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tcR: an R package for T cell receptor repertoire advanced data analysis
VI. Nazarov, MV. Pogorelyy, EA. Komech, IV. Zvyagin, DA. Bolotin, M. Shugay, DM. Chudakov, YB. Lebedev, IZ. Mamedov,
Language English Country England, Great Britain
Document type Journal Article, Research Support, Non-U.S. Gov't
NLK
BioMedCentral
from 2000-12-01
BioMedCentral Open Access
from 2000
Directory of Open Access Journals
from 2000
Free Medical Journals
from 2000
PubMed Central
from 2000
Europe PubMed Central
from 2000
ProQuest Central
from 2009-01-01
Open Access Digital Library
from 2000-01-01
Open Access Digital Library
from 2000-01-01
Open Access Digital Library
from 2000-07-01
Medline Complete (EBSCOhost)
from 2000-01-01
Health & Medicine (ProQuest)
from 2009-01-01
ROAD: Directory of Open Access Scholarly Resources
from 2000
Springer Nature OA/Free Journals
from 2000-12-01
- MeSH
- Immunoglobulins genetics MeSH
- Humans MeSH
- Programming Languages MeSH
- Receptors, Antigen, T-Cell genetics immunology MeSH
- Sequence Analysis, DNA methods MeSH
- Software * MeSH
- High-Throughput Nucleotide Sequencing methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
BACKGROUND: The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. RESULTS: Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies. CONCLUSIONS: tcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network ( http://cran.r-project.org/mirrors.html ). The source code and development version are available at tcR GitHub ( http://imminfo.github.io/tcr/ ) along with the full documentation and typical usage examples.
References provided by Crossref.org
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- $a Nazarov, Vadim I $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. vdm.nazarov@gmail.com. National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russia. vdm.nazarov@gmail.com.
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- $a tcR: an R package for T cell receptor repertoire advanced data analysis / $c VI. Nazarov, MV. Pogorelyy, EA. Komech, IV. Zvyagin, DA. Bolotin, M. Shugay, DM. Chudakov, YB. Lebedev, IZ. Mamedov,
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- $a BACKGROUND: The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. RESULTS: Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies. CONCLUSIONS: tcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network ( http://cran.r-project.org/mirrors.html ). The source code and development version are available at tcR GitHub ( http://imminfo.github.io/tcr/ ) along with the full documentation and typical usage examples.
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- $a Pogorelyy, Mikhail V $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. m.pogorely@gmail.com.
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- $a Komech, Ekaterina A $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. ekomech@gmail.com.
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- $a Zvyagin, Ivan V $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. izvyagin@gmail.com. Central European Institute of Technology, Masaryk University, Brno, Czech Republic. izvyagin@gmail.com.
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- $a Bolotin, Dmitry A $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. bolotin.dmitriy@gmail.com.
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- $a Shugay, Mikhail $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. mikhail.shugay@gmail.com.
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- $a Chudakov, Dmitry M $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. chudakovdm@mail.ru. Central European Institute of Technology, Masaryk University, Brno, Czech Republic. chudakovdm@mail.ru.
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- $a Lebedev, Yury B $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. lebedev_yb@ibch.ru.
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- $a Mamedov, Ilgar Z $u Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, 16/10 Miklukho-Maklaya, Moscow, 117997, Russia. imamedov78@gmail.com.
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