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A Streamlined Approach to Antibody Novel Germline Allele Prediction and Validation

Overview of attention for article published in Frontiers in immunology, September 2017
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Title
A Streamlined Approach to Antibody Novel Germline Allele Prediction and Validation
Published in
Frontiers in immunology, September 2017
DOI 10.3389/fimmu.2017.01072
Pubmed ID
Authors

Ben S. Wendel, Chenfeng He, Peter D. Crompton, Susan K. Pierce, Ning Jiang

Abstract

Advancements in high-throughput sequencing and molecular identifier-based error correction have opened the door to antibody repertoire sequencing with single mutation precision, increasing both the breadth and depth of immune response characterization. However, improvements in sequencing technology cannot resolve one key aspect of antibody repertoire sequencing accuracy: the possibility of undocumented novel germline alleles. Somatic hypermutation (SHM) calling requires a reference germline sequence, and the antibody variable region gene alleles collected by the IMGT database, although large in number, are not comprehensive. Mismatches, resulted from single nucleotide polymorphisms or other genetic variation, between the true germline sequence and the closest IMGT allele can inflate SHM counts, leading to inaccurate antibody repertoire analysis. Here, we developed a streamlined approach to novel allele prediction and validation using bulk PBMC antibody repertoire sequencing data and targeted genomic DNA amplification and sequencing using PBMCs from only 4 ml of blood to quickly and effectively improve the fidelity of antibody repertoire analysis. This approach establishes a framework for comprehensively annotating novel alleles using a small amount of blood sample, which is extremely useful in studying young children's immune systems.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 30%
Student > Master 5 17%
Student > Ph. D. Student 4 13%
Professor > Associate Professor 3 10%
Professor 2 7%
Other 3 10%
Unknown 4 13%
Readers by discipline Count As %
Immunology and Microbiology 9 30%
Agricultural and Biological Sciences 7 23%
Biochemistry, Genetics and Molecular Biology 3 10%
Medicine and Dentistry 2 7%
Chemical Engineering 1 3%
Other 1 3%
Unknown 7 23%