Skip to main content
SpringerLink
Log in

Towards a better prediction of subcellular location of long non-coding RNA

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

The spatial distribution pattern of long non-coding RNA (lncRNA) in cell is tightly related to their function. With the increment of publicly available subcellular location data, a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA. Unfortunately, these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling. To address those issues and enhance the prediction performance, we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy. As a result, the new predictor could achieve the overall accuracy of 91.60%. The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website. It will help to get the knowledge of lncRNA subcellular localization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Chiu H S, Somvanshi S, Patel E, Chen T W, Singh V P, Zorman B, Patil S L, Pan Y, Chatterjee S S, Cancer Genome Atlas Research N, Sood A K, Gunaratne P H, Sumazin P. Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context. Cell Reports, 2018, 23(1): 297–312.e12

    Article  Google Scholar 

  2. Ji J, Tang J, Xia KJ, Jiang R. LncRNA in tumorigenesis microenvironment. Current Bioinformatics, 2019, 14(7): 640–641

    Article  Google Scholar 

  3. Guo C J, Xu G, Chen L L. Mechanisms of long noncoding RNA nuclear retention. Trends in Biochemical Sciences, 2020, 45(11): 947–960

    Article  Google Scholar 

  4. Chowdhury M R, Basak J, Bahadur R P. Elucidating the functional role of predicted miRNAs in post-transcriptional gene regulation along with symbiosis in medicago truncatula. Current Bioinformatics, 2020, 15(2): 108–120

    Article  Google Scholar 

  5. Cheng L, Hu Y, Sun J, Zhou M, Jiang Q. DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function. Bioinformatics, 2018, 34(11): 1953–1956

    Article  Google Scholar 

  6. Cheng L, Wang P, Tian R, Wang S, Guo Q, Luo M, Zhou W, Liu G, Jiang H, Jiang Q. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Research, 2019, 47(D1): D140–D144

    Article  Google Scholar 

  7. Jiang Q, Ma R, Wang J, Wu X, Jin S, Peng J, Tan R, Zhang T, Li Y, Wang Y. LncRNA2Function: a comprehensive resource for functional investigation of human lncRNAs based on RNA-seq data. BMC Genomics, 2015, 16(3): 1–11

    Google Scholar 

  8. Jiang Q, Wang J, Wu X, Ma R, Zhang T, Jin S, Han Z, Tan R, Peng J, Liu G, Li Y, Wang Y. LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression. Nucleic Acids Research, 2015, 43(Database issue): D193–196

    Article  Google Scholar 

  9. Jiang Q, Wang J, Wang Y, Ma R, Wu X, Li Y. TF2LncRNA: identifying common transcription factors for a list of lncRNA genes from ChIP-Seq data. Biomed Research International, 2014, 2014: 317642

    Article  Google Scholar 

  10. Ning L, Cui T, Zheng B, Wang N, Luo J, Yang B, Du M, Cheng J, Dou Y, Wang D. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Research, 2021, 49(D1): D160–d164

    Article  Google Scholar 

  11. Mora-Marquez F, Luis Vazquez-Poletti J, Chano V, Collada C, Soto A, Lopez de Heredia U. Hardware performance evaluation of de novo transcriptome assembly software in amazon elastic compute cloud. Current Bioinformatics, 2020, 15(5): 420–430

    Article  Google Scholar 

  12. Hu B, Zheng L, Long C, Song M, Li T, Yang L, Zuo Y. EmExplorer: a database for exploring time activation of gene expression in mammalian embryos. Open Biology, 2019, 9(6): 190054

    Article  Google Scholar 

  13. Zhu X, Li H D, Guo L, Wu F X, Wang J. Analysis of single-cell RNA-seq data by clustering approaches. Current Bioinformatics, 2019, 14(4): 314–322

    Article  Google Scholar 

  14. Zhang T, Tan P, Wang L, Jin N, Li Y, Zhang L, Yang H, Hu Z, Zhang L, Hu C, Li C, Qian K, Zhang C, Huang Y, Li K, Lin H, Wang D. RNALocate: a resource for RNA subcellular localizations. Nucleic Acids Research, 2017, 45(D1): D135–D138

    Google Scholar 

  15. Mas-Ponte D, Carlevaro-Fita J, Palumbo E, Hermoso Pulido T, Guigo R, Johnson R. LncATLAS database for subcellular localization of long noncoding RNAs. RNA, 2017, 23(7): 1080–1087

    Article  Google Scholar 

  16. Wen X, Gao L, Guo X, Li X, Huang X, Wang Y, Xu H, He R, Jia C, Liang F. lncSLdb: a resource for long non-coding RNA subcellular localization. Database (Oxford), 2018, 2018: 1–6

    Article  Google Scholar 

  17. Gudenas B L, Wang L. Prediction of LncRNA subcellular localization with deep learning from sequence features. Science Reports, 2018, 8(1): 16385

    Article  Google Scholar 

  18. Zhao T, Hu Y, Peng J, Cheng L. DeepLGP: a novel deep learning method for prioritizing lncRNA target genes. Bioinformatics, 2020, 36(16): 4466–4472

    Article  Google Scholar 

  19. Zhao T, Hu Y, Cheng L. Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning Approaches. Briefings in Bioinformatics, 2020, 22(4): bbaa212

    Article  Google Scholar 

  20. Wu B, Zhang H, Lin L, Wang H, Gao Y, Zhao L, Chen Y-P P, Chen R, Gu L. A similarity searching system for biological phenotype images using deep convolutional encoder-decoder architecture. Current Bioinformatics, 2019, 14(7): 628–639

    Article  Google Scholar 

  21. Charoenkwan P, Nantasenamat C, Hasan M M, Shoombuatong W. Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. Journal of Computer-Aided Molecular Design, 2020, 34(10): 1105–1116

    Article  Google Scholar 

  22. Liu K, Cao L, Du P, Chen W. im6A-TS-CNN: identifying the N(6)-methyladenine site in multiple tissues by using the convolutional neural network. Molecular Therapy-Nucleic Acids, 2020, 21: 1044–1049

    Article  Google Scholar 

  23. Zuckerman B, Ulitsky I. Predictive models of subcellular localization of long RNAs. RNA, 2019, 25(5): 557–572

    Article  Google Scholar 

  24. Dong Y M, Bi J H, He Q E, Song K. ESDA: an improved approach to accurately identify human snoRNAs for precision cancer therapy. Current Bioinformatics, 2020, 15(1): 34–40

    Article  Google Scholar 

  25. Cao Z, Pan X, Yang Y, Huang Y, Shen H B. The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier. Bioinformatics, 2018, 34(13): 2185–2194

    Article  Google Scholar 

  26. Su Z D, Huang Y, Zhang Z Y, Zhao Y W, Wang D, Chen W, Chou K C, Lin H. iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics, 2018, 34(24): 4196–4204

    Article  Google Scholar 

  27. Ahmad A, Lin H, Shatabda S. Locate-R: subcellular localization of long non-coding RNAs using nucleotide compositions. Genomics, 2020, 112(3): 2583–2589

    Article  Google Scholar 

  28. Feng S, Liang Y, Du W, Lv W, Li Y. LncLocation: efficient subcellular location prediction of long non-coding RNA-based multi-source heterogeneous feature fusion. International Journal of Molecular Sciences, 2020, 21(19): 7271

    Article  Google Scholar 

  29. Wang Y, Shi F, Cao L, Dey N, Wu Q, Ashour A S, Sherratt R S, Rajinikanth V, Wu L. Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Current Bioinformatics, 2019, 14(4): 282–294

    Article  Google Scholar 

  30. Pruitt K D, Tatusova T, Maglott D R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research, 2007, 35(Database issue): D61–65

    Article  Google Scholar 

  31. Lai H Y, Zhang Z Y, Su Z D, Su W, Ding H, Chen W, Lin H. iProEP: a computational predictor for predicting promoter. Molecular Therapy-Nucleic Acids, 2019, 17: 337–346

    Article  Google Scholar 

  32. Liu K, Chen W. iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics, 2020, 36(11): 3336–3342

    Article  Google Scholar 

  33. Hasan M M, Basith S, Khatun M S, Lee G, Manavalan B, Kurata H. Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework. Briefings in Bioinformatics, 2020, 22(3): bbaa202

    Article  Google Scholar 

  34. Manavalan B, Basith S, Shin T H, Wei L, Lee G. Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation. Molecular Therapy-Nucleic Acids, 2019, 16: 733–744

    Article  Google Scholar 

  35. Basith S, Manavalan B, Shin T H, Lee G. SDM6A: a web-based integrative machine-learning framework for predicting 6mA sites in the rice genome. Molecular Therapy-Nucleic Acids, 2019, 18: 131–141

    Article  Google Scholar 

  36. Zheng L, Huang S, Mu N, Zhang H, Zhang J, Chang Y, Yang L, Zuo Y. RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule. Database (Oxford), 2019

  37. Zhang Z Y, Yang Y H, Ding H, Wang D, Chen W, Lin H. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Briefings in Bioinformatics, 2021, 22(1): 526–535

    Article  Google Scholar 

  38. Zhang J, Liu B. A review on the recent developments of sequence-based protein feature extraction methods. Current Bioinformatics, 2019, 14(3): 190–199

    Article  Google Scholar 

  39. Liang P F, Yang W R, Chen X, Long C S, Zheng L, Li H S, Zuo Y C. Machine learning of single-cell transcriptome highly identifies mRNA signature by comparing F-score selection with DGE analysis. Molecular Therapy-Nucleic Acids, 2020, 20: 155–163

    Article  Google Scholar 

  40. Liu K, Chen W, Lin H. XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites. Molecular Genetics and Genomics, 2020, 295(1): 13–21

    Article  Google Scholar 

  41. Guo X, Gao L, Wang Y, Chiu D K Y, Wang B, Deng Y, Wen X. Large-scale investigation of long noncoding RNA secondary structures in human and mouse. Current Bioinformatics, 2018, 13(5): 450–460

    Article  Google Scholar 

  42. Zhang D, Xu Z C, Su W, Yang Y H, Lv H, Yang H, Lin H. iCarPS: a computational tool for identifying protein carbonylation sites by novel encoded features. Bioinformatics, 2021, 37(2): 171–177

    Article  Google Scholar 

  43. Wang S P, Zhang Q, Lu J, Cai Y D. Analysis and prediction of nitrated tyrosine sites with the mRMR method and support vector machine algorithm. Current Bioinformatics, 2018, 13(1): 3–13

    Article  Google Scholar 

  44. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226–1238

    Article  Google Scholar 

  45. Chen J, Zhao J, Yang S, Chen Z, Zhang Z. Prediction of protein ubiquitination sites in arabidopsis thaliana. Current Bioinformatics, 2019, 14(7): 614–620

    Article  Google Scholar 

  46. Charoenkwan P, Nantasenamat C, Hasan M M, Shoombuatong W. iTTCA-Hybrid: improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation. Analytical Biochemistry, 2020, 599: 113747

    Article  Google Scholar 

  47. Jiang Q, Wang G, Jin S, Li Y, Wang Y. Predicting human microRNA-disease associations based on support vector machine. International Journal of Dato Mining and Bioinformatics, 2013, 8(3): 282–293

    Article  Google Scholar 

  48. Chang C C, Lin C J. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27

    Article  Google Scholar 

  49. Wei L, He W, Malik A, Su R, Cui L, Manavalan B. Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework. Briefings in Bioinformatics, 2021, 22(4): bbaa275

    Article  Google Scholar 

  50. Hasan M M, Manavalan B, Shoombuatong W, Khatun M S, Kurata H. i4mC-Mouse: improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes. Computational and Structural Biotechnology Journal, 2020, 18: 906–912

    Article  Google Scholar 

  51. Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan M M, Shoombuatong W. iBitter-SCM: identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics, 2020, 112(4): 2813–2822

    Article  Google Scholar 

  52. Charoenkwan P, Chiangjong W, Lee V S, Nantasenamat C, Hasan M M, Shoombuatong W. Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method. Scientific Reports, 2021, 11(1): 1–13

    Article  Google Scholar 

  53. Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan M M, Shoombuatong W. iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. Journal of Proteome Research, 2020, 19(10): 4125–4136

    Article  Google Scholar 

  54. Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan M M, Shoombuatong W. iAMY-SCM: improved prediction and analysis of amyloid proteins using a scoring card method with propensity scores of dipeptides. Genomics, 2021, 113(1): 689–698

    Article  Google Scholar 

  55. Charoenkwan P, Kanthawong S, Schaduangrat N, Yana J, Shoombuatong W. PVPred-SCM: improved prediction and analysis of phage virion proteins using a scoring card method. Cells, 2020, 9(2): 353

    Article  Google Scholar 

  56. Charoenkwan P, Nantasenamat C, Hasan M M, Shoombuatong W. iTTCA-Hybrid: improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation. Analytical Biochemistry, 2020, 599: 113747

    Article  Google Scholar 

  57. Charoenkwan P, Shoombuatong W, Lee H C, Chaijaruwanich J, Huang H L, Ho S Y. SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs. PLoS ONE, 2013, 8(9): e72368

    Article  Google Scholar 

  58. Charoenkwan P, Yana J, Nantasenamat C, Hasan M M, Shoombuatong W. iUmami-SCM: a novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides. Journal of Chemical Information and Modeling, 2020, 60(12): 6666–6678

    Article  Google Scholar 

  59. Long H, Sun Z, Li M, Fu H Y, Lin M C. Predicting protein phosphorylation sites based on deep learning. Current Bioinformatics, 2020, 15(4): 300–308

    Article  Google Scholar 

  60. Cheng L. Computational and biological methods for gene therapy. Current Gene Therapy, 2019, 19(4): 210–210

    Article  Google Scholar 

  61. Cheng L, Hu Y. Human disease system biology. Current Gene Therapy, 2018, 18(5): 255–256

    Article  Google Scholar 

  62. Kuang L, Zhao H, Wang L, Xuan Z, Pei T. A novel approach based on point cut set to predict associations of diseases and LncRNAs. Current Bioinformatics, 2019, 14(4): 333–343

    Article  Google Scholar 

  63. Chen W, Feng P, Song X, Lv H, Lin H. iRNA-m7G: identifying N(7)-methylguanosine sites by fusing multiple features. Molecular Therapy Nucleic Acids, 2019, 18: 269–274

    Article  Google Scholar 

  64. Liu D, Li G, Zuo Y. Function determinants of TET proteins: the arrangements of sequence motifs with specific codes. Briefings in Bioinformatics, 2019, 20(5): 1826–1835

    Article  Google Scholar 

  65. Zheng L, Liu D, Yang W, Yang L, Zuo Y. RaacLogo: a new sequence logo generator by using reduced amino acid clusters. Briefings in Bioinformatics, 2021, 22(3): bbaa096

    Article  Google Scholar 

  66. Bailey T L. DREME: motif discovery in transcription factor ChIP-seq data. Bioinformatics, 2011, 27(12): 1653–1659

    Article  Google Scholar 

  67. Ginestet C. ggplot2: elegant graphics for data analysis. Journal of the Royal Statistical Society Series a-Statistics in Society, 2011, 174: 245–245

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Scientific Foundation of China (Grant No. 61772119), Sichuan Provincial Science Fund for Distinguished Young Scholars (2020JDJQ0012).

Author information

Authors and Affiliations

  1. School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Key Laboratory for NeuroInformation of Ministry of Education, Chengdu, 610054, China

    Zhao-Yue Zhang, Zi-Jie Sun, Yu-He Yang & Hao Lin

Authors
  1. Zhao-Yue Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zi-Jie Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu-He Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hao Lin.

Additional information

Zhao-Yue Zhang received the MS degree in Biophysics from University of Electronic Science and Technology of China, China. She is a research assistant of Center for Informational Biology and the Key Laboratory for NeuroInformation of Ministry of Education in University of Electronic Science and Technology of China. Her research interests are bioinformatics, machine learning and RNA subcellular localization.

Zi-Jie Sun is a graduate student at the Center for Informational Biology, University of Electronic Science and Technology of China, China. Her research interests are bioinformatics, statistical analysis and drug repositioning.

Yu-He Yang is a graduate student at the Center for Informational Biology, University of Electronic Science and Technology of China, China. Her research interests are bioinformatics, machine learning and RNA methylation.

Hao Lin is a professor at the Center for Informational Biology, University of Electronic Science and Technology of China, China. His research is in the areas of bioinformatics and system biology.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, ZY., Sun, ZJ., Yang, YH. et al. Towards a better prediction of subcellular location of long non-coding RNA. Front. Comput. Sci. 16, 165903 (2022). https://doi.org/10.1007/s11704-021-1015-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-021-1015-3

Keywords

Search

Navigation