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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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Omer A. MicroRNAs as powerful tool against COVID-19: Computational perspective. WIREs Mech Dis 2023; 15:e1621. [PMID: 37345625 DOI: 10.1002/wsbm.1621] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 05/23/2023] [Indexed: 06/23/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 is the virus that is responsible for the current pandemic, COVID-19 (SARS-CoV-2). MiRNAs, a component of RNAi technology, belong to the family of short, noncoding ssRNAs, and may be crucial in the battle against this global threat since they are involved in regulating complex biochemical pathways and may prevent viral proliferation, translation, and host expression. The complicated metabolic pathways are modulated by the activity of many proteins, mRNAs, and miRNAs working together in miRNA-mediated genetic control. The amount of omics data has increased dramatically in recent years. This massive, linked, yet complex metabolic regulatory network data offers a wealth of opportunity for iterative analysis; hence, extensive, in-depth, but time-efficient screening is necessary to acquire fresh discoveries; this is readily performed with the use of bioinformatics. We have reviewed the literature on microRNAs, bioinformatics, and COVID-19 infection to summarize (1) the function of miRNAs in combating COVID-19, and (2) the use of computational methods in combating COVID-19 in certain noteworthy studies, and (3) computational tools used by these studies against COVID-19 in several purposes. This article is categorized under: Infectious Diseases > Computational Models.
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Affiliation(s)
- Ankur Omer
- Government College Silodi, MPHED, Katni, Madhya Pradesh, India
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3
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Shahrear S, Zinnia MA, Ahmed T, Islam ABMMK. Deciphering the role of predicted miRNAs of polyomaviruses in carcinogenesis. Biochim Biophys Acta Mol Basis Dis 2022; 1868:166537. [PMID: 36089125 DOI: 10.1016/j.bbadis.2022.166537] [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: 01/31/2022] [Revised: 08/13/2022] [Accepted: 09/01/2022] [Indexed: 11/20/2022]
Abstract
Human polyomaviruses are relatively common in the general population. Polyomaviruses maintain a persistent infection after initial infection in childhood, acting as an opportunistic pathogen in immunocompromised populations and their association has been linked to carcinogenesis. A comprehensive understanding of the underlying molecular mechanisms of carcinogenesis in consequence of polyomavirus infection remains elusive. However, the critical role of viral miRNAs and their potential targets in modifying the transcriptome profile of the host remains largely unknown. Polyomavirus-derived miRNAs have the potential to play a substantial role in carcinogenesis. Employing computational approaches, putative viral miRNAs along with their target genes have been predicted and possible roles of the targeted genes in many significant biological processes have been obtained. Polyomaviruses have been observed to target intracellular signal transduction pathways through miRNA-mediated epigenetic regulation, which may contribute to cancer development. In addition, BKPyV-infected human renal cell microarray data was coupled with predicted target genes and analysis of the downregulated genes indicated that viruses target multiple signaling pathways (e.g. MAPK signaling pathway, PI3K-Akt signaling pathway, PPAR signaling pathway) in the host as well as turning off several tumor suppression genes (e.g. FGGY, EPHX2, CACNA2D3, CDH16) through miRNA-induced mechanisms, assuring cell transformation. This study provides a conceptual framework for the underlying molecular mechanisms involved in the course of carcinogenesis upon polyomavirus infection.
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Affiliation(s)
- Sazzad Shahrear
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | | | - Tasnim Ahmed
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
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4
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Samami E, Pourali G, Arabpour M, Fanipakdel A, Shahidsales S, Javadinia SA, Hassanian SM, Mohammadparast S, Avan A. The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress. Front Oncol 2022; 11:716831. [PMID: 35186706 PMCID: PMC8855122 DOI: 10.3389/fonc.2021.716831] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 12/31/2021] [Indexed: 12/20/2022] Open
Abstract
Prostate cancer (P.C.) is one of the most frequent diagnosed cancers among men and the first leading cause of death with an annual incidence of 1.4 million worldwide. Prostate-specific antigen is being used for screening/diagnosis of prostate disease, although it is associated with several limitations. Thus, identification of novel biomarkers is warranted for diagnosis of patients at earlier stages. MicroRNAs (miRNAs) are recently being emerged as potential biomarkers. It has been shown that these small molecules can be circulated in body fluids and prognosticate the risk of developing P.C. Several miRNAs, including MiR-20a, MiR-21, miR-375, miR-378, and miR-141, have been proposed to be expressed in prostate cancer. This review summarizes the current knowledge about possible molecular mechanisms and potential application of tissue specific and circulating microRNAs as diagnosis, prognosis, and therapeutic targets in prostate cancer.
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Affiliation(s)
- Elham Samami
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Pourali
- Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Arabpour
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Azar Fanipakdel
- Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Seyed Alireza Javadinia
- Vasei Clinical Research Development Unit, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Mohammadparast
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Medical Sciences Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- *Correspondence: Amir Avan,
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R E, Jain DK, Kotecha K, Pandya S, Reddy SS, E R, Varadarajan V, Mahanti A, V S. Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA. Front Public Health 2022; 9:821410. [PMID: 35004605 PMCID: PMC8733243 DOI: 10.3389/fpubh.2021.821410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.
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Affiliation(s)
- Elakkiya R
- School of Computing, SASTRA Deemed University, Thanjavur, India
| | - Deepak Kumar Jain
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | | | - Rajalakshmi E
- School of Computing, SASTRA Deemed University, Thanjavur, India
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
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Gharbi S, Mohammadi Z, Dezaki MS, Dokanehiifard S, Dabiri S, Korsching E. Characterization of the first microRNA in human CDH1 that affects cell cycle and apoptosis and indicates breast cancers progression. J Cell Biochem 2022; 123:657-672. [PMID: 34997630 DOI: 10.1002/jcb.30211] [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: 09/27/2021] [Revised: 11/26/2021] [Accepted: 12/21/2021] [Indexed: 11/12/2022]
Abstract
The E-cadherin protein (Cadherin 1, gene: CDH1), a master regulator of the human epithelial homeostasis, contributes to the epithelial-mesenchymal transition (EMT) which confers cell migratory features to the cells. The EMT is central to many pathophysiological changes in cancer. Therefore, a better understanding of this regulatory scenario is beneficial for therapeutic regiments. The CDH1 gene is approximately 100 kbp long and consists of 16 exons with a relatively large second intron. Since none microRNA (miRNA) has been identified in CDH1 up to now we screened the CDH1 gene for promising miRNA hairpin structures in silico. Out of the 27 hairpin structures we identified, one stable RNA fold with a promising sequence motive was selected for experimental verification. The exogenous validation of the hairpin sequence was performed by transfection of HEK293T cells and the mature miRNA sequences could be verified by quantitative polymerase chain reaction. The endogenous expression of the mature miRNA provisionally named CDH1-i2-miR-1 could be confirmed in two normal (HEK293T, HUVEK) and five cancer cell lines (MCF7, MDA-MB-231, SW480, HT-29, A549). The functional characterization by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay showed a suppression of HEK293T cell proliferation. A flow cytometry-based approach showed the ability of CDH1-i2-miR-1 to arrest transfected cells on a G2/M state while annexin staining exemplified an apoptotic effect. BAX and PTEN expression levels were affected following the overexpression with the new miRNA. The in vivo expression level was assessed in 35 breast tumor tissues and their paired nonmalignant marginal part. A fourfold downregulation in the tumor specimens compared to their marginal controls could be observed. It can be concluded that the sequence of the hub gene CDH1 harbors at least one miRNA but eventually even more relevant for the pathophysiology of breast cancer.
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Affiliation(s)
- Sedigheh Gharbi
- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Zahra Mohammadi
- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Maryam Saedi Dezaki
- Department of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Sadat Dokanehiifard
- Department of Human Genetics, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - Shahriar Dabiri
- Department of Pathology, Pathology and Stem Cell Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Eberhard Korsching
- Institute of Bioinformatics, Faculty of Medicine, University of Münster, Münster, Germany
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Satyam R, Bhardwaj T, Goel S, Jha NK, Jha SK, Nand P, Ruokolainen J, Kamal MA, Kesari KK. miRNAs in SARS-CoV 2: A Spoke in the Wheel of Pathogenesis. Curr Pharm Des 2021; 27:1628-1641. [PMID: 33023438 DOI: 10.2174/1381612826999201001200529] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/28/2020] [Accepted: 08/04/2020] [Indexed: 02/08/2023]
Abstract
INTRODUCTION The rapid emergence of Severe Acute Respiratory Syndrome coronavirus 2 (SARS-- CoV-2) has resulted in an increased mortality rate across the globe. However, the underlying mechanism of SARS-CoV-2 altering human immune response is still elusive. The existing literature on miRNA mediated pathogenesis of RNA virus viz. Dengue virus, West Nile virus, etc. raises a suspicion that miRNA encoded by SARS-CoV-2 might facilitate virus replication and regulate the host's gene expression at the post-transcriptional level. METHODS We investigated this possibility via computational prediction of putative miRNAs encoded by the SARS-CoV-2 genome using a novel systematic pipeline that predicts putative mature-miRNA and their targeted genes transcripts. To trace down if viral-miRNAs targeted the genes critical to the immune pathway, we assessed whether mature miRNA transcripts exhibit effective hybridization with the 3'UTR region of human gene transcripts. Conversely, we also tried to study human miRNA-mediated viral gene regulation to get insight into the miRNA mediated offense and defense mechanism of virus and its host organisms in toto. RESULTS Our analysis led us to shortlist six putative miRNAs that target, majorly, genes related to cell proliferation/ differentiation/signaling, and senescence. Nonetheless, they also target immune-related genes that directly/ indirectly orchestrate immune pathways like TNF (Tumor Necrosis Factor) signaling and Chemokine signaling pathways putatively serving as the nucleus to cytokine storms. CONCLUSION Besides, these six miRNAs were found to be conserved so far across 80 complete genomes of SARS-CoV-2 (NCBI Virus, last assessed 12 April 2020) including Indian strains that are also targeted by 7 human miRNAs and can, therefore, be exploited to develop MicroRNA-Attenuated Vaccines.
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Affiliation(s)
- Rohit Satyam
- Department of Biotechnology, Noida Institute of Engineering and Technology (NIET), Greater Noida, India
| | - Tulika Bhardwaj
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Sachin Goel
- Department of Biotechnology, Noida Institute of Engineering and Technology (NIET), Greater Noida, India
| | - Niraj Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET), Sharda University, Greater Noida, India
| | - Saurabh Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET), Sharda University, Greater Noida, India
| | - Parma Nand
- Department of Biotechnology, School of Engineering & Technology (SET), Sharda University, Greater Noida, India
| | | | - Mohammad Amjad Kamal
- West China School of Nursing / Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
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8
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Zhao Y, Kuang Z, Wang Y, Li L, Yang X. MicroRNA annotation in plants: current status and challenges. Brief Bioinform 2021; 22:6180404. [PMID: 33754625 DOI: 10.1093/bib/bbab075] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/01/2021] [Accepted: 02/15/2021] [Indexed: 11/14/2022] Open
Abstract
Last two decades, the studies on microRNAs (miRNAs) and the numbers of annotated miRNAs in plants and animals have surged. Herein, we reviewed the current progress and challenges of miRNA annotation in plants. Via the comparison of plant and animal miRNAs, we pinpointed out the difficulties on plant miRNA annotation and proposed potential solutions. In terms of recalling the history of methods and criteria in plant miRNA annotation, we detailed how the major progresses made and evolved. By collecting and categorizing bioinformatics tools for plant miRNA annotation, we surveyed their advantages and disadvantages, especially for ones with the principle of mimicking the miRNA biogenesis pathway by parsing deeply sequenced small RNA (sRNA) libraries. In addition, we summarized all available databases hosting plant miRNAs, and posted the potential optimization solutions such as how to increase the signal-to-noise ratio (SNR) in these databases. Finally, we discussed the challenges and perspectives of plant miRNA annotations, and indicated the possibilities offered by an all-in-one tool and platform according to the integration of artificial intelligence.
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Affiliation(s)
- Yongxin Zhao
- Beijing Academy of Agriculture and Forestry Sciences, China
| | - Zheng Kuang
- Peking University and Beijing Academy of Agriculture and Forestry Sciences, China
| | | | - Lei Li
- School of Advanced Agricultural Sciences and School of Life Sciences at the Peking University, China
| | - Xiaozeng Yang
- Beijing Academy of Agriculture and Forestry Sciences, China
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Yang XF, Zhou YK, Zhang L, Gao Y, Du PF. Predicting LncRNA Subcellular Localization Using Unbalanced Pseudo-k Nucleotide Compositions. Curr Bioinform 2020. [DOI: 10.2174/1574893614666190902151038] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background:
Long non-coding RNAs (lncRNAs) are transcripts with a length more
than 200 nucleotides, functioning in the regulation of gene expression. More evidence has shown
that the biological functions of lncRNAs are intimately related to their subcellular localizations.
Therefore, it is very important to confirm the lncRNA subcellular localization.
Methods:
In this paper, we proposed a novel method to predict the subcellular localization of
lncRNAs. To more comprehensively utilize lncRNA sequence information, we exploited both kmer
nucleotide composition and sequence order correlated factors of lncRNA to formulate
lncRNA sequences. Meanwhile, a feature selection technique which was based on the Analysis Of
Variance (ANOVA) was applied to obtain the optimal feature subset. Finally, we used the support
vector machine (SVM) to perform the prediction.
Results:
The AUC value of the proposed method can reach 0.9695, which indicated the proposed
predictor is an efficient and reliable tool for determining lncRNA subcellular localization. Furthermore,
the predictor can reach the maximum overall accuracy of 90.37% in leave-one-out cross
validation, which clearly outperforms the existing state-of- the-art method.
Conclusion:
It is demonstrated that the proposed predictor is feasible and powerful for the prediction
of lncRNA subcellular. To facilitate subsequent genetic sequence research, we shared the
source code at https://github.com/NicoleYXF/lncRNA.
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Affiliation(s)
- Xiao-Fei Yang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yuan-Ke Zhou
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Lin Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Yang Gao
- School of Medicine, Nankai University, Tianjin 300071, China
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
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Bugnon LA, Yones C, Milone DH, Stegmayer G. Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:2857-2867. [PMID: 31170082 DOI: 10.1109/tnnls.2019.2914471] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. For this task, a classifier is trained to identify RNA sequences having the highest chance of being miRNA precursors. The big issue is that well-known pre-miRNAs are usually just a few in comparison to the hundreds of thousands of candidate sequences in a genome, which results in highly imbalanced data. This imbalance has a strong influence on most standard classifiers and, if not properly addressed, the classifier is not able to work properly in a real-life scenario. This work provides a comparative assessment of recent deep neural architectures for dealing with the large imbalanced data issue in the classification of pre-miRNAs. We present and analyze recent architectures in a benchmark framework with genomes of animals and plants, with increasing imbalance ratios up to 1:2000. We also propose a new graphical way for comparing classifiers performance in the context of high-class imbalance. The comparative results obtained show that, at a very high imbalance, deep belief neural networks can provide the best performance.
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Stegmayer G, Di Persia LE, Rubiolo M, Gerard M, Pividori M, Yones C, Bugnon LA, Rodriguez T, Raad J, Milone DH. Predicting novel microRNA: a comprehensive comparison of machine learning approaches. Brief Bioinform 2020; 20:1607-1620. [PMID: 29800232 DOI: 10.1093/bib/bby037] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 03/26/2018] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION The importance of microRNAs (miRNAs) is widely recognized in the community nowadays because these short segments of RNA can play several roles in almost all biological processes. The computational prediction of novel miRNAs involves training a classifier for identifying sequences having the highest chance of being precursors of miRNAs (pre-miRNAs). The big issue with this task is that well-known pre-miRNAs are usually few in comparison with the hundreds of thousands of candidate sequences in a genome, which results in high class imbalance. This imbalance has a strong influence on most standard classifiers, and if not properly addressed in the model and the experiments, not only performance reported can be completely unrealistic but also the classifier will not be able to work properly for pre-miRNA prediction. Besides, another important issue is that for most of the machine learning (ML) approaches already used (supervised methods), it is necessary to have both positive and negative examples. The selection of positive examples is straightforward (well-known pre-miRNAs). However, it is difficult to build a representative set of negative examples because they should be sequences with hairpin structure that do not contain a pre-miRNA. RESULTS This review provides a comprehensive study and comparative assessment of methods from these two ML approaches for dealing with the prediction of novel pre-miRNAs: supervised and unsupervised training. We present and analyze the ML proposals that have appeared during the past 10 years in literature. They have been compared in several prediction tasks involving two model genomes and increasing imbalance levels. This work provides a review of existing ML approaches for pre-miRNA prediction and fair comparisons of the classifiers with same features and data sets, instead of just a revision of published software tools. The results and the discussion can help the community to select the most adequate bioinformatics approach according to the prediction task at hand. The comparative results obtained suggest that from low to mid-imbalance levels between classes, supervised methods can be the best. However, at very high imbalance levels, closer to real case scenarios, models including unsupervised and deep learning can provide better performance.
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Affiliation(s)
- Georgina Stegmayer
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Leandro E Di Persia
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Mariano Rubiolo
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Matias Gerard
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Milton Pividori
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Cristian Yones
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Leandro A Bugnon
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Tadeo Rodriguez
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Jonathan Raad
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- sinc(i), Research Institute for Signals, Systems and Computational Intelligence (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
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Mishra A, Gupta S, Gupta P, Dhawan SS, Lal RK. In Silico Identification of miRNA and Targets from Chrysopogon zizanioides (L.) Roberty with Functional Validation from Leaf and Root Tissues. Appl Biochem Biotechnol 2020; 192:1076-1092. [PMID: 32656724 DOI: 10.1007/s12010-020-03381-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/23/2020] [Indexed: 11/24/2022]
Abstract
microRNAs are small non-coding RNA molecule that plays an important role in metabolism. Chrysopogon zizanioides (L.) Roberty is an important aromatic plant used in perfumery industries, soil, water conservation, and agricultural practices. In this study, the transcriptomic sequence of vetiver leaf and root was subjected to miRNA identification by the computational methods. miRNA identification was carried out using a homology-based method by C-mii software with several other online tools. A total of 80 miRNA were identified from both leaf and root sequences. Target identification was done by identified miRNA sets. A total of 25 and 31 miRNA families were identified in both leaf and root, respectively, with ten common families involve in different ontological function. miR169 and miR5021 regulate most of the target in leaf and root. In vetiver, many primary and secondary metabolism elements are regulated by miRNA as photo-system, transcription factor, terpenoid metabolism, etc. Here is the first in silico study revealing the specific miRNAs and their target genes for corresponding root and leaf tissues respectively in C. zizanioides.
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Affiliation(s)
- Anand Mishra
- Genetics and Plant Breeding Division, CSIR-Central Institute for Medicinal and Aromatic Plants, Lucknow, UP, 226015, India.
| | - Sanchita Gupta
- Bioinformatics Department, CSIR-National Botanical Research Institute, Lucknow, UP, 226001, India
| | - Pankhuri Gupta
- Biotechnology Division, CSIR-Central Institute for Medicinal and Aromatic Plants, U P, Lucknow, 226015, India.,CSIR-Human Resource Development Centre Campus, Academy of Scientific and Innovative Research (AcSIR), Sector 19, Kamla Nehru Nagar, Ghaziabad, Uttar Pradesh, 201002, India
| | - Sunita Singh Dhawan
- Biotechnology Division, CSIR-Central Institute for Medicinal and Aromatic Plants, U P, Lucknow, 226015, India
| | - Raj Kishori Lal
- Genetics and Plant Breeding Division, CSIR-Central Institute for Medicinal and Aromatic Plants, Lucknow, UP, 226015, India
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Ayachit G, Pandya H, Das J. miRDetect: A combinatorial approach for automated detection of novel miRNA precursors from plant EST data using homology and Random Forest classification. Genomics 2020; 112:3201-3206. [PMID: 32380232 DOI: 10.1016/j.ygeno.2020.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 04/25/2020] [Accepted: 05/02/2020] [Indexed: 01/23/2023]
Abstract
Identification of microRNAs from plants is a crucial step for understanding the mechanisms of pathways and regulation of genes. A number of tools have been developed for the detection of microRNAs from small RNA-seq data. However, there is a lack of pipeline for detection of miRNA from EST dataset even when a huge resource is publicly available and the method is known. Here we present miRDetect, a python implementation to detect novel miRNA precursors from plant EST data using homology and machine learning approach. 10-fold cross validation was applied to choose best classifier based on ROC, accuracy, MCC and F1-scores using 112 features. miRDetect achieved a classification accuracy of 93.35% on a Random Forest classifier and outperformed other precursor detection tools in terms of performance. The miRDetect pipeline aids in identifying novel plant precursors using a mixed approach and will be helpful to researchers with less informatics background.
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Affiliation(s)
- Garima Ayachit
- Department of Botany, Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India
| | - Himanshu Pandya
- Department of Botany, Bioinformatics and Climate Change, Gujarat University, Ahmedabad, India.
| | - Jayashankar Das
- Centre for Genomics & Biomedical Informatics, IMS and SUM Hospital, Siksha "O" Anusandhan University (Deemed to be), K8, Kalinga Nagar, Bhubaneswar, Odisha 751003, India.
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Tang X, Sun Y. Fast and accurate microRNA search using CNN. BMC Bioinformatics 2019; 20:646. [PMID: 31881831 PMCID: PMC6933638 DOI: 10.1186/s12859-019-3279-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 11/18/2019] [Indexed: 12/15/2022] Open
Abstract
Background There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families. Results We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster. Conclusion Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.
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Affiliation(s)
- Xubo Tang
- Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Yanni Sun
- Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
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15
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Abstract
Background:DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.Objective:Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.Method:The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.Results:Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.Conclusion:Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.
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Affiliation(s)
- Kaiyang Qu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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Zhang J, Liu B. A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods. Curr Bioinform 2019. [DOI: 10.2174/1574893614666181212102749] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.
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Affiliation(s)
- Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
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Fu X, Zhu W, Cai L, Liao B, Peng L, Chen Y, Yang J. Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures. Front Genet 2019; 10:119. [PMID: 30858864 PMCID: PMC6397858 DOI: 10.3389/fgene.2019.00119] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 02/04/2019] [Indexed: 11/30/2022] Open
Abstract
Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.
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Affiliation(s)
- Xiangzheng Fu
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Lijun Cai
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Bo Liao
- College of Information Science and Engineering, Hunan University, Changsha, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Yifan Chen
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Jialiang Yang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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18
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Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features. Sci Rep 2019; 9:1521. [PMID: 30728425 PMCID: PMC6365589 DOI: 10.1038/s41598-018-38048-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 12/18/2018] [Indexed: 02/07/2023] Open
Abstract
The significant role of microRNAs (miRNAs) in various biological processes and diseases has been widely studied and reported in recent years. Several computational methods associated with mature miRNA identification suffer various limitations involving canonical biological features extraction, class imbalance, and classifier performance. The proposed classifier, miRFinder, is an accurate alternative for the identification of mature miRNAs. The structured-sequence features were proposed to precisely extract miRNA biological features, and three algorithms were selected to obtain the canonical features based on the classifier performance. Moreover, the center of mass near distance training based on K-means was provided to improve the class imbalance problem. In particular, the AdaBoost-SVM algorithm was used to construct the classifier. The classifier training process focuses on incorrectly classified samples, and the integrated results use the common decision strategies of the weak classifier with different weights. In addition, the all mature miRNA sites were predicted by different classifiers based on the features of different sites. Compared with other methods, the performance of the classifiers has a high degree of efficacy for the identification of mature miRNAs. MiRFinder is freely available at https://github.com/wangying0128/miRFinder .
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19
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Ma Y, Yu Z, Han G, Li J, Anh V. Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs. BMC Bioinformatics 2018; 19:521. [PMID: 30598066 PMCID: PMC6311913 DOI: 10.1186/s12859-018-2518-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs identification. The source codes of our method can be downloaded from https://github.com/myl446/MicroRNA-NHPred .
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Affiliation(s)
- Yuanlin Ma
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105 China
| | - Zuguo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105 China
- School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane, Q4001 Australia
| | - Guosheng Han
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105 China
| | - Jinyan Li
- Advanced Analytics Institute, Faculty of Engineering & IT, University of Technology Sydney, P.O Box 123, Broadway, NSW 2007 Australia
| | - Vo Anh
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Hunan, 411105 China
- School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Q4001 Australia
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20
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Kumar R, Kumari B, Kumar M. Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information. Mitochondrion 2018; 42:11-22. [DOI: 10.1016/j.mito.2017.10.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 07/21/2017] [Accepted: 10/06/2017] [Indexed: 12/22/2022]
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21
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Fu X, Liao B, Zhu W, Cai L. New 3D graphical representation for RNA structure analysis and its application in the pre-miRNA identification of plants. RSC Adv 2018; 8:30833-30841. [PMID: 35548744 PMCID: PMC9085476 DOI: 10.1039/c8ra04138e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/24/2018] [Indexed: 11/26/2022] Open
Abstract
MicroRNAs (miRNAs) are a family of short non-coding RNAs that play significant roles as post-transcriptional regulators. Consequently, various methods have been proposed to identify precursor miRNAs (pre-miRNAs), among which the comparative studies of miRNA structures are the most important. To measure and classify the structural similarity of miRNAs, we propose a new three-dimensional (3D) graphical representation of the secondary structure of miRNAs, in which an miRNA secondary structure is initially transformed into a characteristic sequence based on physicochemical properties and frequency of base. A numerical characterization of the 3D graph is used to represent the miRNA secondary structure. We then utilize a novel Euclidean distance method based on this expression to compute the distance of different miRNA sequences for the sequence similarity analysis. Finally, we use this sequence similarity analysis method to identify plant pre-miRNAs among three commonly used datasets. Results show that the method is reasonable and effective.
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Affiliation(s)
- Xiangzheng Fu
- College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
| | - Bo Liao
- College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
| | - Wen Zhu
- College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
| | - Lijun Cai
- College of Information Science and Engineering, Hunan University Changsha Hunan 410082 China
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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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Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis. Oncotarget 2018; 7:85613-85623. [PMID: 27784000 PMCID: PMC5356763 DOI: 10.18632/oncotarget.12828] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Accepted: 10/14/2016] [Indexed: 01/09/2023] Open
Abstract
MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers' generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages. However, the results can be notably different. In this study, we compared miRNAs obtained from 6 common significance tests/R packages (t-test, Limma, DESeq, edgeR, LRT and MARS) with the miRNAs archived in two databases; HMDD 2.0 database, which collects experimentally validated differentially expressed miRNAs, and Infer microRNA-disease association database, which contains the potential disease-associated miRNAs by network forecasting. Finally, we sought the MARS method in DEGseq package more effectively searched out differentially expressed miRNAs than other common methods.
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24
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Chen X, Yan CC, Zhang X, You ZH. Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2017; 18:558-576. [PMID: 27345524 PMCID: PMC5862301 DOI: 10.1093/bib/bbw060] [Citation(s) in RCA: 295] [Impact Index Per Article: 42.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2016] [Indexed: 02/07/2023] Open
Abstract
LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.
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Affiliation(s)
- Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
- Corresponding authors. Xing Chen, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China. E-mail: ; Zhu-Hong You, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. E-mail:
| | | | - Xu Zhang
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China
- Corresponding authors. Xing Chen, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China. E-mail: ; Zhu-Hong You, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. E-mail:
| | - Zhu-Hong You
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
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Srivastava S, Sharma A. Study of microRNA mediated gene regulation in Striga hermonthica through in-silico approach. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.aggene.2017.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Singh N, Sharma A. Turmeric (Curcuma longa): miRNAs and their regulating targets are involved in development and secondary metabolite pathways. C R Biol 2017; 340:481-491. [PMID: 29126713 DOI: 10.1016/j.crvi.2017.09.009] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 08/20/2017] [Accepted: 09/30/2017] [Indexed: 01/22/2023]
Abstract
Turmeric has been used as a therapeutic herb over centuries in traditional medicinal systems due to the presence of several secondary metabolite compounds. microRNAs are known to regulate gene expression at the post-transcriptional level by transcriptional cleavage or translation repression. miRNAs have been demonstrated to play an active role in secondary metabolism regulation. The present work was focused on the identification of the miRNAs involved in the regulation of secondary metabolite and development process of turmeric. Eighteen miRNA families were identified for turmeric. Sixteen miRNA families were observed to regulate 238 target transcripts. LncRNAs targets of the putative miRNA candidates were also predicted. Our results indicated their role in binding, reproduction, stress, and other developmental processes. Gene annotation and pathway analysis illustrated the biological function of the targets regulated by the putative miRNAs. The miRNA-mediated gene regulatory network also revealed co-regulated targets that were regulated by two or more miRNA families. miR156 and miR5015 were observed to be involved in rhizome development. miR5021 showed regulation for terpenoid backbone biosynthesis and isoquinoline alkaloid biosynthesis pathways. The flavonoid biosynthesis pathway was observed to be regulated by miR2919. The analysis revealed the probable involvement of three miRNAs (miR1168.2, miR156b and miR1858) in curcumin biosynthesis. Other miRNAs were found to be involved in the growth and developmental process of turmeric. Phylogenetic analysis of selective miRNAs was also performed.
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Affiliation(s)
- Noopur Singh
- Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, P.O. CIMAP, 226015 Lucknow, UP, India.
| | - Ashok Sharma
- Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, P.O. CIMAP, 226015 Lucknow, UP, India.
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Tahir M, Hayat M, Kabir M. Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:69-75. [PMID: 28688491 DOI: 10.1016/j.cmpb.2017.05.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 05/05/2017] [Accepted: 05/19/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhancers. Due to high availability of huge amount of DNA sequences, it is needed to develop fast, reliable and robust intelligent computational method, which not only identify enhancers but also determines their strength. Considerable progress has been achieved in this regard; however, timely and precisely identification of enhancers is still a challenging task. METHODS Two-level intelligent computational model for identification of enhancers and their subgroups is proposed. Two different feature extraction techniques including di-nucleotide composition and tri-nucleotide composition were adopted for extraction of numerical descriptors. Four classification methods including probabilistic neural network, support vector machine, k-nearest neighbor and random forest were utilized for classification. RESULTS The proposed method yielded 77.25% of accuracy for dataset S1 contains enhancers and non-enhancers, whereas 64.70% of accuracy for dataset S2 comprises of strong enhancer and weak enhancer sequences using jackknife cross-validation test. CONCLUSION The predictive results validated that the proposed method is better than that of existing approaches so far reported in the literature. It is thus highly observed that the developed method will be useful and expedient for basic research and academia.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, KP Pakistan.
| | - Muhammad Kabir
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
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DNA Oncogenic Virus-Induced Oxidative Stress, Genomic Damage, and Aberrant Epigenetic Alterations. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2017; 2017:3179421. [PMID: 28740569 PMCID: PMC5504953 DOI: 10.1155/2017/3179421] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 05/01/2017] [Accepted: 05/23/2017] [Indexed: 12/18/2022]
Abstract
Approximately 20% of human cancers is attributable to DNA oncogenic viruses such as human papillomavirus (HPV), hepatitis B virus (HBV), and Epstein-Barr virus (EBV). Unrepaired DNA damage is the most common and overlapping feature of these DNA oncogenic viruses and a source of genomic instability and tumour development. Sustained DNA damage results from unceasing production of reactive oxygen species and activation of inflammasome cascades that trigger genomic changes and increased propensity of epigenetic alterations. Accumulation of epigenetic alterations may interfere with genome-wide cellular signalling machineries and promote malignant transformation leading to cancer development. Untangling and understanding the underlying mechanisms that promote these detrimental effects remain the major objectives for ongoing research and hope for effective virus-induced cancer therapy. Here, we review current literature with an emphasis on how DNA damage influences HPV, HVB, and EBV replication and epigenetic alterations that are associated with carcinogenesis.
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29
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Abstract
Many computational tools have been proposed during the two last decades for predicting piRNAs, which are molecules with important role in post-transcriptional gene regulation. However, these tools are mostly based on only one feature that is generally related to the sequence. Discoveries in the domain of piRNAs are still in their beginning stages, and recent publications have shown many new properties. Here, we propose an integrative approach for piRNA prediction in which several types of genomic and epigenomic properties that can be used to characterize these molecules are examined. We reviewed and extracted a large number of piRNA features from the literature that have been observed experimentally in several species. These features are represented by different kernels, in a Multiple Kernel Learning based approach, implemented within an object-oriented framework. The obtained tool, called IpiRId, shows prediction results that attain more than 90% of accuracy on different tested species (human, mouse and fly), outperforming all existing tools. Besides, our method makes it possible to study the validity of each given feature in a given species. Finally, the developed tool is modular and easily extensible, and can be adapted for predicting other types of ncRNAs. The IpiRId software and the user-friendly web-based server of our tool are now freely available to academic users at: https://evryrna.ibisc.univ-evry.fr/evryrna/.
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30
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Shi J, Li X, Dong M, Graham M, Yadav N, Liang C. JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures. PLoS One 2017; 12:e0179040. [PMID: 28582416 PMCID: PMC5459502 DOI: 10.1371/journal.pone.0179040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 05/23/2017] [Indexed: 11/19/2022] Open
Abstract
Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.
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Affiliation(s)
- Jieming Shi
- Department of Biology, Miami University, Oxford, Ohio, United States of America
| | - Xi Li
- Department of Biology, Miami University, Oxford, Ohio, United States of America
- College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, Guangxi, China
| | - Min Dong
- Department of Biology, Miami University, Oxford, Ohio, United States of America
- Department of Automation, Xiamen University, Fujian, China
| | - Mitchell Graham
- Department of Biology, Miami University, Oxford, Ohio, United States of America
| | - Nehul Yadav
- Department of Biology, Miami University, Oxford, Ohio, United States of America
| | - Chun Liang
- Department of Biology, Miami University, Oxford, Ohio, United States of America
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31
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Barman RK, Mukhopadhyay A, Das S. An improved method for identification of small non-coding RNAs in bacteria using support vector machine. Sci Rep 2017; 7:46070. [PMID: 28383059 PMCID: PMC5382675 DOI: 10.1038/srep46070] [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: 09/22/2016] [Accepted: 03/08/2017] [Indexed: 12/25/2022] Open
Abstract
Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enabled identification of sRNAs in bacteria, but experimental detection remains a challenge and grossly incomplete for most species. Thus, there is a need to develop computational tools to predict bacterial sRNAs. Here, we propose a computational method to identify sRNAs in bacteria using support vector machine (SVM) classifier. The primary sequence and secondary structure features of experimentally-validated sRNAs of Salmonella Typhimurium LT2 (SLT2) was used to build the optimal SVM model. We found that a tri-nucleotide composition feature of sRNAs achieved an accuracy of 88.35% for SLT2. We validated the SVM model also on the experimentally-detected sRNAs of E. coli and Salmonella Typhi. The proposed model had robustly attained an accuracy of 81.25% and 88.82% for E. coli K-12 and S. Typhi Ty2, respectively. We confirmed that this method significantly improved the identification of sRNAs in bacteria. Furthermore, we used a sliding window-based method and identified sRNAs from complete genomes of SLT2, S. Typhi Ty2 and E. coli K-12 with sensitivities of 89.09%, 83.33% and 67.39%, respectively.
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Affiliation(s)
- Ranjan Kumar Barman
- Biomedical Informatics Centre, National Institute Of Cholera and Enteric Diseases, Kolkata, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
| | - Santasabuj Das
- Biomedical Informatics Centre, National Institute Of Cholera and Enteric Diseases, Kolkata, West Bengal, India.,Division of Clinical Medicine, National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India
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32
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Wei L, Bowen Z, Zhiyong C, Gao X, Liao M. Exploring local discriminative information from evolutionary profiles for cytokine–receptor interaction prediction. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.02.078] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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33
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Zhang J, Yang J, Huang T, Shu Y, Chen L. Identification of novel proliferative diabetic retinopathy related genes on protein–protein interaction network. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.136] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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34
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Prabahar A, Natarajan J. Prediction of microRNAs involved in immune system diseases through network based features. J Biomed Inform 2016; 65:34-45. [PMID: 27871823 DOI: 10.1016/j.jbi.2016.11.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 09/28/2016] [Accepted: 11/13/2016] [Indexed: 12/13/2022]
Abstract
MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.
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Affiliation(s)
- Archana Prabahar
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore 641 046, India.
| | - Jeyakumar Natarajan
- Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore 641 046, India.
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35
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Singh N, Srivastava S, Shasany AK, Sharma A. Identification of miRNAs and their targets involved in the secondary metabolic pathways of Mentha spp. Comput Biol Chem 2016; 64:154-162. [DOI: 10.1016/j.compbiolchem.2016.06.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Revised: 04/07/2016] [Accepted: 06/15/2016] [Indexed: 11/28/2022]
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36
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BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species. BIOMED RESEARCH INTERNATIONAL 2016; 2016:9565689. [PMID: 27635401 PMCID: PMC5011242 DOI: 10.1155/2016/9565689] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 07/05/2016] [Accepted: 07/17/2016] [Indexed: 01/21/2023]
Abstract
MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.
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37
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Huang HH. An ensemble distance measure of k-mer and Natural Vector for the phylogenetic analysis of multiple-segmented viruses. J Theor Biol 2016; 398:136-44. [DOI: 10.1016/j.jtbi.2016.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Revised: 02/25/2016] [Accepted: 03/02/2016] [Indexed: 11/29/2022]
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38
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Improving classification of mature microRNA by solving class imbalance problem. Sci Rep 2016; 6:25941. [PMID: 27181057 PMCID: PMC4867574 DOI: 10.1038/srep25941] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 04/22/2016] [Indexed: 11/29/2022] Open
Abstract
MicroRNAs (miRNAs) are ~20–25 nucleotides non-coding RNAs, which regulated gene expression in the post-transcriptional level. The accurate rate of identifying the start sit of mature miRNA from a given pre-miRNA remains lower. It is noting that the mature miRNA prediction is a class-imbalanced problem which also leads to the unsatisfactory performance of these methods. We improved the prediction accuracy of classifier using balanced datasets and presented MatFind which is used for identifying 5′ mature miRNAs candidates from their pre-miRNA based on ensemble SVM classifiers with idea of adaboost. Firstly, the balanced-dataset was extract based on K-nearest neighbor algorithm. Secondly, the multiple SVM classifiers were trained in orderly using the balance datasets base on represented features. At last, all SVM classifiers were combined together to form the ensemble classifier. Our results on independent testing dataset show that the proposed method is more efficient than one without treating class imbalance problem. Moreover, MatFind achieves much higher classification accuracy than other three approaches. The ensemble SVM classifiers and balanced-datasets can solve the class-imbalanced problem, as well as improve performance of classifier for mature miRNA identification. MatFind is an accurate and fast method for 5′ mature miRNA identification.
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39
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Chen J, Liu B, Huang D. Protein Remote Homology Detection Based on an Ensemble Learning Approach. BIOMED RESEARCH INTERNATIONAL 2016; 2016:5813645. [PMID: 27294123 PMCID: PMC4875977 DOI: 10.1155/2016/5813645] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Accepted: 02/21/2016] [Indexed: 12/15/2022]
Abstract
Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.
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Affiliation(s)
- Junjie Chen
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Bingquan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Dong Huang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
- Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
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40
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Chen W, Tang H, Lin H. MethyRNA: a web server for identification of N6-methyladenosine sites. J Biomol Struct Dyn 2016; 35:683-687. [DOI: 10.1080/07391102.2016.1157761] [Citation(s) in RCA: 74] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Wei Chen
- Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China
| | - Hua Tang
- Department of Pathophysiology, Sichuan Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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41
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Liu B, Wang S, Dong Q, Li S, Liu X. Identification of DNA-binding proteins by combining auto-cross covariance transformation and ensemble learning. IEEE Trans Nanobioscience 2016; 15:328-334. [PMID: 28113908 DOI: 10.1109/tnb.2016.2555951] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. With the rapid development of next generation of sequencing technique, the number of protein sequences is unprecedentedly increasing. Thus it is necessary to develop computational methods to identify the DNA-binding proteins only based on the protein sequence information. In this study, a novel method called iDNA-KACC is presented, which combines the Support Vector Machine (SVM) and the auto-cross covariance transformation. The protein sequences are first converted into profile-based protein representation, and then converted into a series of fixed-length vectors by the auto-cross covariance transformation with Kmer composition. The sequence order effect can be effectively captured by this scheme. These vectors are then fed into Support Vector Machine (SVM) to discriminate the DNA-binding proteins from the non DNA-binding ones. iDNA-KACC achieves an overall accuracy of 75.16% and Matthew correlation coefficient of 0.5 by a rigorous jackknife test. Its performance is further improved by employing an ensemble learning approach, and the improved predictor is called iDNA-KACC-EL. Experimental results on an independent dataset shows that iDNA-KACC-EL outperforms all the other state-of-the-art predictors, indicating that it would be a useful computational tool for DNA binding protein identification. .
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42
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Wang R, Xu Y, Liu B. Recombination spot identification Based on gapped k-mers. Sci Rep 2016; 6:23934. [PMID: 27030570 PMCID: PMC4814916 DOI: 10.1038/srep23934] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 03/16/2016] [Indexed: 12/14/2022] Open
Abstract
Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have proposed several computational methods for recombination spot identification. The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics.
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Affiliation(s)
- Rong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Yong Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
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43
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Paul S, Vera J. Rough hypercuboid based supervised clustering of miRNAs. MOLECULAR BIOSYSTEMS 2016; 11:2068-81. [PMID: 25996345 DOI: 10.1039/c5mb00213c] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The microRNAs are small, endogenous non-coding RNAs found in plants, animals, and some viruses, which function in RNA silencing and post-transcriptional regulation of gene expression. It is suggested by various genome-wide studies that a substantial fraction of miRNA genes is likely to form clusters. The coherent expression of the miRNA clusters can then be used to classify samples according to the clinical outcome. In this regard, a new clustering algorithm, termed as rough hypercuboid based supervised attribute clustering (RH-SAC), is proposed to find such groups of miRNAs. The proposed algorithm is based on the theory of rough set, which directly incorporates the information of sample categories into the miRNA clustering process, generating a supervised clustering algorithm for miRNAs. The effectiveness of the new approach is demonstrated on several publicly available miRNA expression data sets using support vector machine. The so-called B.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. The association of the miRNA clusters to various biological pathways is also shown by doing pathway enrichment analysis.
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Affiliation(s)
- Sushmita Paul
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University of Erlangen-Nürnberg, Hartmannstr. 14, 91052 Erlangen, Germany.
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44
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Ding H, Liang ZY, Guo FB, Huang J, Chen W, Lin H. Predicting bacteriophage proteins located in host cell with feature selection technique. Comput Biol Med 2016; 71:156-61. [PMID: 26945463 DOI: 10.1016/j.compbiomed.2016.02.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 02/18/2016] [Accepted: 02/18/2016] [Indexed: 10/22/2022]
Abstract
A bacteriophage is a virus that can infect a bacterium. The fate of an infected bacterium is determined by the bacteriophage proteins located in the host cell. Thus, reliably identifying bacteriophage proteins located in the host cell is extremely important to understand their functions and discover potential anti-bacterial drugs. Thus, in this paper, a computational method was developed to recognize bacteriophage proteins located in host cells based only on their amino acid sequences. The analysis of variance (ANOVA) combined with incremental feature selection (IFS) was proposed to optimize the feature set. Using a jackknife cross-validation, our method can discriminate between bacteriophage proteins located in a host cell and the bacteriophage proteins not located in a host cell with a maximum overall accuracy of 84.2%, and can further classify bacteriophage proteins located in host cell cytoplasm and in host cell membranes with a maximum overall accuracy of 92.4%. To enhance the value of the practical applications of the method, we built a web server called PHPred (〈http://lin.uestc.edu.cn/server/PHPred〉). We believe that the PHPred will become a powerful tool to study bacteriophage proteins located in host cells and to guide related drug discovery.
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Affiliation(s)
- Hui Ding
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Zhi-Yong Liang
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Feng-Biao Guo
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Jian Huang
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China; Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology and Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
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45
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Liu B, Fang L. WITHDRAWN: Identification of microRNA precursor based on gapped n-tuple structure status composition kernel. Comput Biol Chem 2016:S1476-9271(16)30036-6. [PMID: 26935400 DOI: 10.1016/j.compbiolchem.2016.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Accepted: 02/01/2016] [Indexed: 10/22/2022]
Abstract
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Affiliation(s)
- Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
| | - Longyun Fang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
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46
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Chen J, Wang X, Liu B. iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions. Sci Rep 2016; 6:19062. [PMID: 26753561 PMCID: PMC4709562 DOI: 10.1038/srep19062] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Accepted: 12/02/2015] [Indexed: 11/09/2022] Open
Abstract
The identification of microRNA precursors (pre-miRNAs) helps in understanding regulator in biological processes. The performance of computational predictors depends on their training sets, in which the negative sets play an important role. In this regard, we investigated the influence of benchmark datasets on the predictive performance of computational predictors in the field of miRNA identification, and found that the negative samples have significant impact on the predictive results of various methods. We constructed a new benchmark set with different data distributions of negative samples. Trained with this high quality benchmark dataset, a new computational predictor called iMiRNA-SSF was proposed, which employed various features extracted from RNA sequences. Experimental results showed that iMiRNA-SSF outperforms three state-of-the-art computational methods. For practical applications, a web-server of iMiRNA-SSF was established at the website http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/.
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Affiliation(s)
- Junjie Chen
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Xiaolong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.,Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China.,Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
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47
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Aftabi Y, Colagar AH, Mehrnejad F. An in silico approach to investigate the source of the controversial interpretations about the phenotypic results of the human AhR-gene G1661A polymorphism. J Theor Biol 2016; 393:1-15. [PMID: 26776670 DOI: 10.1016/j.jtbi.2016.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2015] [Revised: 12/11/2015] [Accepted: 01/01/2016] [Indexed: 12/21/2022]
Abstract
Aryl hydrocarbon receptor (AhR) acts as an enhancer binding ligand-activated intracellular receptor. Chromatin remodeling components and general transcription factors such as TATA-binding protein (TBP) are evoked on AhR-target genes by interaction with its flexible transactivation domain (TAD). AhR-G1661A single nucleotide polymorphism (SNP: rs2066853) causes an arginine to lysine substitution in the acidic sub-domain of TAD at position 554 (R554K). Although, numerous studies associate the SNP with some abnormalities such as cancer, other reliable investigations refuse the associations. Consequently, the interpretation of the phenotypic results of G1661A-transition has been controversial. In this study, an in silico analysis were performed to investigate the possible effects of the transition on AhR-mRNA, protein structure, interaction properties and modifications. The analysis revealed that the R554K substitution affects secondary structure and solvent accessibility of adjacent residues. Also, it causes to decreasing of the AhR stability; altering the hydropathy features of the local sequence and changing the pattern of the residues at the binding site of the TAD-acidic sub-domain. Generating of new sites for ubiquitination and acetylation for AhR-K554 variant respectively at positions 544 and 560 was predicted. Our findings intensify the idea that the AhR-G1661A transition may affects AhR-TAD interactions, especially with the TBP, which influence AhR-target genes expression. However, the previously reported flexibility of the modular TAD could act as an intervening factor, moderate the SNP effects and causes distinct outcomes in different individuals and tissues.
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Affiliation(s)
- Younes Aftabi
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran
| | - Abasalt Hosseinzadeh Colagar
- Department of Molecular and Cell Biology, Faculty of Basic Sciences, University of Mazandaran, Babolsar, Post Code: 47416-95447, Mazandaran, Iran.
| | - Faramarz Mehrnejad
- Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, P.O. Box: 14395-1561, Tehran, Iran
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Ahmad K, Waris M, Hayat M. Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition. J Membr Biol 2016; 249:293-304. [PMID: 26746980 DOI: 10.1007/s00232-015-9868-8] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 12/30/2015] [Indexed: 12/15/2022]
Abstract
Mitochondrion is the key organelle of eukaryotic cell, which provides energy for cellular activities. Submitochondrial locations of proteins play crucial role in understanding different biological processes such as energy metabolism, program cell death, and ionic homeostasis. Prediction of submitochondrial locations through conventional methods are expensive and time consuming because of the large number of protein sequences generated in the last few decades. Therefore, it is intensively desired to establish an automated model for identification of submitochondrial locations of proteins. In this regard, the current study is initiated to develop a fast, reliable, and accurate computational model. Various feature extraction methods such as dipeptide composition (DPC), Split Amino Acid Composition, and Composition and Translation were utilized. In order to overcome the issue of biasness, oversampling technique SMOTE was applied to balance the datasets. Several classification learners including K-Nearest Neighbor, Probabilistic Neural Network, and support vector machine (SVM) are used. Jackknife test is applied to assess the performance of classification algorithms using two benchmark datasets. Among various classification algorithms, SVM achieved the highest success rates in conjunction with the condensed feature space of DPC, which are 95.20 % accuracy on dataset SML3-317 and 95.11 % on dataset SML3-983. The empirical results revealed that our proposed model obtained the highest results so far in the literatures. It is anticipated that our proposed model might be useful for future studies.
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Affiliation(s)
- Khurshid Ahmad
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Muhammad Waris
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Maqsood Hayat
- Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, Pakistan.
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Predicting cancerlectins by the optimal g-gap dipeptides. Sci Rep 2015; 5:16964. [PMID: 26648527 PMCID: PMC4673586 DOI: 10.1038/srep16964] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Accepted: 10/22/2015] [Indexed: 12/14/2022] Open
Abstract
The cancerlectin plays a key role in the process of tumor cell differentiation. Thus, to fully understand the function of cancerlectin is significant because it sheds light on the future direction for the cancer therapy. However, the traditional wet-experimental methods were money- and time-consuming. It is highly desirable to develop an effective and efficient computational tool to identify cancerlectins. In this study, we developed a sequence-based method to discriminate between cancerlectins and non-cancerlectins. The analysis of variance (ANOVA) was used to choose the optimal feature set derived from the g-gap dipeptide composition. The jackknife cross-validated results showed that the proposed method achieved the accuracy of 75.19%, which is superior to other published methods. For the convenience of other researchers, an online web-server CaLecPred was established and can be freely accessed from the website http://lin.uestc.edu.cn/server/CalecPred. We believe that the CaLecPred is a powerful tool to study cancerlectins and to guide the related experimental validations.
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50
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Survey of Natural Language Processing Techniques in Bioinformatics. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:674296. [PMID: 26525745 PMCID: PMC4615216 DOI: 10.1155/2015/674296] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 06/12/2015] [Accepted: 06/21/2015] [Indexed: 01/02/2023]
Abstract
Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.
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