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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 met...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805457/ https://www.ncbi.nlm.nih.gov/pubmed/36587048 http://dx.doi.org/10.1038/s41598-022-27178-8 |
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author | Gu, Xinyi Wang, Shen Jin, Bo Qi, Zhidan Deng, Jin Huang, Chen Yin, Xiaofeng |
author_facet | Gu, Xinyi Wang, Shen Jin, Bo Qi, Zhidan Deng, Jin Huang, Chen Yin, Xiaofeng |
author_sort | Gu, Xinyi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9805457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98054572023-01-02 A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome Gu, Xinyi Wang, Shen Jin, Bo Qi, Zhidan Deng, Jin Huang, Chen Yin, Xiaofeng Sci Rep Article 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. Nature Publishing Group UK 2022-12-31 /pmc/articles/PMC9805457/ /pubmed/36587048 http://dx.doi.org/10.1038/s41598-022-27178-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gu, Xinyi Wang, Shen Jin, Bo Qi, Zhidan Deng, Jin Huang, Chen Yin, Xiaofeng A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title | A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title_full | A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title_fullStr | A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title_full_unstemmed | A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title_short | A pathway analysis-based algorithm for calculating the participation degree of ncRNA in transcriptome |
title_sort | pathway analysis-based algorithm for calculating the participation degree of ncrna in transcriptome |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805457/ https://www.ncbi.nlm.nih.gov/pubmed/36587048 http://dx.doi.org/10.1038/s41598-022-27178-8 |
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