VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires

Mikhail Shugay, Dmitriy V Bagaev, Maria A Turchaninova, Dmitriy A Bolotin, Olga V Britanova, Ekaterina V Putintseva, Mikhail V Pogorelyy, Vadim I Nazarov, Ivan V Zvyagin, Vitalina I Kirgizova, Kirill I Kirgizov, Elena V Skorobogatova and Dmitriy M Chudakov

PLOS Computational Biology, 2015, vol. 11, issue 11, 1-16

Abstract: Despite the growing number of immune repertoire sequencing studies, the field still lacks software for analysis and comprehension of this high-dimensional data. Here we report VDJtools, a complementary software suite that solves a wide range of T cell receptor (TCR) repertoires post-analysis tasks, provides a detailed tabular output and publication-ready graphics, and is built on top of a flexible API. Using TCR datasets for a large cohort of unrelated healthy donors, twins, and multiple sclerosis patients we demonstrate that VDJtools greatly facilitates the analysis and leads to sound biological conclusions. VDJtools software and documentation are available at https://github.com/mikessh/vdjtools.Author Summary: High-throughput profiling of T- and B-cell antigen receptor repertoires promises great advances in our understanding of the mechanisms underlying adaptive immune system function, treatment of autoimmune and infectious diseases, and development of novel approaches in cancer immunotherapy. A number of recently developed software tools aim at processing immune repertoire data by mapping Variable (V), Diversity (D) and Joining (J) antigen receptor segments to sequencing reads and assembling T- and B-cell clonotypes. Nevertheless, there still exists a major gap in common methods of data post-analysis in the field: there is no standardized data format so far, and most of data comparative analysis is carried out using a variety of in-house scripts. Here we present VDJtools, a software framework that can analyze output of most commonly used TCR repertoire processing tools and allows applying a diverse set of post-analysis strategies. The main aims of our framework are: To ensure consistency of post-analysis methods and reproducibility of obtained results; to save the time of bioinformaticians analyzing TCR repertoire data by providing comprehensive tabular output and open-source API; and to provide a simple enough command line tool so that immunologists and biologists with little computational background could use it to generate publication-ready results.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004503 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 04503&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004503

DOI: 10.1371/journal.pcbi.1004503

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().