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LncRNAs2Pathways: Identifying the pathways influenced by a set of lncRNAs of
interest based on a global network propagation method
#MMPMID28425476
Han J
; Liu S
; Sun Z
; Zhang Y
; Zhang F
; Zhang C
; Shang D
; Yang H
; Su F
; Xu Y
; Li C
; Ren H
; Li X
Sci Rep
2017[Apr]; 7
(?): 46566
PMID28425476
show ga
Long non-coding RNAs (lncRNAs) have been demonstrated to play essential roles in
diverse cellular processes and biological functions. Exploring the functions
associated with lncRNAs may help provide insight into their underlying biological
mechanisms. The current methods primarily focus on investigating the functions of
individual lncRNAs; however, essential biological functions may be affected by
the combinatorial effects of multiple lncRNAs. Here, we have developed a novel
computational method, LncRNAs2Pathways, to identify the functional pathways
influenced by the combinatorial effects of a set of lncRNAs of interest based on
a global network propagation algorithm. A new Kolmogorov-Smirnov-like statistical
measure weighted by the network propagation score, which considers the expression
correlation among lncRNAs and coding genes, was used to evaluate the biological
pathways influenced by the lncRNAs of interest. We have described the
LncRNAs2Pathways methodology and illustrated its effectiveness by analyzing three
lncRNA sets associated with glioma, prostate and pancreatic cancers. We further
analyzed the reproducibility and robustness and compared our results with those
of two other methods. Based on these analyses, we showed that LncRNAs2Pathways
can effectively identify the functional pathways associated with lncRNA sets.
Finally, we implemented this method as a freely available R-based tool.
free
free
free
