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Identification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data

1 School of Mathematics, Shandong University, Jinan, Shandong 250100, China
2 Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
3 College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China

Special Issues: Machine Learning in Molecular Biology

Long noncoding RNAs (lncRNA) play important roles in gene expression regulation in diverse biological contexts. Numerous studies have indicated that lncRNA-gene interactions are closely related to the occurrence and development of cancers. Thus, it is important to develop an effective method for the identification of target genes of lncRNA. Meanwhile, the high throughput sequencing data provide tremendous information about regulation correlation, by which the new target genes could be detected from known lncRNA regulated genes. In this study, we developed a method for elucidating lncRNA-gene interactions by using a biclustering approach, which allows for the identification of particular expression patterns across multiple datasets, indicating networks of lncRNA and gene interactions. A p-value strategy is followed to link co-expression patterns to certain lncRNAs. The method was applied on the breast cancer RNA-seq datasets along with a set of known lncRNA regulated genes. The evaluation indicated that the method can detect some new targets but fail to obtain higher coverage. We believe that this developed method will provide useful information for future studies on lncRNAs.
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Keywords lncRNA; Biclustering; RNA-seq; gene regulation; DNA motif

Citation: Sijie Lu, Juan Xie, Yang Li, Bin Yu, Qin Ma, Bingqiang Liu. Identification of lncRNAs-gene interactions in transcription regulation based on co-expression analysis of RNA-seq data. Mathematical Biosciences and Engineering, 2019, 16(6): 7112-7125. doi: 10.3934/mbe.2019357

References

  • 1. V. S. Patil, R. Zhou and T. M. Rana, Gene regulation by non-coding RNAs, Crit. Rev. Biochem. Mol. Biol., 49(2014), 16–32.
  • 2. J. Carlevaro-Fita, L. Liu, Y. Zhou, et al., LnCompare: gene set feature analysis for human long non-coding RNAs, Nucleic Acids Res., 47(2019), W523–W529.
  • 3. K. W. Vance and C. P. Ponting, Transcriptional regulatory functions of nuclear long noncoding RNAs, Trends Genet., 30(2014), 348–355.
  • 4. F. Ferre, A. Colantoni and M. Helmer-Citterich, Revealing protein-lncRNA interaction, Brief Bioinform., 17(2016), 106–116.
  • 5. A. E. Kornienko, P. M. Guenzl, D. P. Barlow, et al., Gene regulation by the act of long non-coding RNA transcription, BMC Biol., 11(2013), 59.
  • 6. J. L. Rinn and H. Y. Chang, Genome regulation by long noncoding RNAs, Annu. Rev. Biochem., 81(2012), 145–166.
  • 7. S. J. Liu, M. A. Horlbeck, S. M. Cho, et al., CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells, Science, 355(2017), eaah7111.
  • 8. Y. Meng and F. Yu, Long non coding RNA FAM3D-AS1 inhibits development of colorectal cancer through NF-kB signaling pathway, Biosci. Rep., 39(2019), online published.
  • 9. L. Peng, S. Gao, F. Bai, et al., LncRNA TPTE2P1 promotes the proliferation of thyroid carcinoma by inhibiting miR-520c-3p, Panminerva Med., (2019), online published.
  • 10. X. Chen, Q. Ma, X. Rao, et al., Genome-scale identification of cell-wall-related genes in switchgrass through comparative genomics and computational analyses of transcriptomic data, BioEnergy Res., 9(2016), 172–180.
  • 11. S. Wang, Y. Yin, Q. Ma, et al., Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis, BMC Plant Biol., 12(2012), 138.
  • 12. I. Ulitsky, A. Maron-Katz, S. Shavit, et al., Expander: From expression microarrays to networks and functions, Nat. Protoc., 5(2010), 303–322.
  • 13. Z. Zhou, Y. Shen, M. R. Khan, et al., LncReg: A reference resource for lncRNA-associated regulatory networks, Database (Oxford)., (2015), bav083.
  • 14. D. Smedley, S. Haider, S. Durinck, et al., The BioMart community portal: an innovative alternative to large, centralized data repositories, Nucleic Acids Res., 43(2015), W589-W598.
  • 15. G. Li, Q. Ma, H. Tang, et al., QUBIC: A qualitative biclustering algorithm for analyses of gene expression data, Nucleic Acids Res., 37(2009), e101.
  • 16. C. I. Castillo-Davis and D. L. Hartl, GeneMerge-post-genomic analysis, data mining, and hypothesis testing, Bioinformatics, 19(2003), 891–892.
  • 17. S. Heinz, C. Benner, N. Spann, et al., Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities, Mol. Cell., 38(2010), 576–589.
  • 18. A. Khan, O. Fornes, A. Stigliani, et al., JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework, Nucleic Acids Res., 46(2017), D260–D266.
  • 19. J. Yang, X. Chen, A. McDermaid, et al., DMINDA 2.0: Integrated and systematic views of regulatory DNA motif identification and analyses, Bioinformatics, 33(2017), 2586–2588.
  • 20. Y. Zhang, J. Xie, J. Yang, et al., QUBIC: A bioconductor package for qualitative biclustering analysis of gene co-expression data, Bioinformatics, 33(2017), 450–452.
  • 21. J. Xie, A. Ma, Y. Zhang, et al., QUBIC2: A novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis, bioRxiv, (2018), 409961.
  • 22. G. Li, B. Liu and Y. Xu, Accurate recognition of cis-regulatory motifs with the correct lengths in prokaryotic genomes, Nucleic Acids Res., 38(2010), e12.
  • 23. J. J. Quinn and H. Y. Chang, Unique features of long non-coding RNA biogenesis and function, Nat. Rev. Genet., 17(2016), 47–62.
  • 24. M. Rossi, G. Bucci, D. Rizzotto, et al., LncRNA EPR controls epithelial proliferation by coordinating Cdkn1a transcription and mRNA decay response to TGF-beta, Nat. Commun., 10(2019), 1969.
  • 25. G. Li, B. Liu, Q. Ma, et al., A new framework for identifying cis-regulatory motifs in prokaryotes, Nucleic Acids Res., 39(2011), e42.

 

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