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.

Basic information

Original name

MAGERI: Computational pipeline for molecular-barcoded targeted resequencing

Authors

SHUGAY, Mikhail (643 Russian Federation, belonging to the institution), A.R. ZARETSKY (643 Russian Federation), D.A. SHAGIN (643 Russian Federation), I.A. SHAGINA (643 Russian Federation), I.A. VOLCHENKOV (643 Russian Federation), A.A. SHELENKOV (643 Russian Federation), Mikhail LEBEDIN (643 Russian Federation, belonging to the institution), D.V. BAGAEV (643 Russian Federation), S. LUKYANOV (643 Russian Federation) and Dmitriy CHUDAKOV (643 Russian Federation, guarantor, belonging to the institution)

Edition

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

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10609 Biochemical research methods

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.955

RIV identification code

RIV/00216224:14740/17:00100339

Organization unit

Central European Institute of Technology

DOI

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

UT WoS

000402889500008

Keywords in English

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

Abstract

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.

Links

LQ1601, research and development project

Name: CEITEC 2020 (Acronym: CEITEC2020)

Investor: Ministry of Education, Youth and Sports of the CR

633592, interní kód MU

Name: APERIM - Advanced bioinformatics platform for PERsonalised cancer IMmunotherapy (Acronym: APERIM)

Investor: European Union, 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 and Dmitriy CHUDAKOV. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. PLoS Computational Biology. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2017, vol. 13, No 5, p. nestránkováno, 17 pp. ISSN 1553-734X. Available from: 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 and Dmitriy CHUDAKOV. MAGERI: Computational pipeline for molecular-barcoded targeted resequencing. \textit{PLoS Computational Biology}. SAN FRANCISCO: PUBLIC LIBRARY SCIENCE, 2017, vol.~13, No~5, p.~nestránkováno, 17 pp. ISSN~1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1005480.

Detailed Information on Publication Record