A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome

After sequencing, it is common to screen ncRNA according to expression differences. But this may lose a lot of valuable information and there is currently no indicator to characterize the regulatory function and participation degree of ncRNA on transcriptome. Based on existing pathway enrichment methods, we developed a new algorithm to calculating the participation degree of ncRNA in transcriptome (PDNT). Here we analyzed multiple data sets, and differentially expressed genes (DEGs) were used for pathway enrichment analysis. The PDNT algorithm was used to calculate the Contribution value (C value) of each ncRNA based on its target genes and the pathways they participates in. The results showed that compared with ncRNAs screened by log2 fold change (FC) and p-value, those screened by C value regulated more DEGs in IPA canonical pathways, and their target DEGs were more concentrated in the core region of the protein–protein interaction (PPI) network. The ranking of disease critical ncRNAs increased integrally after sorting with C value. Collectively, we found that the PDNT algorithm provides a measure from another view compared with the log2FC and p-value and it may provide more clues to effectively evaluate ncRNA.