Quantifying selection in high-throughput Immunoglobulin sequencing data sets.
Nucleic Acids Res
; 40(17): e134, 2012 Sep 01.
Article
em En
| MEDLINE
| ID: mdl-22641856
ABSTRACT
High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imunoglobulinas
/
Análise Mutacional de DNA
/
Genes de Imunoglobulinas
/
Sequenciamento de Nucleotídeos em Larga Escala
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
Estados Unidos