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Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data.
Joanna E Handzlik1,2, Spyros Tastsoglou1,3, Ioannis S Vlachos4,5
1DIANA-Lab, Department of Electrical & Computer Engineering, University of Thessaly, Volos, 38221, Greece.
Scientific Reports
|January 22, 2020
Summary
Manatee is a new algorithm that accurately quantifies small non-coding RNA (sncRNA) expression. It improves analysis of small RNA sequencing (sRNA-Seq) data by rescuing multimapped reads and identifying novel loci.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Small non-coding RNAs (sncRNAs) are crucial regulators in biological processes and disease states.
- Next Generation Sequencing (NGS) enables comprehensive exploration of small RNA (sRNA) transcriptomes.
- Analyzing sRNA sequencing (sRNA-Seq) data presents challenges due to read mapping ambiguity and modifications.
Purpose of the Study:
- To develop an accurate algorithm, Manatee, for quantifying sRNA expression.
- To detect novel non-coding RNA loci from sRNA-Seq data.
- To improve the analysis of complex sRNA transcriptomes.
Main Methods:
- Manatee algorithm integrates prior sRNA annotations with alignment density.
- It rescues multimapped reads for comprehensive quantification.
- The approach was validated using real and simulated sRNA-Seq datasets.
Main Results:
- Manatee demonstrates high accuracy in quantifying diverse sRNA classes.
- The algorithm successfully identifies and quantifies expression from unannotated loci.
- It also detects microRNA isoforms (isomiRs).
Conclusions:
- Manatee provides accurate and comprehensive transcriptome-wide sRNA quantification.
- The algorithm enhances the discovery of novel non-coding RNAs and isoforms.
- Manatee is user-friendly and suitable for integration into existing bioinformatics pipelines.
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