MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F17%3A00100339" target="_blank" >RIV/00216224:14740/17:00100339 - isvavai.cz</a>

Výsledek na webu

<a href="http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005480&type=printable" target="_blank" >http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005480&type=printable</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pcbi.1005480" target="_blank" >10.1371/journal.pcbi.1005480</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10609 - Biochemical research methods

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PLoS Computational Biology

ISSN

1553-734X

e-ISSN

—

Svazek periodika

13

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

—

Kód UT WoS článku

000402889500008

EID výsledku v databázi Scopus

2-s2.0-85020126470