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PLoS Comput Biol. 2017 May 5;13(5):e1005480. doi: 10.1371/journal.pcbi.1005480. eCollection 2017 May.

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing.

Author information

1
Shemyakin-Ovchinnikov Institute of bioorganic chemistry RAS, Miklukho-Maklaya 16/10, Moscow, Russia.
2
Pirogov Russian National Research Medical University, Ostrovityanova 1, Moscow, Russia.
3
Central European Institute of Technology, Masaryk University, Brno, Czech republic.
4
Evrogen JSC, Miklukho-Maklaya 16/10, Moscow, Russia.
5
Skolkovo Institute of Science and Technology, Nobel 3, Moscow, Russia.

Abstract

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.

PMID:
28475621
PMCID:
PMC5419444
DOI:
10.1371/journal.pcbi.1005480
[Indexed for MEDLINE]
Free PMC Article

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