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A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome
Xinyi Gu1,2, Shen Wang1,2, Bo Jin1,2
1Department of Orthopedics and Traumatology, Peking University People's Hospital, Beijing, 100044, China.
Scientific Reports
|December 31, 2022
Summary
A new algorithm, Participation Degree of ncRNA in Transcriptome (PDNT), quantifies non-coding RNA regulatory roles. This method identifies critical ncRNAs missed by traditional expression analysis, offering deeper insights into transcriptome regulation.
Area of Science:
- Transcriptomics
- Bioinformatics
- Computational Biology
Background:
- Standard ncRNA screening based on expression differences (log2 fold change and p-value) may overlook valuable regulatory information.
- A quantitative indicator is needed to accurately characterize the regulatory function and participation degree of ncRNAs in the transcriptome.
Purpose of the Study:
- To develop a novel algorithm, the Participation Degree of ncRNA in Transcriptome (PDNT), for evaluating ncRNA regulatory function.
- To assess the efficacy of the PDNT algorithm in identifying functionally significant ncRNAs compared to traditional methods.
Main Methods:
- Developed the PDNT algorithm, calculating a Contribution value (C value) for each ncRNA based on its target genes and their associated pathways.
- Analyzed multiple datasets, utilizing differentially expressed genes (DEGs) for pathway enrichment analysis.
- Compared ncRNAs identified by C value with those identified by log2 fold change (FC) and p-value using IPA canonical pathways and protein-protein interaction (PPI) network analysis.
Main Results:
- ncRNAs identified by the C value regulated more DEGs within IPA canonical pathways compared to those identified by log2FC and p-value.
- Target DEGs of C value-selected ncRNAs were more concentrated in the core region of the protein-protein interaction (PPI) network.
- The ranking of disease-critical ncRNAs improved when sorted by C value, indicating enhanced identification of key regulatory molecules.
Conclusions:
- The PDNT algorithm offers a novel metric for assessing ncRNA regulatory impact, complementing traditional expression-based screening.
- PDNT provides valuable insights into ncRNA participation and function within the transcriptome, potentially uncovering critical regulatory roles missed by conventional methods.
- This algorithm may enhance the evaluation and identification of functionally significant ncRNAs for further research and therapeutic targeting.
