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MAGERI: Computational pipeline for molecular-barcoded targeted resequencing
M. Shugay, AR. Zaretsky, DA. Shagin, IA. Shagina, IA. Volchenkov, AA. Shelenkov, MY. Lebedin, DV. Bagaev, S. Lukyanov, DM. Chudakov,
Language English Country United States
Document type Journal Article
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- MeSH
- Databases, Genetic MeSH
- Humans MeSH
- Biomarkers, Tumor blood genetics MeSH
- Neoplasms genetics MeSH
- RNA, Viral genetics MeSH
- Sequence Analysis, DNA methods MeSH
- Sequence Analysis, RNA methods MeSH
- Software * MeSH
- Computational Biology methods MeSH
- High-Throughput Nucleotide Sequencing methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Unique molecular identifiers (UMIs) show outstanding performance in targeted high-throughput resequencing, being the most promising approach for the accurate identification of rare variants in complex DNA samples. This approach has application in multiple areas, including cancer diagnostics, thus demanding dedicated software and algorithms. Here we introduce MAGERI, a computational pipeline that efficiently handles all caveats of UMI-based analysis to obtain high-fidelity mutation profiles and call ultra-rare variants. Using an extensive set of benchmark datasets including gold-standard biological samples with known variant frequencies, cell-free DNA from tumor patient blood samples and publicly available UMI-encoded datasets we demonstrate that our method is both robust and efficient in calling rare variants. The versatility of our software is supported by accurate results obtained for both tumor DNA and viral RNA samples in datasets prepared using three different UMI-based protocols.
References provided by Crossref.org
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