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Bhasuran B, Manoharan S, Iyyappan OR, Murugesan G, Prabahar A, Raja K. Large Language Models and Genomics for Summarizing the Role of microRNA in Regulating mRNA Expression. Biomedicines 2024; 12:1535. [PMID: 39062108 PMCID: PMC11274411 DOI: 10.3390/biomedicines12071535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 06/30/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
microRNA (miRNA)-messenger RNA (mRNA or gene) interactions are pivotal in various biological processes, including the regulation of gene expression, cellular differentiation, proliferation, apoptosis, and development, as well as the maintenance of cellular homeostasis and pathogenesis of numerous diseases, such as cancer, cardiovascular diseases, neurological disorders, and metabolic conditions. Understanding the mechanisms of miRNA-mRNA interactions can provide insights into disease mechanisms and potential therapeutic targets. However, extracting these interactions efficiently from a huge collection of published articles in PubMed is challenging. In the current study, we annotated a miRNA-mRNA Interaction Corpus (MMIC) and used it for evaluating the performance of a variety of machine learning (ML) models, deep learning-based transformer (DLT) models, and large language models (LLMs) in extracting the miRNA-mRNA interactions mentioned in PubMed. We used the genomics approaches for validating the extracted miRNA-mRNA interactions. Among the ML, DLT, and LLM models, PubMedBERT showed the highest precision, recall, and F-score, with all equal to 0.783. Among the LLM models, the performance of Llama-2 is better when compared to others. Llama 2 achieved 0.56 precision, 0.86 recall, and 0.68 F-score in a zero-shot experiment and 0.56 precision, 0.87 recall, and 0.68 F-score in a three-shot experiment. Our study shows that Llama 2 achieves better recall than ML and DLT models and leaves space for further improvement in terms of precision and F-score.
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Affiliation(s)
- Balu Bhasuran
- School of Information, Florida State University, Tallahassee, FL 32306, USA;
| | - Sharanya Manoharan
- Department of Bioinformatics, Stella Maris College, Chennai 600086, Tamil Nadu, India;
| | - Oviya Ramalakshmi Iyyappan
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai 641112, Tamil Nadu, India;
| | - Gurusamy Murugesan
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Guntur District, Vaddeswaram 522302, Andhra Pradesh, India;
| | - Archana Prabahar
- Center for Gene Regulation in Health and Disease, Department of Biological, Geological, and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA;
| | - Kalpana Raja
- Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, USA
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2
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Morishita EC, Nakamura S. Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery. Expert Opin Drug Discov 2024; 19:415-431. [PMID: 38321848 DOI: 10.1080/17460441.2024.2313455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality. AREAS COVERED The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs. EXPERT OPINION Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.
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Adel RM, Helal H, Ahmed Fouad M, Sobhy Abd-Elhalem S. Regulation of miRNA-155-5p ameliorates NETosis in pulmonary fibrosis rat model via inhibiting its target cytokines IL-1β, TNF-α and TGF-β1. Int Immunopharmacol 2024; 127:111456. [PMID: 38159555 DOI: 10.1016/j.intimp.2023.111456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/16/2023] [Accepted: 12/24/2023] [Indexed: 01/03/2024]
Abstract
Idiopathic pulmonary fibrosis (IPF) is an age-related inflammatory disease with no cure up till now.It is accompanied by neutrophils infiltration as the main responders to inflammation and fibrosis. Importantly, neutrophils release neutrophil extracellular traps (NETs) through NETosis process. The function of microRNAs during inflammation became of great biological attention. Owing to microRNAs' central role in immune system, microRNA-155-5p (miR-155-5p) is intensely involved in the inflammatory response. Capsaicin (Cap) is a bioactive compound that exhibits antioxidative and anti-inflammatory functions. Recent studies have shown its role in regulation of certain microRNAs' expressions. Accordingly, the present study aims to investigate the effect of miR-155-5p regulation in suppressing NETs production via ameliorating its target inflammatory cytokines, IL-1ß, TNF-α and TGF-ß1, in bleomycin (BLM)-induced pulmonary fibrosis rat model treated by Cap. The obtained results demonstrated that miR-155-5p downregulation was associated with significant decrease in IL-1ß, TNF-α, TGF-β1, which consequently, reduced hydroxyproline (HYP), NETs activity markers as NE and PAD-4, and alleviated CTGF levels in lung tissues of animals treated by Cap. Furthermore, NETosis ultrastructure examination by transmission electron microscope (TEM), MPO immunohistochemical staining and histopathological studies confirmed an abolishment in NETs formation and an improvement in lung tissue architecture in Cap-treated rats. This study concluded that Cap quenched the inflammatory response through interrupting IL-1β, TNF-α and TGF-β1 pathway via modulating miR-155-5p expression. In addition, Cap was able to alleviate pulmonary NETosis markers by restraining NETs activity markers. These findings provide novel insight into the application of Cap-based treatment in ameliorating pulmonary damage in IPF.
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Affiliation(s)
- Rana Mostafa Adel
- Zoology Department, Faculty of Women for Arts, Science and Education, Ain Shams University, 11757, Cairo, Egypt.
| | - Hamed Helal
- Zoology Department, Faculty of Science, Al-Azhar University, 11884, Nasr City, Cairo, Egypt.
| | - Mona Ahmed Fouad
- Zoology Department, Faculty of Women for Arts, Science and Education, Ain Shams University, 11757, Cairo, Egypt.
| | - Sahar Sobhy Abd-Elhalem
- Zoology Department, Faculty of Women for Arts, Science and Education, Ain Shams University, 11757, Cairo, Egypt.
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Ahirwar SS, Rizwan R, Sethi S, Shahid Z, Malviya S, Khandia R, Agarwal A, Kotnis A. Comparative Analysis of Published Database Predicting MicroRNA Binding in 3'UTR of mRNA in Diverse Species. Microrna 2024; 13:2-13. [PMID: 37929739 DOI: 10.2174/0122115366261005231018070640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 09/03/2023] [Accepted: 09/15/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Micro-RNAs are endogenous non-coding RNA moieties of 22-27 nucleotides that play a crucial role in the regulation of various biological processes and make them useful prognostic and diagnostic biomarkers. Discovery and experimental validation of miRNA is a laborious and time-consuming process. For early prediction, multiple bioinformatics databases are available for miRNA target prediction; however, their utility can confuse amateur researchers in selecting the most appropriate tools for their study. OBJECTIVE This descriptive review aimed to analyse the usability of the existing database based on the following criteria: accessibility, efficiency, interpretability, updatability, and flexibility for miRNA target prediction of 3'UTR of mRNA in diverse species so that the researchers can utilize the database most appropriate to their research. METHODS A systematic literature search was performed in PubMed, Google Scholar and Scopus databases up to November 2022. ≥10,000 articles found online, including ⁓130 miRNA tools, which contain various information on miRNA. Out of them, 31 databases that provide information on validated 3'UTR miRNAs target databases were included and analysed in this review. RESULTS These miRNA database tools are being used in varied areas of biological research to select the most suitable miRNA for their experimental validation. These databases, updated until the year 2021, consist of miRNA-related data from humans, animals, mice, plants, viruses etc. They contain 525-29806351 data entries, and information from most databases is freely available on the online platform. CONCLUSION Reviewed databases provide significant information, but not all information is accurate or up-to-date. Therefore, Diana-TarBase and miRWalk are the most comprehensive and up-to-date databases.
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Affiliation(s)
- Sonu Singh Ahirwar
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, AIIMS Bhopal, Saket Nagar, Bhopal, MP, India
| | - Rehma Rizwan
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, AIIMS Bhopal, Saket Nagar, Bhopal, MP, India
| | - Samdish Sethi
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, AIIMS Bhopal, Saket Nagar, Bhopal, MP, India
| | - Zainab Shahid
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, AIIMS Bhopal, Saket Nagar, Bhopal, MP, India
| | - Shivani Malviya
- Department of Biochemistry and Genetics, Barkatullah University, Bhopal, Madhya Pradesh, 462026, India
| | - Rekha Khandia
- Department of Biochemistry and Genetics, Barkatullah University, Bhopal, Madhya Pradesh, 462026, India
| | - Amit Agarwal
- Department of Neurosurgery, All India Institute of Medical Sciences Bhopal, Bhopal MP, 462020, India
| | - Ashwin Kotnis
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, AIIMS Bhopal, Saket Nagar, Bhopal, MP, India
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Kariuki D, Asam K, Aouizerat BE, Lewis KA, Florez JC, Flowers E. Review of databases for experimentally validated human microRNA-mRNA interactions. Database (Oxford) 2023; 2023:7142843. [PMID: 37098414 PMCID: PMC10129384 DOI: 10.1093/database/baad014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/13/2023] [Accepted: 03/09/2023] [Indexed: 04/27/2023]
Abstract
MicroRNAs (miRs) may contribute to disease etiology by influencing gene expression. Numerous databases are available for miR target prediction and validation, but their functionality is varied, and outputs are not standardized. The purpose of this review is to identify and describe databases for cataloging validated miR targets. Using Tools4miRs and PubMed, we identified databases with experimentally validated targets, human data, and a focus on miR-messenger RNA (mRNA) interactions. Data were extracted about the number of times each database was cited, the number of miRs, the target genes, the interactions per database, experimental methodology and key features of each database. The search yielded 10 databases, which in order of most cited to least were: miRTarBase, starBase/The Encyclopedia of RNA Interactomes, DIANA-TarBase, miRWalk, miRecords, miRGator, miRSystem, miRGate, miRSel and targetHub. Findings from this review suggest that the information presented within miR target validation databases can be enhanced by adding features such as flexibility in performing queries in multiple ways, downloadable data, ongoing updates and integrating tools for further miR-mRNA target interaction analysis. This review is designed to aid researchers, especially those new to miR bioinformatics tools, in database selection and to offer considerations for future development and upkeep of validation tools. Database URL http://mirtarbase.cuhk.edu.cn/.
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Affiliation(s)
- Dorian Kariuki
- Department of Physiological Nursing, University of California, San Francisco, CA 94143, USA
| | - Kesava Asam
- Bluestone Center for Clinical Research, New York University, New York, CA 10010, USA
| | - Bradley E Aouizerat
- Bluestone Center for Clinical Research, New York University, New York, CA 10010, USA
- Department of Oral and Maxillofacial Surgery, New York University, New York, CA 10010, USA
| | - Kimberly A Lewis
- Department of Physiological Nursing, University of California, San Francisco, CA 94143, USA
| | - Jose C Florez
- Department of Medicine, Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Elena Flowers
- Department of Physiological Nursing, University of California, San Francisco, CA 94143, USA
- Institute for Human Genetics, University of California, San Francisco, CA 94143, USA
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Schmitz U. Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets. Methods Mol Biol 2023; 2630:155-177. [PMID: 36689183 DOI: 10.1007/978-1-0716-2982-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.
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Affiliation(s)
- Ulf Schmitz
- Department of Molecular & Cell Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Douglas, Australia.
- Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia.
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Literature Mining of Disease Associated Noncoding RNA in the Omics Era. Molecules 2022; 27:molecules27154710. [PMID: 35897884 PMCID: PMC9331993 DOI: 10.3390/molecules27154710] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/20/2022] [Accepted: 07/22/2022] [Indexed: 02/01/2023] Open
Abstract
Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting knowledge resources. Thus, text-mining methods have been increasingly adapted for automatic extraction of relations between an ncRNA and its target or a disease condition from biomedical literature. These bioinformatics tools can also assist in more complex research, such as database curation of candidate ncRNAs and hypothesis generation with respect to pathophysiological mechanisms. In this concise review, we first introduced basic concepts and workflow of literature mining systems. Then, we compared available bioinformatics tools tailored for ncRNA studies, including the tasks, applicability, and limitations. Their powerful utilities and flexibility are demonstrated by examples in a variety of diseases, such as Alzheimer’s disease, atherosclerosis and cancers. Finally, we outlined several challenges from the viewpoints of both system developers and end users. We concluded that the application of text-mining techniques will booster disease-associated ncRNA discoveries in the biomedical literature and enable integrative biology in the current omics era.
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Kong X, Wang J, Cao Y, Lu X, Zhang H, Zhang X, Bo C, Bai M, Li S, Jiao Y, Wang L. Construction of miRNA-regulated drug-pathway network to screen drug repurposing candidates for multiple sclerosis. Medicine (Baltimore) 2022; 101:e29107. [PMID: 35356949 PMCID: PMC10684250 DOI: 10.1097/md.0000000000029107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 02/28/2022] [Indexed: 02/07/2023] Open
Abstract
ABSTRACT Given the high disability rate of multiple sclerosis (MS), there is a need for safer and more effective therapeutic agents. Existing literature highlights the prominent roles of miRNA in MS pathophysiology. Nevertheless, there are few studies that have explored the usefulness of existing drugs in treating MS through potential miRNA-modulating abilities.The current investigation identifies genes that may exacerbate the risk of MS due to their respective miRNA associations. These findings were then used to determine potential drug candidates through the construction of miRNA-regulated drug-pathway network through genes. We uncovered a total of 48 MS risk pathways, 133 MS risk miRNAs, and 186 drugs that can affect these pathways. Potential MS risk miRNAs that are also regulated by therapeutic candidates were hsa05215 and hsa05152. We analyzed the properties of the miRNA-regulated drug-pathway network through genes and uncovered a number of novel MS agents by assessing their respective Z-values.A total of 20 likely drug candidates were identified, including human immunoglobulin, aspirin, alemtuzumab, minocycline, abciximab, alefacept, palivizumab, bevacizumab, efalizumab, tositumomab, minocycline, etanercept, catumaxomab, and sarilumab. Each of these agents were then explored with regards to their likely mechanism of action in treating MS.The current investigation provides a fresh perspective on MS biological mechanisms as well as likely treatment strategies.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Lihua Wang
- Correspondence: Lihua Wang, Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin 150086, Heilongjiang Province, China(e-mail: ).
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9
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Circulating MicroRNAs as Cancer Biomarkers in Liquid Biopsies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:23-73. [DOI: 10.1007/978-3-031-08356-3_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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10
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Friedrich J, Hammes HP, Krenning G. miRetrieve-an R package and web application for miRNA text mining. NAR Genom Bioinform 2021; 3:lqab117. [PMID: 34988440 PMCID: PMC8696973 DOI: 10.1093/nargab/lqab117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 11/01/2021] [Accepted: 12/03/2021] [Indexed: 12/30/2022] Open
Abstract
microRNAs (miRNAs) regulate gene expression and thereby influence biological processes in health and disease. As a consequence, miRNAs are intensely studied and literature on miRNAs has been constantly growing. While this growing body of literature reflects the interest in miRNAs, it generates a challenge to maintain an overview, and the comparison of miRNAs that may function across diverse disease fields is complex due to this large number of relevant publications. To address these challenges, we designed miRetrieve, an R package and web application that provides an overview on miRNAs. By text mining, miRetrieve can characterize and compare miRNAs within specific disease fields and across disease areas. This overview provides focus and facilitates the generation of new hypotheses. Here, we explain how miRetrieve works and how it is used. Furthermore, we demonstrate its applicability in an exemplary case study and discuss its advantages and disadvantages.
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Affiliation(s)
- Julian Friedrich
- Cardiovascular Regenerative Medicine (CAVAREM), Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 (EA11), 9713 GZ Groningen, The Netherlands
- 5th Medical Department, Section of Endocrinology, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Hans-Peter Hammes
- 5th Medical Department, Section of Endocrinology, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
- European Center of Angioscience, Medical Faculty Mannheim, University of Heidelberg, 68167 Mannheim, Germany
| | - Guido Krenning
- Cardiovascular Regenerative Medicine (CAVAREM), Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 (EA11), 9713 GZ Groningen, The Netherlands
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11
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Roychowdhury D, Gupta S, Qin X, Arighi CN, Vijay-Shanker K. emiRIT: a text-mining-based resource for microRNA information. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6287648. [PMID: 34048547 PMCID: PMC8163238 DOI: 10.1093/database/baab031] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/15/2021] [Accepted: 05/04/2021] [Indexed: 01/18/2023]
Abstract
microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information. Database URL: https://research.bioinformatics.udel.edu/emirit/
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Affiliation(s)
- Debarati Roychowdhury
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, 18 Amstel Ave, Newark, DE 19716, USA
| | - Samir Gupta
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, 18 Amstel Ave, Newark, DE 19716, USA
| | - Xihan Qin
- Department of Computer and Information Sciences, Center of Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Room 205, Newark, DE 19711, USA
| | - Cecilia N Arighi
- Department of Computer and Information Sciences, Center of Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Room 205, Newark, DE 19711, USA
| | - K Vijay-Shanker
- Department of Computer and Information Sciences, University of Delaware, 101 Smith Hall, 18 Amstel Ave, Newark, DE 19716, USA
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12
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Pourteymourfard Tabrizi Z, Jami MS. Selection of suitable bioinformatic tools in micro-RNA research. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Hernández-Romero IA, Guerra-Calderas L, Salgado-Albarrán M, Maldonado-Huerta T, Soto-Reyes E. The Regulatory Roles of Non-coding RNAs in Angiogenesis and Neovascularization From an Epigenetic Perspective. Front Oncol 2019; 9:1091. [PMID: 31709179 PMCID: PMC6821677 DOI: 10.3389/fonc.2019.01091] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 10/03/2019] [Indexed: 12/13/2022] Open
Abstract
Angiogenesis is a crucial process for organ morphogenesis and growth during development, and it is especially relevant during the repair of wounded tissue in adults. It is coordinated by an equilibrium of pro- and anti-angiogenic factors; nevertheless, when affected, it promotes several diseases. Lately, a growing body of evidence is indicating that non-coding RNAs (ncRNAs), such as miRNAs, circRNAs, and lncRNAs, play critical roles in angiogenesis. These ncRNAs can act in cis or trans and alter gene transcription by several mechanisms including epigenetic processes. In the following pages, we will discuss the functions of ncRNAs in the regulation of angiogenesis and neovascularization, both in normal and disease contexts, from an epigenetic perspective. Additionally, we will describe the contribution of Next-Generation Sequencing (NGS) techniques to the discovery and understanding of the role of ncRNAs in angiogenesis.
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Affiliation(s)
| | | | | | | | - Ernesto Soto-Reyes
- Natural Sciences Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City, Mexico
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14
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Mulholland EJ, Green WP, Buckley NE, McCarthy HO. Exploring the Potential of MicroRNA Let-7c as a Therapeutic for Prostate Cancer. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:927-937. [PMID: 31760377 PMCID: PMC6883330 DOI: 10.1016/j.omtn.2019.09.031] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 09/02/2019] [Accepted: 09/08/2019] [Indexed: 01/20/2023]
Abstract
Prostate cancer (PCa) is one of the leading causes of mortality worldwide and often presents with aberrant microRNA (miRNA) expression. Identifying and understanding the unique expression profiles could aid in the detection and treatment of this disease. This review aims to identify miRNAs as potential therapeutic targets for PCa. Three bio-informatic searches were conducted to identify miRNAs that are reportedly implicated in the pathogenesis of PCa. Only hsa-Lethal-7 (let-7c), recognized for its role in PCa pathogenesis, was common to all three databases. Three further database searches were conducted to identify known targets of hsa-let-7c. Four targets were identified, HMGA2, c-Myc (MYC), TRAIL, and CASP3. An extensive review of the literature was undertaken to assess the role of hsa-let-7c in the progression of other malignancies and to evaluate its potential as a therapeutic target for PCa. The heterogeneous nature of cancer makes it logical to develop mechanisms by which the treatment of malignancies is tailored to an individual, harnessing specific knowledge of the underlying biology of the disease. Resetting cellular miRNA levels is an exciting prospect that will allow this ambition to be realized.
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Affiliation(s)
- Eoghan J Mulholland
- Gastrointestinal Stem Cell Biology Laboratory, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - William P Green
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland
| | - Niamh E Buckley
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland
| | - Helen O McCarthy
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, Northern Ireland.
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15
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Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH, Chiew MY, Tai CS, Wei TY, Tsai TR, Huang HT, Wang CY, Wu HY, Ho SY, Chen PR, Chuang CH, Hsieh PJ, Wu YS, Chen WL, Li MJ, Wu YC, Huang XY, Ng FL, Buddhakosai W, Huang PC, Lan KC, Huang CY, Weng SL, Cheng YN, Liang C, Hsu WL, Huang HD. miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 2019; 46:D296-D302. [PMID: 29126174 PMCID: PMC5753222 DOI: 10.1093/nar/gkx1067] [Citation(s) in RCA: 1256] [Impact Index Per Article: 251.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 10/25/2017] [Indexed: 01/16/2023] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.
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Affiliation(s)
- Chih-Hung Chou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Sirjana Shrestha
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chi-Dung Yang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 350, Taiwan
| | - Nai-Wen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 106, Taiwan
| | - Yu-Ling Lin
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Kuang-Wen Liao
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wei-Chi Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Ting-Hsuan Sun
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Siang-Jyun Tu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wei-Hsiang Lee
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Clinical Research Center, Chung Shan Medical University Hospital, Taichung, 402, Taiwan
| | - Men-Yee Chiew
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chun-San Tai
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Ting-Yen Wei
- Interdisciplinary Program of Life Science, National Tsing Hua University, Hsinchu, 300, Taiwan
| | - Tzi-Ren Tsai
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hsin-Tzu Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chung-Yu Wang
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hsin-Yi Wu
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Shu-Yi Ho
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Pin-Rong Chen
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Cheng-Hsun Chuang
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Pei-Jung Hsieh
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yi-Shin Wu
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wen-Liang Chen
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Meng-Ju Li
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Department of Pediatrics, National Taiwan University Hospital Hsinchu Branch, Hsinchu, 300, Taiwan
| | - Yu-Chun Wu
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Xin-Yi Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Fung Ling Ng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Waradee Buddhakosai
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Pei-Chun Huang
- Delivery Room, Department of Nursing, National Taiwan University Hospital Hsinchu Branch, Hsinchu, 300, Taiwan
| | - Kuan-Chun Lan
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chia-Yen Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Cathay General Hospital, Taipei, 106, Taiwan
| | - Shun-Long Weng
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu, 300, Taiwan.,Mackay Medicine, Nursing and Management College, Taipei, 112, Taiwan.,Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan
| | - Yeong-Nan Cheng
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chao Liang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan
| | - Hsien-Da Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong Province, 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong Province, 518172, China
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16
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Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome. Methods Mol Biol 2019; 1912:215-250. [PMID: 30635896 DOI: 10.1007/978-1-4939-8982-9_9] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.
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17
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Abstract
One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.
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18
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Dai HJ, Wang CK, Chang NW, Huang MS, Jonnagaddala J, Wang FD, Hsu WL. Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5365313. [PMID: 30809637 PMCID: PMC6391575 DOI: 10.1093/database/baz030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 02/01/2019] [Accepted: 02/06/2019] [Indexed: 01/08/2023]
Abstract
The detection of MicroRNA (miRNA) mentions in scientific literature facilitates researchers with the ability to find relevant and appropriate literature based on queries formulated using miRNA information. Considering most published biological studies elaborated on signal transduction pathways or genetic regulatory information in the form of figure captions, the extraction of miRNA from both the main content and figure captions of a manuscript is useful in aggregate analysis and comparative analysis of the studies published. In this study, we present a statistical principle-based miRNA recognition and normalization method to identify miRNAs and link them to the identifiers in the Rfam database. As one of the core components in the text mining pipeline of the database miRTarBase, the proposed method combined the advantages of previous works relying on pattern, dictionary and supervised learning and provided an integrated solution for the problem of miRNA identification. Furthermore, the knowledge learned from the training data was organized in a human-interpretable manner to understand the reason why the system considers a span of text as a miRNA mention, and the represented knowledge can be further complemented by domain experts. We studied the ambiguity level of miRNA nomenclature to connect the miRNA mentions to the Rfam database and evaluated the performance of our approach on two datasets: the BioCreative VI Bio-ID corpus and the miRNA interaction corpus by extending the later corpus with additional Rfam normalization information. Our study highlights and also proposes a better understanding of the challenges associated with miRNA identification and normalization in scientific literature and the research gap that needs to be further explored in prospective studies.
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Affiliation(s)
- Hong-Jie Dai
- Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan, ROC
| | - Chen-Kai Wang
- Big Data Laboratories, Chunghwa Telecom Co., Taoyuan, Taiwan, ROC
| | - Nai-Wen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Ming-Siang Huang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Jitendra Jonnagaddala
- School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia
| | - Feng-Duo Wang
- Department of Computer Science and Information Engineering, National Taitung University, Taitung, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
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19
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Chen W, Zhang L, Shi C, Ren G, Kong Q, Qin C. Comprehensive analysis of hippocampal miRNAomes in humans and mice. Epigenomics 2018; 10:813-828. [PMID: 29979109 DOI: 10.2217/epi-2017-0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM This study aims to explore the similarity and difference of hippocampal miRNAomes between humans and mice. MATERIALS & METHODS A systematic comparison of the miRNAomes between healthy human and mouse hippocampi was performed using high-throughput sequencing followed by bioinformatic analyses. RESULTS A novel miRNA termed novel-21-5p and a human-specific miR-656-3p were identified in human hippocampi, which were expressed ubiquitously and predicted to be associated with neural activities. Compared with mouse, abundantly expressed miRNAs in human hippocampus were notably enriched in pathways pertaining to neural activities, such as neurotrophin TRK receptor signaling pathway, axon guidance and synaptic transmission. Expression pattern of orthologous miRNAs between human and mouse hippocampi was conserved. Meanwhile, the expression conservation was positively correlated with the sequence conservation. CONCLUSION Hippocampal miRNAomes between humans and mice were overall comparable; the differences in expression or function across species should be considered when constructing mouse models.
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Affiliation(s)
- Wei Chen
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) & Comparative Medicine Centre, Peking Union Medical Collage (PUMC), Beijing, PR China.,Experimental & Translational Research Center, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
| | - Ling Zhang
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) & Comparative Medicine Centre, Peking Union Medical Collage (PUMC), Beijing, PR China
| | - Changhua Shi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) & Comparative Medicine Centre, Peking Union Medical Collage (PUMC), Beijing, PR China
| | - Guanhua Ren
- Library of Peking University First Hospital, Beijing, PR China
| | - Qi Kong
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) & Comparative Medicine Centre, Peking Union Medical Collage (PUMC), Beijing, PR China
| | - Chuan Qin
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences (CAMS) & Comparative Medicine Centre, Peking Union Medical Collage (PUMC), Beijing, PR China
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20
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Zhao B, Chen Y, Mu L, Hu S, Wu X. Identification and profiling of microRNA between back and belly Skin in Rex rabbits (Oryctolagus cuniculus). WORLD RABBIT SCIENCE 2018. [DOI: 10.4995/wrs.2018.7058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Skin is an important trait for Rex rabbits and skin development is influenced by many processes, including hair follicle cycling, keratinocyte differentiation and formation of coat colour and skin morphogenesis. We identified differentially expressed microRNAs (miRNAs) between the back and belly skin in Rex rabbits. In total, 211 miRNAs (90 upregulated miRNAs and 121 downregulated miRNAs) were identified with a |log<sub>2</sub> (fold change)|>1 and <em>P</em>-value<0.05. Using target gene prediction for the miRNAs, differentially expressed predicted target genes were identified and the functional enrichment and signalling pathways of these target genes were processed to reveal their biological functions. A number of differentially expressed miRNAs were found to be involved in regulation of the cell cycle, skin epithelium differentiation, keratinocyte proliferation, hair follicle development and melanogenesis. In addition, target genes regulated by miRNAs play key roles in the activities of the Hedgehog signalling pathway, Wnt signalling pathway, Osteoclast differentiation and MAPK pathway, revealing mechanisms of skin development. Nine candidate miRNAs and 5 predicted target genes were selected for verification of their expression by quantitative reverse transcription polymerase chain reaction. A regulation network of miRNA and their target genes was constructed by analysing the GO enrichment and signalling pathways. Further studies should be carried out to validate the regulatory relationships between candidate miRNAs and their target genes.
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21
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Shukla V, Varghese VK, Kabekkodu SP, Mallya S, Satyamoorthy K. A compilation of Web-based research tools for miRNA analysis. Brief Funct Genomics 2018; 16:249-273. [PMID: 28334134 DOI: 10.1093/bfgp/elw042] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Since the discovery of microRNAs (miRNAs), a class of noncoding RNAs that regulate the gene expression posttranscriptionally in sequence-specific manner, there has been a release of number of tools useful for both basic and advanced applications. This is because of the significance of miRNAs in many pathophysiological conditions including cancer. Numerous bioinformatics tools that have been developed for miRNA analysis have their utility for detection, expression, function, target prediction and many other related features. This review provides a comprehensive assessment of web-based tools for the miRNA analysis that does not require prior knowledge of any computing languages.
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22
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Kandhro AH, Shoombuatong W, Nantasenamat C, Prachayasittikul V, Nuchnoi P. The MicroRNA Interaction Network of Lipid Diseases. Front Genet 2017; 8:116. [PMID: 29018475 PMCID: PMC5615414 DOI: 10.3389/fgene.2017.00116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2017] [Accepted: 08/24/2017] [Indexed: 02/06/2023] Open
Abstract
Background: Dyslipidemia is one of the major forms of lipid disorder, characterized by increased triglycerides (TGs), increased low-density lipoprotein-cholesterol (LDL-C), and decreased high-density lipoprotein-cholesterol (HDL-C) levels in blood. Recently, MicroRNAs (miRNAs) have been reported to involve in various biological processes; their potential usage being a biomarkers and in diagnosis of various diseases. Computational approaches including text mining have been used recently to analyze abstracts from the public databases to observe the relationships/associations between the biological molecules, miRNAs, and disease phenotypes. Materials and Methods: In the present study, significance of text mined extracted pair associations (miRNA-lipid disease) were estimated by one-sided Fisher's exact test. The top 20 significant miRNA-disease associations were visualized on Cytoscape. The CyTargetLinker plug-in tool on Cytoscape was used to extend the network and predicts new miRNA target genes. The Biological Networks Gene Ontology (BiNGO) plug-in tool on Cytoscape was used to retrieve gene ontology (GO) annotations for the targeted genes. Results: We retrieved 227 miRNA-lipid disease associations including 148 miRNAs. The top 20 significant miRNAs analysis on CyTargetLinker provides defined, predicted and validated gene targets, further targeted genes analyzed by BiNGO showed targeted genes were significantly associated with lipid, cholesterol, apolipoprotein, and fatty acids GO terms. Conclusion: We are the first to provide a reliable miRNA-lipid disease association network based on text mining. This could help future experimental studies that aim to validate predicted gene targets.
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Affiliation(s)
- Abdul H. Kandhro
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
| | - Virapong Prachayasittikul
- Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
| | - Pornlada Nuchnoi
- Center for Research and Innovation, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
- Department of Clinical Microscopy, Faculty of Medical Technology, Mahidol UniversityBangkok, Thailand
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23
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Chen X, Zhao W, Yuan Y, Bai Y, Sun Y, Zhu W, Du Z. MicroRNAs tend to synergistically control expression of genes encoding extensively-expressed proteins in humans. PeerJ 2017; 5:e3682. [PMID: 28828274 PMCID: PMC5560240 DOI: 10.7717/peerj.3682] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Accepted: 07/22/2017] [Indexed: 01/02/2023] Open
Abstract
Considering complicated microRNA (miRNA) biogenesis and action mechanisms, it was thought so high energy-consuming for a cell to afford simultaneous over-expression of many miRNAs. Thus it prompts that an alternative miRNA regulation pattern on protein-encoding genes must exist, which has characteristics of energy-saving and precise protein output. In this study, expression tendency of proteins encoded by miRNAs’ target genes was evaluated in human organ scale, followed by quantitative assessment of miRNA synergism. Expression tendency analysis suggests that universally expressed proteins (UEPs) tend to physically interact in clusters and participate in fundamental biological activities whereas disorderly expressed proteins (DEPs) are inclined to relatively independently execute organ-specific functions. Consistent with this, miRNAs that mainly target UEP-encoding mRNAs, such as miR-21, tend to collaboratively or even synergistically act with other miRNAs in fine-tuning protein output. Synergistic gene regulation may maximize miRNAs’ efficiency with less dependence on miRNAs’ abundance and overcome the deficiency that targeting plenty of genes by single miRNA makes miRNA-mediated regulation high-throughput but insufficient due to target gene dilution effect. Furthermore, our in vitro experiment verified that merely 25 nM transfection of miR-21 be sufficient to influence the overall state of various human cells. Thus miR-21 was identified as a hub in synergistic miRNA–miRNA interaction network. Our findings suggest that synergistic miRNA–miRNA interaction is an important endogenous miRNA regulation mode, which ensures adequate potency of miRNAs at low abundance, especially those implicated in fundamental biological regulation.
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Affiliation(s)
- Xue Chen
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Wei Zhao
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Ye Yuan
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Yan Bai
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Yong Sun
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Wenliang Zhu
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
| | - Zhimin Du
- Department of Pharmacy, The Second Affiliated Hospital of Harbin Medical University (Institute of Clinical Pharmacy, The Heilongjiang Key Laboratory of Drug Research, Harbin Medical University), Harbin, China
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24
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Singh NK. miRNAs target databases: developmental methods and target identification techniques with functional annotations. Cell Mol Life Sci 2017; 74:2239-2261. [PMID: 28204845 PMCID: PMC11107700 DOI: 10.1007/s00018-017-2469-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 01/09/2017] [Accepted: 01/18/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE microRNA (miRNA) regulates diverse biological mechanisms and metabolisms in plants and animals. Thus, the discoveries of miRNA has revolutionized the life sciences and medical research.The miRNA represses and cleaves the targeted mRNA by binding perfect or near perfect or imperfect complementary base pairs by RNA-induced silencing complex (RISC) formation during biogenesis process. One miRNA interacts with one or more mRNA genes and vice versa, hence takes part in causing various diseases. In this paper, the different microRNA target databases and their functional annotations developed by various researchers have been reviewed. The concurrent research review aims at comprehending the significance of miRNA and presenting the existing status of annotated miRNA target resources built by researchers henceforth discovering the knowledge for diagnosis and prognosis. METHODS AND RESULTS This review discusses the applications and developmental methodologies for constructing target database as well as the utility of user interface design. An integrated architecture is drawn and a graphically comparative study of present status of miRNA targets in diverse diseases and various biological processes is performed. These databases comprise of information such as miRNA target-associated disease, transcription factor binding sites (TFBSs) in miRNA genomic locations, polymorphism in miRNA target, A-to-I edited target, Gene Ontology (GO), genome annotations, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, target expression analysis, TF-miRNA and miRNA-mRNA interaction networks, drugs-targets interactions, etc. CONCLUSION miRNA target databases contain diverse experimentally and computationally predicted target through various algorithms. The comparison of various miRNA target database has been performed on various parameters. The computationally predicted target databases suffer from false positive information as there is no common theory for prediction of miRNA targets. The review conclusion emphasizes the need of more intelligent computational improvement for the miRNA target identification, their functional annotations and datasbase development.
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Affiliation(s)
- Nagendra Kumar Singh
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India.
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25
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Lamurias A, Clarke LA, Couto FM. Extracting microRNA-gene relations from biomedical literature using distant supervision. PLoS One 2017; 12:e0171929. [PMID: 28263989 PMCID: PMC5338769 DOI: 10.1371/journal.pone.0171929] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 01/29/2017] [Indexed: 11/18/2022] Open
Abstract
Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.
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Affiliation(s)
- Andre Lamurias
- LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Luka A. Clarke
- BioISI: Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
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26
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Cao Y, Lu X, Wang J, Zhang H, Liu Z, Xu S, Wang T, Ning S, Xiao B, Wang L. Construction of an miRNA-regulated drug-pathway network reveals drug repurposing candidates for myasthenia gravis. Int J Mol Med 2017; 39:268-278. [PMID: 28075449 PMCID: PMC5358695 DOI: 10.3892/ijmm.2017.2853] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 01/04/2017] [Indexed: 12/31/2022] Open
Abstract
Myasthenia gravis (MG) is a rare debilitating autoimmune neuromuscular disorder. Many studies have focused on the mechanism and treatment strategies of MG. However, the exact pathogenesis of MG and effective treatment strategies remain unclear. Recent studies have indicated that microRNAs (miRNAs or miRs) can regulate the pathological pathways of MG, suggesting their potential role in novel treatments. In the present study, we created a comprehensive catalog of experimentally confirmed MG risk genes and miRNAs by manually mining published literature and public databases. Based on these genes and miRNAs, we identified 41 MG risk pathways and 105 approved drugs that can affect these pathways. Some important MG-related pathways, such as hsa04060 (cytokine-cytokine receptor interaction) and hsa05200 (pathway in cancer), were found to be regulated by MG risk miRNAs and drugs. Furthermore, we constructed an miRNA-regulated drug-pathway network and identified miRNAs and drugs that synergistically regulate key MG pathways and biological processes. We developed a drug repurposing strategy to identify 25 drug repurposing candidates for MG; several of these drugs, such as rituximab, adalimumab, sunitinib, and muromonab, have the potential to be novel MG treatment drugs. This study provides novel insight into the pathogenesis of MG and potential drug candidates for MG were identified.
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Affiliation(s)
- Yuze Cao
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Xiaoyan Lu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Jianjian Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Huixue Zhang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Zhaojun Liu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Si Xu
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Tianfeng Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Shangwei Ning
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, P.R. China
| | - Lihua Wang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang 150081, P.R. China
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27
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Amirkhah R, Meshkin HN, Farazmand A, Rasko JEJ, Schmitz U. Computational and Experimental Identification of Tissue-Specific MicroRNA Targets. Methods Mol Biol 2017; 1580:127-147. [PMID: 28439832 DOI: 10.1007/978-1-4939-6866-4_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.
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Affiliation(s)
- Raheleh Amirkhah
- Reza Institute of Cancer Bioinformatics and Personalized Medicine, Mashhad, Iran
| | - Hojjat Naderi Meshkin
- Stem Cells and Regenerative Medicine Research Group, Academic Center for Education, Culture Research (ACECR), Khorasan Razavi Branch, Mashhad, Iran
| | - Ali Farazmand
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - John E J Rasko
- Gene & Stem Cell Therapy Program, Centenary Institute, Camperdown; Sydney Medical School, University of Sydney, Camperdown, NSW, 2050, Australia
| | - Ulf Schmitz
- Gene & Stem Cell Therapy Program, Centenary Institute, Camperdown; Sydney Medical School, University of Sydney, Camperdown, NSW, 2050, Australia.
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28
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Roy S, Curry BC, Madahian B, Homayouni R. Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts. BMC Bioinformatics 2016; 17:350. [PMID: 27766940 PMCID: PMC5073981 DOI: 10.1186/s12859-016-1223-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Background The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. Results For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms. Conclusions LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1223-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sujoy Roy
- Bioinformatics Program, University of Memphis, Memphis, 38152, USA.,Center for Translational Informatics, University of Memphis, Memphis, 38152, USA
| | - Brandon C Curry
- Bioinformatics Program, University of Memphis, Memphis, 38152, USA
| | - Behrouz Madahian
- Department of Mathematical Sciences, University of Memphis, Memphis, 38152, USA
| | - Ramin Homayouni
- Bioinformatics Program, University of Memphis, Memphis, 38152, USA. .,Center for Translational Informatics, University of Memphis, Memphis, 38152, USA. .,Department of Biology, University of Memphis, Memphis, 38152, USA.
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29
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Wagner M, Vicinus B, Muthra ST, Richards TA, Linder R, Frick VO, Groh A, Rubie C, Weichert F. Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years. Comput Biol Med 2016; 73:173-85. [PMID: 27208610 DOI: 10.1016/j.compbiomed.2016.03.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Revised: 03/19/2016] [Accepted: 03/21/2016] [Indexed: 11/29/2022]
Abstract
BACKGROUND The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining. METHODS A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features. RESULTS The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable. CONCLUSIONS The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable.
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Affiliation(s)
- Mathias Wagner
- Department of Pathology, University of Saarland, Homburg Saar Campus, Homburg Saar, Germany
| | - Benjamin Vicinus
- Department of General, Visceral, Vascular and Pediatric Surgery, University of Saarland, Homburg Saar Campus, Homburg Saar, Germany; Institute of Virology, University of Saarland, Homburg Saar Campus, Homburg Saar, Germany
| | - Sherieda T Muthra
- Lombardi Comprehensive Cancer Center, Georgetown University, 37th & O St NW, Washington, DC 20057, United States of America.
| | - Tereza A Richards
- The Medical Library, University of the West Indies, Mona, Kingston, Jamaica
| | - Roland Linder
- Institute of Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Vilma Oliveira Frick
- Department of General, Visceral, Vascular and Pediatric Surgery, University of Saarland, Homburg Saar Campus, Homburg Saar, Germany
| | - Andreas Groh
- Department of Mathematics, University of Saarland, Saarbrücken Campus, Saarbrücken, Germany
| | - Claudia Rubie
- Department of General, Visceral, Vascular and Pediatric Surgery, University of Saarland, Homburg Saar Campus, Homburg Saar, Germany
| | - Frank Weichert
- Department of Computer Science VII, Technical University of Dortmund, Dortmund, Germany
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30
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Bayer J, Kuenne C, Preussner J, Looso M. LimiTT: link miRNAs to targets. BMC Bioinformatics 2016; 17:210. [PMID: 27170328 PMCID: PMC4866021 DOI: 10.1186/s12859-016-1070-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2015] [Accepted: 05/04/2016] [Indexed: 11/29/2022] Open
Abstract
Background MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation of miRNA target interactions (MTIs) by high throughput screenings is challenging, as frequently used in silico target prediction tools are prone to emit false positives. This issue is aggravated for niche model organisms, where validated miRNAs and MTIs both have to be transferred from well described model organisms. Even though DBs exist that contain experimentally validated MTIs, they are limited in their search options and they utilize different miRNA and target identifiers. Results The implemented pipeline LimiTT integrates four existing DBs containing experimentally validated MTIs. In contrast to other cumulative databases (DBs), LimiTT includes MTI data of 26 species. Additionally, the pipeline enables the identification and enrichment analysis of MTIs with and without species specificity based on dynamic quality criteria. Multiple tabular and graphical outputs are generated to permit the detailed assessment of results. Conclusion Our freely available web-based pipeline LimiTT (https://bioinformatics.mpi-bn.mpg.de/) is optimized to determine MTIs with and without species specification. It links miRNAs and/or putative targets with high granularity. The integrated mapping to homologous target identifiers enables the identification of MTIs not only for standard models, but for niche model organisms as well. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1070-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Julia Bayer
- Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany
| | - Carsten Kuenne
- Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany
| | - Jens Preussner
- Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany
| | - Mario Looso
- Group of Bioinformatics, Max Planck Institute for Heart and Lung Research, Ludwigstrasse 43, D-61231, Bad Nauheim, Germany.
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31
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Gupta S, Ross KE, Tudor CO, Wu CH, Schmidt CJ, Vijay-Shanker K. miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases. J Biomed Semantics 2016; 7:9. [PMID: 27216254 PMCID: PMC4877743 DOI: 10.1186/s13326-015-0044-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 12/21/2015] [Indexed: 12/31/2022] Open
Abstract
Background MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation. Methods We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence. Results miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD. We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46–90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator’s ranked list. Conclusions miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as “In what diseases does microRNA regulation of apoptosis play a role?” or “Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?”.” Electronic supplementary material The online version of this article (doi:10.1186/s13326-015-0044-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Samir Gupta
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA.
| | - Karen E Ross
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Catalina O Tudor
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Cathy H Wu
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA.,Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, 19711, USA
| | - Carl J Schmidt
- Department of Food and Animal Sciences, University of Delaware, Newark, DE, 19711, USA
| | - K Vijay-Shanker
- Department of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA
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32
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Lee S, Hwang S, Yu HJ, Oh D, Choi YJ, Kim MC, Kim Y, Ryu DY. Expression of microRNAs in Horse Plasma and Their Characteristic Nucleotide Composition. PLoS One 2016; 11:e0146374. [PMID: 26731407 PMCID: PMC4711666 DOI: 10.1371/journal.pone.0146374] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 12/16/2015] [Indexed: 11/19/2022] Open
Abstract
MicroRNAs (miRNAs) in blood plasma are stable under high levels of ribonuclease activity and could function in tissue-to-tissue communication, suggesting that they may have distinctive structural characteristics compared with non-circulating miRNAs. In this study, the expression of miRNAs in horse plasma and their characteristic nucleotide composition were examined and compared with non-plasma miRNAs. Highly expressed plasma miRNA species were not part of the abundant group of miRNAs in non-plasma tissues, except for the eca-let-7 family. eca-miR-486-5p, -92a, and -21 were among the most abundant plasma miRNAs, and their human orthologs also belong to the most abundant group of miRNAs in human plasma. Uracil and guanine were the most common nucleotides of both plasma and non-plasma miRNAs. Cytosine was the least common in plasma and non-plasma miRNAs, although levels were higher in plasma miRNAs. Plasma miRNAs also showed higher expression levels of miRNAs containing adenine and cytosine repeats, compared with non-plasma miRNAs. These observations indicate that miRNAs in the plasma have a unique nucleotide composition.
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Affiliation(s)
- Seungwoo Lee
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Seungwoo Hwang
- Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, South Korea
| | - Hee Jeong Yu
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Dayoung Oh
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Yu Jung Choi
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Myung-Chul Kim
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Yongbaek Kim
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Doug-Young Ryu
- BK21 Plus Program for Creative Veterinary Science Research, Research Institute for Veterinary Science and College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
- * E-mail:
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Chou CH, Chang NW, Shrestha S, Hsu SD, Lin YL, Lee WH, Yang CD, Hong HC, Wei TY, Tu SJ, Tsai TR, Ho SY, Jian TY, Wu HY, Chen PR, Lin NC, Huang HT, Yang TL, Pai CY, Tai CS, Chen WL, Huang CY, Liu CC, Weng SL, Liao KW, Hsu WL, Huang HD. miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 2015; 44:D239-47. [PMID: 26590260 PMCID: PMC4702890 DOI: 10.1093/nar/gkv1258] [Citation(s) in RCA: 798] [Impact Index Per Article: 88.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 10/30/2015] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides, which negatively regulate the gene expression at the post-transcriptional level. This study describes an update of the miRTarBase (http://miRTarBase.mbc.nctu.edu.tw/) that provides information about experimentally validated miRNA-target interactions (MTIs). The latest update of the miRTarBase expanded it to identify systematically Argonaute-miRNA-RNA interactions from 138 crosslinking and immunoprecipitation sequencing (CLIP-seq) data sets that were generated by 21 independent studies. The database contains 4966 articles, 7439 strongly validated MTIs (using reporter assays or western blots) and 348 007 MTIs from CLIP-seq. The number of MTIs in the miRTarBase has increased around 7-fold since the 2014 miRTarBase update. The miRNA and gene expression profiles from The Cancer Genome Atlas (TCGA) are integrated to provide an effective overview of this exponential growth in the miRNA experimental data. These improvements make the miRTarBase one of the more comprehensively annotated, experimentally validated miRNA-target interactions databases and motivate additional miRNA research efforts.
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Affiliation(s)
- Chih-Hung Chou
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Nai-Wen Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 106, Taiwan
| | - Sirjana Shrestha
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Sheng-Da Hsu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Yu-Ling Lin
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wei-Hsiang Lee
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan Clinical Research Center, Chung Shan Medical University Hospital, Taichung, 402, Taiwan
| | - Chi-Dung Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan Institute of Population Health Sciences, National Health Research Institutes, Miaoli, 350, Taiwan
| | - Hsiao-Chin Hong
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Ting-Yen Wei
- Interdisciplinary Program of Life Science, National Tsing Hua University, Hsinchu, 300, Taiwan
| | - Siang-Jyun Tu
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Tzi-Ren Tsai
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Shu-Yi Ho
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Ting-Yan Jian
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hsin-Yi Wu
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Pin-Rong Chen
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Nai-Chieh Lin
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Hsin-Tzu Huang
- Degree Program of Applied Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Tzu-Ling Yang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chung-Yuan Pai
- Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chun-San Tai
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wen-Liang Chen
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Chia-Yen Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Cathay General Hospital, Taipei, 106, Taiwan
| | - Chun-Chi Liu
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, 402, Taiwan
| | - Shun-Long Weng
- Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu, 300, Taiwan Mackay Medicine, Nursing and Management College, Taipei, 112, Taiwan Department of Medicine, Mackay Medical College, New Taipei City, 252, Taiwan
| | - Kuang-Wen Liao
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu, 300, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan
| | - Hsien-Da Huang
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 300, Taiwan Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, 300, Taiwan Center for Bioinformatics Research, National Chiao Tung University, Hsinchu, 300, Taiwan Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
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Li G, Ross KE, Arighi CN, Peng Y, Wu CH, Vijay-Shanker K. miRTex: A Text Mining System for miRNA-Gene Relation Extraction. PLoS Comput Biol 2015; 11:e1004391. [PMID: 26407127 PMCID: PMC4583433 DOI: 10.1371/journal.pcbi.1004391] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 06/08/2015] [Indexed: 12/27/2022] Open
Abstract
MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes. MicroRNAs (miRNAs) are an important class of RNAs that regulate a wide range of biological processes by post-transcriptional regulation of gene expression. The amount of literature describing experimentally validated miRNA targets is increasing rapidly, which poses a challenge to researchers and biocurators to stay up-to-date with the available information. Text mining methods have been used to extract miRNA-gene associated pairs and assist in curation. In this paper, we describe miRTex, a text mining system that extracts miRNA-target, miRNA-gene regulation and gene-miRNA regulation relations. We evaluate miRTex performance on two corpora, and show that the elaborate use of lexico-syntactic information and linguistic generalizations enables it to achieve the state-of-the-art performance. We have processed the all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset with miRTex, and provide a website to access the extraction results from all the Medline abstracts. The full-scale text mining results will be a useful resource for miRNA researchers, while the miRTex tool itself can be integrated into literature-based curation pipelines. We present two use cases (for animal and plant miRNAs, respectively) that show how the full-scale text mining can be used in combination with other bioinformatics resources to gain insight into biological processes.
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Affiliation(s)
- Gang Li
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
- * E-mail:
| | - Karen E. Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Cecilia N. Arighi
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - Yifan Peng
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
| | - Cathy H. Wu
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, DC, United States of America
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware, United States of America
| | - K. Vijay-Shanker
- Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, United States of America
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35
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Meng J, Zhang D, Pan N, Sun N, Wang Q, Fan J, Zhou P, Zhu W, Jiang L. Identification of miR-194-5p as a potential biomarker for postmenopausal osteoporosis. PeerJ 2015; 3:e971. [PMID: 26038726 PMCID: PMC4451039 DOI: 10.7717/peerj.971] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Accepted: 05/02/2015] [Indexed: 01/13/2023] Open
Abstract
The incidence of osteoporosis is high in postmenopausal women due to altered estrogen levels and continuous calcium loss that occurs with aging. Recent studies have shown that microRNAs (miRNAs) are involved in the development of osteoporosis. These miRNAs may be used as potential biomarkers to identify women at a high risk for developing the disease. In this study, whole blood samples were collected from 48 postmenopausal Chinese women with osteopenia or osteoporosis and pooled into six groups according to individual T-scores. A miRNA microarray analysis was performed on pooled blood samples to identify potential miRNA biomarkers for postmenopausal osteoporosis. Five miRNAs (miR-130b-3p, -151a-3p, -151b, -194-5p, and -590-5p) were identified in the microarray analysis. These dysregulated miRNAs were subjected to a pathway analysis investigating whether they were involved in regulating osteoporosis-related pathways. Among them, only miR-194-5p was enriched in multiple osteoporosis-related pathways. Enhanced miR-194-5p expression in women with osteoporosis was confirmed by quantitative reverse transcription–polymerase chain reaction analysis. For external validation, a significant correlation between the expression of miR-194-5p and T-scores was found in an independent patient collection comprised of 24 postmenopausal women with normal bone mineral density, 30 postmenopausal women with osteopenia, and 32 postmenopausal women with osteoporosis (p < 0.05). Taken together, the present findings suggest that miR-194-5p may be a viable miRNA biomarker for postmenopausal osteoporosis.
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Affiliation(s)
- Jia Meng
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dapeng Zhang
- Department of Orthopedic Surgery, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Nanan Pan
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ning Sun
- Department of Nursing, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qiujun Wang
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Jingxue Fan
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ping Zhou
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wenliang Zhu
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lihong Jiang
- Department of Geriatrics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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36
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Oulas A, Karathanasis N, Louloupi A, Pavlopoulos GA, Poirazi P, Kalantidis K, Iliopoulos I. Prediction of miRNA targets. Methods Mol Biol 2015; 1269:207-29. [PMID: 25577381 DOI: 10.1007/978-1-4939-2291-8_13] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.
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Affiliation(s)
- Anastasis Oulas
- Institute of Marine Biology, Biotechnology and Aquaculture-HCMR, Heraklion, Crete, Greece
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37
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Ellwanger DC, Leonhardt JF, Mewes HW. Large-scale modeling of condition-specific gene regulatory networks by information integration and inference. Nucleic Acids Res 2014; 42:gku916. [PMID: 25294834 PMCID: PMC4245971 DOI: 10.1093/nar/gku916] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE (http://mips.helmholtz-muenchen.de/cogere), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient η2 (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.
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Affiliation(s)
- Daniel Christian Ellwanger
- Chair of Genome-Oriented Bioinformatics, Technische Universität München, Center of Life and Food Sciences Weihenstephan, 85354 Freising, Germany Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Jörn Florian Leonhardt
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Hans-Werner Mewes
- Chair of Genome-Oriented Bioinformatics, Technische Universität München, Center of Life and Food Sciences Weihenstephan, 85354 Freising, Germany Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany
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Bagewadi S, Bobić T, Hofmann-Apitius M, Fluck J, Klinger R. Detecting miRNA Mentions and Relations in Biomedical Literature. F1000Res 2014; 3:205. [PMID: 26535109 PMCID: PMC4602280 DOI: 10.12688/f1000research.4591.3] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/24/2015] [Indexed: 12/16/2022] Open
Abstract
INTRODUCTION MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. MOTIVATION Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. RESULTS The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. AVAILABILITY The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.
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Affiliation(s)
- Shweta Bagewadi
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
- University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Tamara Bobić
- Hasso Plattner Institute Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Potsdam, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
- University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Juliane Fluck
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
| | - Roman Klinger
- Semantic Computing Group, CIT-EC, Bielefeld University, 33615 Bielefeld, Germany
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Bagewadi S, Bobić T, Hofmann-Apitius M, Fluck J, Klinger R. Detecting miRNA Mentions and Relations in Biomedical Literature. F1000Res 2014; 3:205. [PMID: 26535109 DOI: 10.12688/f1000research.4591.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/19/2014] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. MOTIVATION Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity has motivated the need for an improvised framework. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. RESULTS The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. AVAILABILITY The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.
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Affiliation(s)
- Shweta Bagewadi
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany ; University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Tamara Bobić
- Hasso Plattner Institute Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Potsdam, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany ; University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Juliane Fluck
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
| | - Roman Klinger
- Semantic Computing Group, CIT-EC, Bielefeld University, 33615 Bielefeld, Germany
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40
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Bagewadi S, Bobić T, Hofmann-Apitius M, Fluck J, Klinger R. Detecting miRNA Mentions and Relations in Biomedical Literature. F1000Res 2014; 3:205. [PMID: 26535109 DOI: 10.12688/f1000research.4591.2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/15/2014] [Indexed: 12/30/2022] Open
Abstract
Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93. Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.
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Affiliation(s)
- Shweta Bagewadi
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany ; University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Tamara Bobić
- Hasso Plattner Institute Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Potsdam, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany ; University of Bonn, B-IT, Dahlmannstr. 2, 53113 Bonn, Germany
| | - Juliane Fluck
- Fraunhofer SCAI, Bioinformatics, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
| | - Roman Klinger
- Semantic Computing Group, CIT-EC, Bielefeld University, 33615 Bielefeld, Germany
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41
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Small molecules, big effects: the role of microRNAs in regulation of cardiomyocyte death. Cell Death Dis 2014; 5:e1325. [PMID: 25032848 PMCID: PMC4123081 DOI: 10.1038/cddis.2014.287] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Revised: 05/28/2014] [Accepted: 06/03/2014] [Indexed: 01/14/2023]
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNAs involved in posttranscriptional regulation of gene expression, and exerting regulatory roles in plethora of biological processes. In recent years, miRNAs have received increased attention for their crucial role in health and disease, including in cardiovascular disease. This review summarizes the role of miRNAs in regulation of cardiac cell death/cell survival pathways, including apoptosis, autophagy and necrosis. It is envisaged that these miRNAs may explain the mechanisms behind the pathogenesis of many cardiac diseases, and, most importantly, may provide new avenues for therapeutic intervention that will limit cardiomyocyte cell death before it irreversibly affects cardiac function. Through an in-depth literature analysis coupled with integrative bioinformatics (pathway and synergy analysis), we dissect here the landscape of complex relationships between the apoptosis-regulating miRNAs in the context of cardiomyocyte cell death (including regulation of autophagy–apoptosis cross talk), and examine the gaps in our current understanding that will guide future investigations.
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Abstract
MicroRNAs (miRNAs) have attracted ever-increasing interest in recent years. Since experimental approaches for determining miRNAs are nontrivial in their application, computational methods for the prediction of miRNAs have gained popularity. Such methods can be grouped into two broad categories (1) performing ab initio predictions of miRNAs from primary sequence alone and (2) additionally employing phylogenetic conservation. Most methods acknowledge the importance of hairpin or stem-loop structures and employ various methods for the prediction of RNA secondary structure. Machine learning has been employed in both categories with classification being the predominant method. In most cases, positive and negative examples are necessary for performing classification. Since it is currently elusive to experimentally determine all possible miRNAs for an organism, true negative examples are hard to come by, and therefore the accuracy assessment of algorithms is hampered. In this chapter, first RNA secondary structure prediction is introduced since it provides a basis for miRNA prediction. This is followed by an assessment of homology and then ab initio miRNA prediction methods.
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Affiliation(s)
- Jens Allmer
- Molecular Biology and Genetics, Izmir Institute of Technology, Izmir, Turkey
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43
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Cakir MV, Wirth H, Hopp L, Binder H. MicroRNA expression landscapes in stem cells, tissues, and cancer. Methods Mol Biol 2014; 1107:279-302. [PMID: 24272444 DOI: 10.1007/978-1-62703-748-8_17] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.
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Affiliation(s)
- Mehmet Volkan Cakir
- Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany
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44
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MicroRNAs as Haematopoiesis Regulators. Adv Hematol 2013; 2013:695754. [PMID: 24454381 PMCID: PMC3884629 DOI: 10.1155/2013/695754] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 10/20/2013] [Accepted: 10/27/2013] [Indexed: 12/20/2022] Open
Abstract
The production of different types of blood cells including their formation, development, and differentiation is collectively known as haematopoiesis. Blood cells are divided into three lineages erythriod (erythrocytes), lymphoid (B and T cells), and myeloid (granulocytes, megakaryocytes, and macrophages). Haematopoiesis is a complex process regulated by several mechanisms including microRNAs (miRNAs). miRNAs are small RNAs which regulate the expression of a number of genes involved in commitment and differentiation of hematopoietic stem cells. Evidence shows that miRNAs play an important role in haematopoiesis; for example, myeloid and erythroid differentiation is blocked by the overexpression of miR-15a. miR-221, miR-222, and miR-24 inhibit the erythropoiesis, whereas miR-150 plays a role in B and T cell differentiation. miR-146 and miR-10a are downregulated in megakaryopoiesis. Aberrant expression of miRNAs was observed in hematological malignancies including chronic myelogenous leukemia, chronic lymphocytic leukemia, multiple myelomas, and B cell lymphomas. In this review we have focused on discussing the role of miRNA in haematopoiesis.
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45
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Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A, Chou CH, Chu CF, Huang HY, Lin CM, Ho SY, Jian TY, Lin FM, Chang TH, Weng SL, Liao KW, Liao IE, Liu CC, Huang HD. miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 2013; 42:D78-85. [PMID: 24304892 PMCID: PMC3965058 DOI: 10.1093/nar/gkt1266] [Citation(s) in RCA: 539] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules capable of negatively regulating gene expression to control many cellular mechanisms. The miRTarBase database (http://mirtarbase.mbc.nctu.edu.tw/) provides the most current and comprehensive information of experimentally validated miRNA-target interactions. The database was launched in 2010 with data sources for >100 published studies in the identification of miRNA targets, molecular networks of miRNA targets and systems biology, and the current release (2013, version 4) includes significant expansions and enhancements over the initial release (2010, version 1). This article reports the current status of and recent improvements to the database, including (i) a 14-fold increase to miRNA-target interaction entries, (ii) a miRNA-target network, (iii) expression profile of miRNA and its target gene, (iv) miRNA target-associated diseases and (v) additional utilities including an upgrade reminder and an error reporting/user feedback system.
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Affiliation(s)
- Sheng-Da Hsu
- Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu 300, Taiwan, Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan, Department of Computer Science and Engineering, National Chung Hsing University, Taichung 402, Taiwan, Department of Biological Science and Technology, National Chiao Tung University, Hsinchu 300, Taiwan, Molecular Bioinformatics Center, National Chiao Tung University, Hsinchu 300, Taiwan, Graduate Department of Clinical Pharmacy, Taipei Medical University, Taipei 110, Taiwan, Institute of Molecular Medicine and Bioengineering, National Chiao Tung University, Hsinchu 300, Taiwan, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan, Department of Obstetrics and Gynecology, Hsinchu Mackay Memorial Hospital, Hsinchu 300, Taiwan, Mackay Medicine, Nursing and Management College, Taipei 112, Taiwan, Department of Medicine, Mackay Medical College, New Taipei City 252, Taiwan, and Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 807, Taiwan
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46
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Zhu W, Zhao Y, Xu Y, Sun Y, Wang Z, Yuan W, Du Z. Dissection of protein interactomics highlights microRNA synergy. PLoS One 2013; 8:e63342. [PMID: 23691029 PMCID: PMC3653946 DOI: 10.1371/journal.pone.0063342] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Accepted: 04/01/2013] [Indexed: 11/18/2022] Open
Abstract
Despite a large amount of microRNAs (miRNAs) have been validated to play crucial roles in human biology and disease, there is little systematic insight into the nature and scale of the potential synergistic interactions executed by miRNAs themselves. Here we established an integrated parameter synergy score to determine miRNA synergy, by combining the two mechanisms for miRNA-miRNA interactions, miRNA-mediated gene co-regulation and functional association between target gene products, into one single parameter. Receiver operating characteristic (ROC) analysis indicated that synergy score accurately identified the gene ontology-defined miRNA synergy (AUC = 0.9415, p<0.001). Only a very small portion of the random miRNA-miRNA combinations generated potent synergy, implying poor expectancy of widespread synergy. However, targeting more key genes made two miRNAs more likely to act synergistically. Compared to other miRNAs, miR-21 was a highly exceptional case due to frequent appearance in the top synergistic miRNA pairs. This result highlighted its essential role in coordinating or strengthening physiological and pathological functions of other miRNAs. The synergistic effect of miR-21 and miR-1 were functionally validated for their significant influences on myocardial apoptosis, cardiac hypertrophy and fibrosis. The novel approach established in this study enables easy and effective identification of condition-restricted potent miRNA synergy simply by concentrating the available protein interactomics and miRNA-target interaction data into a single parameter synergy score. Our results may be important for understanding synergistic gene regulation by miRNAs and may have significant implications for miRNA combination therapy of cardiovascular disease.
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Affiliation(s)
- Wenliang Zhu
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yilei Zhao
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yingqi Xu
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yong Sun
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhe Wang
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wei Yuan
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Zhimin Du
- Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
- * E-mail:
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47
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Huang X, Gong R, Li X, Virtue A, Yang F, Yang IH, Tran AH, Yang XF, Wang H. Identification of novel pretranslational regulatory mechanisms for NF-κB activation. J Biol Chem 2013; 288:15628-40. [PMID: 23515310 DOI: 10.1074/jbc.m113.460626] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
NF-κB-controlled transcriptional regulation plays a central role in inflammatory and immune responses. Currently, understanding about NF-κB activation mechanism emphasizes IκB-tethered complex inactivation in the cytoplasm. In the case of NF-κB activation, IκB phosphorylation leads to its degradation, followed by NF-κB relocation to the nucleus and trans-activation of NF-κB-targeted genes. Pretranslational mechanism mediated NF-κB activation remains poorly understood. In this study, we investigated NF-κB pretranslational regulation by performing a series of database mining analyses and using six large national experimental databases (National Center of Biotechnology Information UniGene expressed sequence tag profile database, Gene Expression Omnibus database, Transcription Element Search System database, AceView database, and Epigenomics database) and TargetScan software. We reported the following findings: 1) NF-κB-signaling genes are differentially expressed in human and mouse tissues; 2) heart and vessels are the inflammation-privileged tissues and less easy to be inflamed because lacking in key NF-κB-signaling molecular expression; 3) NF-κB-signaling genes are induced by cardiovascular disease risk factors oxidized phospholipids and proinflammatory cytokines in endothelial cells; 4) transcription factors CCAAT/enhancer-binding proteins and NF-κB have higher binding site frequencies in the promoters of proinflammatory cytokine-induced NF-κB genes; 5) most NF-κB-signaling genes have multiple alternative promoters and alternatively spliced isoforms; 6) NF-κB family genes can be regulated by DNA methylation; and 7) 27 of 38 NF-κB-signaling genes can be regulated by microRNAs. Our findings provide important insight into the mechanism of NF-κB activation, which may contribute to cardiovascular disease, inflammatory diseases, and immunological disorders.
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Affiliation(s)
- Xiao Huang
- Cardiovascular Research Center, Department of Pharmacology, Temple University School of Medicine, Philadelphia, Pennsylvania 19140, USA
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48
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Xie B, Ding Q, Han H, Wu D. miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics 2013; 29:638-44. [DOI: 10.1093/bioinformatics/btt014] [Citation(s) in RCA: 399] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Bossel Ben-Moshe N, Avraham R, Kedmi M, Zeisel A, Yitzhaky A, Yarden Y, Domany E. Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets. Nucleic Acids Res 2012; 40:10614-27. [PMID: 22977182 PMCID: PMC3505984 DOI: 10.1093/nar/gks841] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MicroRNAs (miRs) function primarily as post-transcriptional negative regulators of gene expression through binding to their mRNA targets. Reliable prediction of a miR's targets is a considerable bioinformatic challenge of great importance for inferring the miR's function. Sequence-based prediction algorithms have high false-positive rates, are not in agreement, and are not biological context specific. Here we introduce CoSMic (Context-Specific MicroRNA analysis), an algorithm that combines sequence-based prediction with miR and mRNA expression data. CoSMic differs from existing methods--it identifies miRs that play active roles in the specific biological system of interest and predicts with less false positives their functional targets. We applied CoSMic to search for miRs that regulate the migratory response of human mammary cells to epidermal growth factor (EGF) stimulation. Several such miRs, whose putative targets were significantly enriched by migration processes were identified. We tested three of these miRs experimentally, and showed that they indeed affected the migratory phenotype; we also tested three negative controls. In comparison to other algorithms CoSMic indeed filters out false positives and allows improved identification of context-specific targets. CoSMic can greatly facilitate miR research in general and, in particular, advance our understanding of individual miRs' function in a specific context.
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Affiliation(s)
- Noa Bossel Ben-Moshe
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, 76100, Israel
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Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs. BMC SYSTEMS BIOLOGY 2012; 6:90. [PMID: 22824421 PMCID: PMC3430561 DOI: 10.1186/1752-0509-6-90] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2011] [Accepted: 07/09/2012] [Indexed: 12/31/2022]
Abstract
Background Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods. Results In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3’UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes. Conclusions We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.
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