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Bioinformatic Analysis of Natively Paired VH:VL Antibody Repertoires for Antibody Discovery.
Fahad, Ahmed S; Madan, Bharat; DeKosky, Brandon J.
Afiliación
  • Fahad AS; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
  • Madan B; Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA.
  • DeKosky BJ; The Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
Methods Mol Biol ; 2552: 447-463, 2023.
Article en En | MEDLINE | ID: mdl-36346608
ABSTRACT
Next-generation DNA sequencing (NGS) of human antibody repertoires has been extensively implemented to discover novel antibody drugs, to analyze B-cell developmental features, and to investigate antibody responses to infectious diseases and vaccination. Because the antibody repertoire encoded by human B cells is highly diverse, NGS analyses of antibody genes have provided a new window into understanding antibody responses for basic immunology, biopharmaceutical drug discovery, and immunotherapy. However, many antibody discovery protocols analyze the heavy and light chains separately due to the short-read nature of most NGS technologies, whereas paired heavy and light chain data are required for complete antibody characterization. Here, we describe a computational workflow to process millions of paired antibody heavy and light chain DNA sequence reads using the Illumina MiSeq 2x300 NGS platform. In this workflow, we describe raw NGS read processing and initial quality filtering, the annotation and assembly of antibody clonotypes relating to paired heavy and light chain antibody lineages, and the generation of complete heavy+light consensus sequences for the downstream cloning and expression of human antibody proteins.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología Computacional / Anticuerpos Límite: Humans Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Biología Computacional / Anticuerpos Límite: Humans Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos