Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Jul 26;7(30):47864-47874.
doi: 10.18632/oncotarget.10012.

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Liang Cheng  1 , Hongbo Shi  1 , Zhenzhen Wang  1 , Yang Hu  2 , Haixiu Yang  1 , Chen Zhou  1 , Jie Sun  1 , Meng Zhou  1
Affiliations

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Liang Cheng et al. Oncotarget. .

Abstract

Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.

Keywords: integrated network; lncRNA functional similarity; lncRNA-disease associations; long non-coding RNAs.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no of interest.

Figures

Figure 1
Figure 1. Performance evaluation of IntNetLncSim
A. The distribution of the similarity of the LmRSet. A solid circle denotes the functional similarity of a pair of lncRNAs in the horizontal axis and the similarity of the LmRSet in the vertical axis. The dashed line is the linear regression line generated by the least squares of the data points. B. The distribution of the similarity of the LmRSet based on the grouped lncRNA pairs. C. The distribution of the similarity of the LmiRSet. D. The distribution of the similarity of the LmiRSet based on the grouped lncRNA pairs. E. The distribution of IntNetLncSim functional similarity scores of lncRNAs based on the integrated network and random network.
Figure 2
Figure 2. The comparison of IntNetLncSim with previous similar methods
A. The correlation between LFS by IntNetLncSim and SemLncSim and the similarity of LmRSet and LmiRSet. B. The correlation between LFS by IntNetLncSim and LNCSIM and the similarity of LmRSet and LmiRSet. C. The correlation between LFS by IntNetLncSim and LFSCM and the similarity of LmRSet and LmiRSet.
Figure 3
Figure 3. ROC curve and AUC value of our method based on leave-one-out cross validation on 150 known experimentally verified lncRNA-disease associations
Figure 4
Figure 4. System overview
Figure 5
Figure 5. Overview of IntNetLncSim demonstrating the basic ideas of measuring lncRNAs functional similarity
Figure 6
Figure 6. Flowchart of predicting disease-related lncRNAs
See this image and copyright information in PMC

References

    1. Kapranov P, Willingham AT, Gingeras TR. Genome-wide transcription and the implications for genomic organization. Nat Rev Genet. 2007;8:413–423. - PubMed
    1. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921. - PubMed
    1. Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: insights into functions. Nature Reviews Genetics. 2009;10:155–159. - PubMed
    1. Khalil AM, Guttman M, Huarte M, Garber M, Raj A, Morales DR, Thomas K, Presser A, Bernstein BE, van Oudenaarden A. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proceedings of the National Academy of Sciences. 2009;106:11667–11672. - PMC - PubMed
    1. Feng J, Bi C, Clark BS, Mady R, Shah P, Kohtz JD. The Evf-2 noncoding RNA is transcribed from the Dlx-5/6 ultraconserved region and functions as a Dlx-2 transcriptional coactivator. Genes & development. 2006;20:1470–1484. - PMC - PubMed