Predicting active enhancers with DNA methylation and histone modification.
CAGE-seq
DNA methylation
Enhancer RNAs
H3K27ac
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
02 Nov 2023
02 Nov 2023
Historique:
received:
23
08
2023
accepted:
27
10
2023
medline:
6
11
2023
pubmed:
3
11
2023
entrez:
3
11
2023
Statut:
epublish
Résumé
Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance. In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction. Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.
Sections du résumé
BACKGROUND
BACKGROUND
Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance.
RESULTS
RESULTS
In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction.
CONCLUSIONS
CONCLUSIONS
Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.
Identifiants
pubmed: 37919681
doi: 10.1186/s12859-023-05547-y
pii: 10.1186/s12859-023-05547-y
pmc: PMC10621108
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
414Subventions
Organisme : Postdoctoral Science Foundation of China
ID : 2022M720662
Organisme : National Natural Science Foundation of China
ID : 62202315
Organisme : National Natural Science Foundation of China
ID : 62250028
Organisme : National Natural Science Foundation of China
ID : 62271329
Organisme : Shenzhen Polytechnic Research Fund
ID : 6022330002K
Organisme : Sichuan Provincial Science Fund for Distinguished Young Scholars
ID : 2021JDJQ0025
Organisme : Municipal Government of Quzhou
ID : 2022D040
Informations de copyright
© 2023. The Author(s).
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