fruits and vegetables are linked with healthier outcomes, the specific mechanisms for these relationships are poorly understood. Experiments examining plant phytochemical production and breeding programs, or separately on the health effects of nutritional supplements have yielded results that are sparse, siloed, and difficult to integrate between the domains of human health and agriculture. To connect plant products to health outcomes through their molecular mechanism an integrated computational resource is necessary. Results We created the Aliment to Bodily Condition Knowledgebase (ABCkb) to connect plants to human health by creating a stepwise path from plant \documentclass[12pt]{minimal}
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\begin{document}$$\rightarrow$$\end{document}→ plant product \documentclass[12pt]{minimal}
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\begin{document}$$\rightarrow$$\end{document}→ human gene \documentclass[12pt]{minimal}
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\begin{document}$$\rightarrow$$\end{document}→ pathways \documentclass[12pt]{minimal}
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\begin{document}$$\rightarrow$$\end{document}→ indication. ABCkb integrates 11 curated sources as well as relationships mined from Medline abstracts by loading into a graph database which is deployed via a Docker container. This new resource, provided in a queryable container with a user-friendly interface connects plant products with human health outcomes for generating nutritive hypotheses. All scripts used are available on github (https://github.com/atrautm1/ABCkb) along with basic directions for building the knowledgebase and a browsable interface is available (https://abckb.charlotte.edu). Supplementary Information The online version contains supplementary material available at 10.1186/s13104-021-05835-x.
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Affiliation(s)
- Aaron Trautman
- Bioinformatics Services Division, UNC Charlotte, Charlotte, NC, USA.,Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC, USA
| | - Richard Linchangco
- Bioinformatics Services Division, UNC Charlotte, Charlotte, NC, USA.,Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC, USA
| | - Rachel Walstead
- Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC, USA
| | - Jeremy J Jay
- Bioinformatics Services Division, UNC Charlotte, Charlotte, NC, USA.,Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC, USA
| | - Cory Brouwer
- Bioinformatics Services Division, UNC Charlotte, Charlotte, NC, USA. .,Department of Bioinformatics and Genomics, UNC Charlotte, Charlotte, NC, USA.
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Bukhari SAC, Pawar S, Mandell J, Kleinstein SH, Cheung KH. LinkedImm: a linked data graph database for integrating immunological data. BMC Bioinformatics 2021; 22:105. [PMID: 34433410 PMCID: PMC8385794 DOI: 10.1186/s12859-021-04031-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.
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Affiliation(s)
- Syed Ahmad Chan Bukhari
- Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, New York, NY, USA
| | - Shrikant Pawar
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Jeff Mandell
- Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA
| | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA.,Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Kei-Hoi Cheung
- Program in Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, CT, USA. .,Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA. .,Yale Center for Medical Informatics, Yale School of Medicine, New Haven, CT, USA.
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Shen M, Chen M, Liang T, Wang S, Xue Y, Bertz R, Xie XQ, Feng Z. Pain Chemogenomics Knowledgebase (Pain-CKB) for Systems Pharmacology Target Mapping and Physiologically Based Pharmacokinetic Modeling Investigation of Opioid Drug-Drug Interactions. ACS Chem Neurosci 2020; 11:3245-3258. [PMID: 32966035 DOI: 10.1021/acschemneuro.0c00372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.
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Affiliation(s)
- Mingzhe Shen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Maozi Chen
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Tianjian Liang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Siyi Wang
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Ying Xue
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Richard Bertz
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Xiang-Qun Xie
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, and Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - Zhiwei Feng
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
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11
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Xu Q, Zhai JC, Huo CQ, Li Y, Dong XJ, Li DF, Huang RD, Shen C, Chang YJ, Zeng XL, Meng FL, Yang F, Zhang WL, Zhang SN, Zhou YM, Zhang Z. OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level. BMC Cancer 2020; 20:740. [PMID: 32770988 PMCID: PMC7414679 DOI: 10.1186/s12885-020-07221-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Background Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Results Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. Conclusions OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com.
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Affiliation(s)
- Quan Xu
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Jin-Cheng Zhai
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Cai-Qin Huo
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Yang Li
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Xue-Jiao Dong
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Dong-Fang Li
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Ru-Dan Huang
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Chuang Shen
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Yu-Jun Chang
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Xi-Ling Zeng
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Fan-Lin Meng
- School of Medicine, Tsinghua University, Beijing, 100084, P.R. China
| | - Fang Yang
- College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, P.R. China
| | - Wan-Ling Zhang
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Sheng-Nan Zhang
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Yi-Ming Zhou
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China.,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China
| | - Zhi Zhang
- National Engineering Research Center for Beijing Biochip Technology, Changping District, Beijing, 102206, P.R. China. .,CapitalBio Corporation, Changping District, Beijing, 102206, P.R. China.
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12
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Danos AM, Krysiak K, Barnell EK, Coffman AC, McMichael JF, Kiwala S, Spies NC, Sheta LM, Pema SP, Kujan L, Clark KA, Wollam AZ, Rao S, Ritter DI, Sonkin D, Raca G, Lin WH, Grisdale CJ, Kim RH, Wagner AH, Madhavan S, Griffith M, Griffith OL. Standard operating procedure for curation and clinical interpretation of variants in cancer. Genome Med 2019; 11:76. [PMID: 31779674 PMCID: PMC6883603 DOI: 10.1186/s13073-019-0687-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 11/07/2019] [Indexed: 02/04/2023] Open
Abstract
Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretation of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC’s clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.
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Affiliation(s)
- Arpad M Danos
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Kilannin Krysiak
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Erica K Barnell
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Adam C Coffman
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas C Spies
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Lana M Sheta
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shahil P Pema
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Lynzey Kujan
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Kaitlin A Clark
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Amber Z Wollam
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shruti Rao
- Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, USA
| | - Deborah I Ritter
- Department of Pediatrics, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA
| | - Dmitriy Sonkin
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Gordana Raca
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Wan-Hsin Lin
- Department of Cancer Biology, Mayo Clinic, Jacksonville, Florida, USA
| | - Cameron J Grisdale
- Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada
| | - Raymond H Kim
- Fred A. Litwin Family Center in Genetic Medicine, University Health Network, Toronto, ON, Canada
| | - Alex H Wagner
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington DC, USA.,Georgetown Lombardi Comprehensive Cancer Center, Washington DC, USA
| | - Malachi Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. .,Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Obi L Griffith
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA. .,Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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13
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Mallona I, Jordà M, Peinado MA. A knowledgebase of the human Alu repetitive elements. J Biomed Inform 2016; 60:77-83. [PMID: 26827622 DOI: 10.1016/j.jbi.2016.01.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 01/20/2016] [Accepted: 01/22/2016] [Indexed: 01/13/2023]
Abstract
Alu elements are the most abundant retrotransposons in the human genome with more than one million copies. Alu repeats have been reported to participate in multiple processes related with genome regulation and compartmentalization. Moreover, they have been involved in the facilitation of pathological mutations in many diseases, including cancer. The contribution of Alus and other repeats in genomic regulation is often overlooked because their study poses technical and analytical challenges hardly attainable with conventional strategies. Here we propose the integration of ontology-based semantic methods to query a knowledgebase for the human Alus. The knowledgebase for the human Alus leverages Sequence (SO) and Gene Ontologies (GO) and is devoted to address functional and genetic information in the genomic context of the Alus. For each Alu element, the closest gene and transcript are stored, as well their functional annotation according to GO, the state of the chromatin and the transcription factors binding sites inside the Alu. The model uses Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). As a case of use and to illustrate the utility of the tool, we have evaluated the epigenetic states of Alu repeats associated with gene promoters according to their transcriptional activity. The ontology is easily extendable, offering a scaffold for the inclusion of new experimental data. The RDF/XML formalization is freely available at http://aluontology.sourceforge.net/.
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Affiliation(s)
- Izaskun Mallona
- Institute of Predictive and Personalized Medicine of Cancer (IMPPC) and Health Research Institute Germans Trias i Pujol (IGTP), Can Ruti Campus. Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain.
| | - Mireia Jordà
- Institute of Predictive and Personalized Medicine of Cancer (IMPPC) and Health Research Institute Germans Trias i Pujol (IGTP), Can Ruti Campus. Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain
| | - Miguel A Peinado
- Institute of Predictive and Personalized Medicine of Cancer (IMPPC) and Health Research Institute Germans Trias i Pujol (IGTP), Can Ruti Campus. Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain
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14
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Abstract
Data linking specific ages or age ranges with disease are abundant in biomedical literature. However, these data are organized such that searching for age-phenotype relationships is difficult. Recently, we described the Age-Phenome Knowledge-base (APK), a computational platform for storage and retrieval of information concerning age-related phenotypic patterns. Here, we report that data derived from over 1.5 million human-related PubMed abstracts have been added to APK. Using a text-mining pipeline, 35,683 entries which describe relationships between age and phenotype (such as disease) have been introduced into the database. Comparing the results to those obtained by a human reader reveals that the overall accuracy of these entries is estimated to exceed 80%. The usefulness of these data for obtaining new insight regarding age-disease relationships is demonstrated using clustering analysis, which is shown to capture obvious, as well as potentially interesting relationships between diseases. In addition, a new tool for browsing and searching the APK database is presented. We thus present a unique resource and a new framework for studying age-disease relationships and other phenotypic processes.
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Affiliation(s)
- Nophar Geifman
- Shraga Segal Department of Microbiology and Immunology, Faculty of Health Sciences and The National Institute for Biotechnology in the Negev, Ben Gurion University, Beersheva 84105, Israel
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