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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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A network-based algorithm for the identification of moonlighting noncoding RNAs and its application in sepsis.
Xueyan Liu1, Yong Xu2, Ran Wang3
1Critical Care Medici at Shenzhen People's Hospital.
Briefings in Bioinformatics
|February 1, 2020
Summary
Moonlighting long noncoding RNAs (mlncRNAs) perform multiple cellular roles. MoonFinder v2.0 predicts human mlncRNAs and analyzes their sepsis expression, offering a new R package for researchers.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Moonlighting proteins offer functional diversity without genome expansion.
- Computational methods for predicting moonlighting proteins exist, but not for long noncoding RNAs (lncRNAs).
- Moonlighting lncRNAs (mlncRNAs) are understudied due to a lack of predictive tools.
Purpose of the Study:
- To update the MoonFinder algorithm for enhanced identification of human mlncRNAs.
- To introduce a 'moonlighting coefficient' for assessing the confidence of mlncRNA activity.
- To investigate the expression and co-expression patterns of mlncRNAs in sepsis patients.
Main Methods:
- Developed MoonFinder v2.0 with an extended framework for protein module detection and RNA-module association.
- Implemented a novel 'moonlighting coefficient' for confidence scoring.
- Analyzed mlncRNA expression and co-expression in adult sepsis patients.
Main Results:
- MoonFinder v2.0 provides a robust framework for human mlncRNA prediction.
- mlncRNAs show a tendency towards upregulation and differential expression in sepsis.
- mlncRNAs exhibit mutually exclusive co-expression patterns compared to other lncRNAs.
Conclusions:
- MoonFinder v2.0 is a valuable R package for predicting human mlncRNAs.
- The study offers the first characterization of mlncRNA expression in sepsis, aiding understanding of their roles in disease.
