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Massively scalable genetic analysis of antibody repertoires

Bryan Briney, Dennis R. Burton
doi: https://doi.org/10.1101/447813
Bryan Briney
1Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA.
2International AIDS Vaccine Initiative Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA 92037, USA
3Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery, The Scripps Research Institute, La Jolla, CA 92037, USA
4Center for Viral Systems Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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  • For correspondence: briney@scripps.edu burton@scripps.edu
Dennis R. Burton
1Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA 92037, USA.
2International AIDS Vaccine Initiative Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA 92037, USA
3Center for HIV/AIDS Vaccine Immunology and Immunogen Discovery, The Scripps Research Institute, La Jolla, CA 92037, USA
5Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Boston, MA 02142, USA
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  • For correspondence: briney@scripps.edu burton@scripps.edu
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Abstract

With technical breakthroughs in the throughput and read-length of next-generation sequencing platforms, antibody repertoire sequencing is becoming an increasingly important tool for detailed characterization of the immune response. There is a need for open, scalable software for the genetic analysis of repertoire-scale antibody sequence data. To address this gap, we have developed the ab[x] package of software tools. There are three core components of the ab[x] toolkit, all of which are freely available: abcloud (github.com/briney/abcloud) for deployment and management of computational resources on Amazon’s Elastic Compute Cloud; abstar (github.com/briney/abstar) for pre-processing, germline gene assignment and primary annotation of antibody sequence data; and abutils (github.com/briney/abutils), which provides utilities for interactive downstream analysis of antibody repertoire data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 19, 2018.
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Massively scalable genetic analysis of antibody repertoires
Bryan Briney, Dennis R. Burton
bioRxiv 447813; doi: https://doi.org/10.1101/447813
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Massively scalable genetic analysis of antibody repertoires
Bryan Briney, Dennis R. Burton
bioRxiv 447813; doi: https://doi.org/10.1101/447813

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