MAGERI: Computational pipeline for molecular-barcoded targeted
resequencing

J 2017

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

SHUGAY, Mikhail, A.R. ZARETSKY, D.A. SHAGIN, I.A. SHAGINA, I.A. VOLCHENKOV et. al.

Základní údaje

Originální název

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

Autoři

SHUGAY, Mikhail (643 Rusko, domácí), A.R. ZARETSKY (643 Rusko), D.A. SHAGIN (643 Rusko), I.A. SHAGINA (643 Rusko), I.A. VOLCHENKOV (643 Rusko), A.A. SHELENKOV (643 Rusko), Mikhail LEBEDIN (643 Rusko, domácí), D.V. BAGAEV (643 Rusko), S. LUKYANOV (643 Rusko) a Dmitriy CHUDAKOV (643 Rusko, garant, domácí)

Vydání

PLoS Computational Biology, SAN FRANCISCO, PUBLIC LIBRARY SCIENCE, 2017, 1553-734X

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10609 Biochemical research methods

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.955

Kód RIV

RIV/00216224:14740/17:00100339

Organizační jednotka

Středoevropský technologický institut

DOI

http://dx.doi.org/10.1371/journal.pcbi.1005480

UT WoS

000402889500008

Klíčová slova anglicky

CIRCULATING TUMOR DNA; THERAPEUTIC RESPONSE; IMMUNE REPERTOIRES; COLORECTAL-CANCER; SOMATIC MUTATION; SEQUENCING ERROR; RARE MUTATIONS; SOLID TUMORS; GENOME; PLASMA

Štítky

OA, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 3. 2018 14:07, Mgr. Pavla Foltynová, Ph.D.

Anotace

V originále

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.

Návaznosti

LQ1601, projekt VaV

Název: CEITEC 2020 (Akronym: CEITEC2020)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, CEITEC 2020

633592, interní kód MU

Název: APERIM - Advanced bioinformatics platform for PERsonalised cancer IMmunotherapy (Akronym: APERIM)

Investor: Evropská unie, APERIM - Advanced bioinformatics platform for PERsonalised cancer IMmunotherapy, Health, demographic change and wellbeing (Societal Challenges)

Citovat

SHUGAY, Mikhail, A.R. ZARETSKY, D.A. SHAGIN, I.A. SHAGINA, I.A. VOLCHENKOV, A.A. SHELENKOV, Mikhail LEBEDIN, D.V. BAGAEV, S. LUKYANOV a Dmitriy CHUDAKOV. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. PLoS Computational Biology. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2017, roč. 13, č. 5, s. nestránkováno, 17 s. ISSN 1553-734X. Dostupné z: https://dx.doi.org/10.1371/journal.pcbi.1005480.

@article{1411034,
   author = {Shugay, Mikhail and Zaretsky, A.R. and Shagin, D.A. and Shagina, I.A. and Volchenkov, I.A. and Shelenkov, A.A. and Lebedin, Mikhail and Bagaev, D.V. and Lukyanov, S. and Chudakov, Dmitriy},
   article_location = {SAN FRANCISCO},
   article_number = {5},
   doi = {http://dx.doi.org/10.1371/journal.pcbi.1005480},
   keywords = {CIRCULATING TUMOR DNA; THERAPEUTIC RESPONSE; IMMUNE REPERTOIRES; COLORECTAL-CANCER; SOMATIC MUTATION; SEQUENCING ERROR; RARE MUTATIONS; SOLID TUMORS; GENOME; PLASMA},
   language = {eng},
   issn = {1553-734X},
   journal = {PLoS Computational Biology},
   title = {MAGERI: Computational pipeline for molecular-barcoded targeted resequencing},
   url = {http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005480&type=printable},
   volume = {13},
   year = {2017}
}

TY  - JOUR
ID  - 1411034
AU  - Shugay, Mikhail - Zaretsky, A.R. - Shagin, D.A. - Shagina, I.A. - Volchenkov, I.A. - Shelenkov, A.A. - Lebedin, Mikhail - Bagaev, D.V. - Lukyanov, S. - Chudakov, Dmitriy
PY  - 2017
TI  - MAGERI: Computational pipeline for molecular-barcoded targeted resequencing
JF  - PLoS Computational Biology
VL  - 13
IS  - 5
SP  - nestránkováno
EP  - nestránkováno
PB  - PUBLIC LIBRARY SCIENCE
SN  - 1553734X
KW  - CIRCULATING TUMOR DNA
KW  - THERAPEUTIC RESPONSE
KW  - IMMUNE REPERTOIRES
KW  - COLORECTAL-CANCER
KW  - SOMATIC MUTATION
KW  - SEQUENCING ERROR
KW  - RARE MUTATIONS
KW  - SOLID TUMORS
KW  - GENOME
KW  - PLASMA
UR  - http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005480&type=printable
L2  - http://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005480&type=printable
N2  - 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.
ER  -

SHUGAY, Mikhail, A.R. ZARETSKY, D.A. SHAGIN, I.A. SHAGINA, I.A. VOLCHENKOV, A.A. SHELENKOV, Mikhail LEBEDIN, D.V. BAGAEV, S. LUKYANOV a Dmitriy CHUDAKOV. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. \textit{PLoS Computational Biology}. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2017, roč.~13, č.~5, s.~nestránkováno, 17 s. ISSN~1553-734X. Dostupné z: https://dx.doi.org/10.1371/journal.pcbi.1005480.

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