Computational models for lncRNA function prediction and functional similarity calculation

Abstract From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further...

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Published inBriefings in functional genomics Vol. 18; no. 1; pp. 58 - 82
Main Authors Chen, Xing, Sun, Ya-Zhou, Guan, Na-Na, Qu, Jia, Huang, Zhi-An, Zhu, Ze-Xuan, Li, Jian-Qiang
Format Journal Article
LanguageEnglish
Published England Oxford University Press 14.02.2019
Subjects
Algorithms
biomarkers
Computational Biology - methods
Computer Simulation
Disease - genetics
Gene Regulatory Networks
genomics
Humans
nervous system
non-coding RNA
prediction
prognosis
RNA, Long Noncoding - genetics
therapeutics
transcription (genetics)
computational model
functional similarity calculation
database
long non-coding RNA
disease
function prediction
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Abstract Abstract From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function–related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.
AbstractList From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.
From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function–related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.
Abstract From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function–related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.
Author Qu, Jia
Huang, Zhi-An
Li, Jian-Qiang
Sun, Ya-Zhou
Zhu, Ze-Xuan
Chen, Xing
Guan, Na-Na
Author_xml – sequence: 1
  givenname: Xing
  orcidid: 0000-0001-9028-5342
  surname: Chen
  fullname: Chen, Xing
  email: xingchen@amss.ac.cn
  organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
– sequence: 2
  givenname: Ya-Zhou
  surname: Sun
  fullname: Sun, Ya-Zhou
  organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
– sequence: 3
  givenname: Na-Na
  surname: Guan
  fullname: Guan, Na-Na
  organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
– sequence: 4
  givenname: Jia
  surname: Qu
  fullname: Qu, Jia
  organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
– sequence: 5
  givenname: Zhi-An
  surname: Huang
  fullname: Huang, Zhi-An
  organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
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  givenname: Ze-Xuan
  surname: Zhu
  fullname: Zhu, Ze-Xuan
  organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
– sequence: 7
  givenname: Jian-Qiang
  surname: Li
  fullname: Li, Jian-Qiang
  organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30247501$$D View this record in MEDLINE/PubMed
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Copyright_xml – notice: The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2018
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Issue 1
Keywords computational model
functional similarity calculation
database
long non-coding RNA
disease
function prediction
Language English
License This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
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PublicationTitle Briefings in functional genomics
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Snippet Abstract From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human...
From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex...
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SubjectTerms Algorithms
biomarkers
Computational Biology - methods
Computer Simulation
Disease - genetics
Gene Regulatory Networks
genomics
Humans
nervous system
non-coding RNA
prediction
prognosis
RNA, Long Noncoding - genetics
therapeutics
transcription (genetics)
Title Computational models for lncRNA function prediction and functional similarity calculation
URI https://www.ncbi.nlm.nih.gov/pubmed/30247501
https://www.proquest.com/docview/2111743852
https://www.proquest.com/docview/2551942499
Volume 18
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