TY - JOUR

T1 - Reconstructing Antibody Repertoires from Error-Prone Immunosequencing Reads

AU - Shlemov, Alexander

AU - Bankevich, Sergey

AU - Bzikadze, Andrey

AU - Turchaninova, Maria A.

AU - Safonova, Yana

AU - Pevzner, Pavel A.

N1 - Reconstructing Antibody Repertoires from Error-Prone Immunosequencing Reads Alexander Shlemov, Sergey Bankevich, Andrey Bzikadze, Maria A. Turchaninova, Yana Safonova, Pavel A. Pevzner. The Journal of Immunology November 1, 2017, 199 (9) 3369-3380; DOI: 10.4049/jimmunol.1700485

PY - 2017

Y1 - 2017

N2 - Transforming error-prone immunosequencing datasets into Ab repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Although various repertoire reconstruction algorithms were released in the last 3 y, it remains unclear how to benchmark them and how to assess the accuracy of the reconstructed repertoires. We describe an accurate IgReC algorithm for constructing Ab repertoires from high-throughput immunosequencing datasets and a new framework for assessing the quality of reconstructed repertoires. Surprisingly, Ab repertoires constructed by IgReC from barcoded immunosequencing datasets in the blind mode (without using information about unique molecular identifiers) improved upon the repertoires constructed by the state-of-the-art tools that use barcoding. This finding suggests that IgReC may alleviate the need to generate repertoires using the barcoding technology (the workhorse of current immunogenomics efforts) because our computational approach to error correction of immunosequencing data is nearly as powerful as the experimental approach based on barcoding.

AB - Transforming error-prone immunosequencing datasets into Ab repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Although various repertoire reconstruction algorithms were released in the last 3 y, it remains unclear how to benchmark them and how to assess the accuracy of the reconstructed repertoires. We describe an accurate IgReC algorithm for constructing Ab repertoires from high-throughput immunosequencing datasets and a new framework for assessing the quality of reconstructed repertoires. Surprisingly, Ab repertoires constructed by IgReC from barcoded immunosequencing datasets in the blind mode (without using information about unique molecular identifiers) improved upon the repertoires constructed by the state-of-the-art tools that use barcoding. This finding suggests that IgReC may alleviate the need to generate repertoires using the barcoding technology (the workhorse of current immunogenomics efforts) because our computational approach to error correction of immunosequencing data is nearly as powerful as the experimental approach based on barcoding.

UR - https://www.jimmunol.org/content/199/9/3369

M3 - Article

VL - 199

SP - 396

EP - 397

JO - Journal of Immunotherapy

JF - Journal of Immunotherapy

SN - 1524-9557

IS - 9

ER -