Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies

[2112.07953] Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Genomics arXiv:2112.07953 (q-bio) [Submitted on 15 Dec 2021 ( v1 ), last revised 4 Apr 2022 (this version, v2)] Title: Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies Authors: Cosimo Lupo , Natanael Spisak , Aleksandra M. Walczak , Thierry Mora View a PDF of the paper titled Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies, by Cosimo Lupo and 3 other authors View PDF HTML (experimental) Abstract: Affinity maturation is crucial for improving the binding affinity of antibodies to antigens. This process is mainly driven by point substitutions caused by somatic hypermutations of the immunoglobulin gene. It also includes deletions and insertions of genomic material known as indels. While the landscape of point substitutions has been extensively studied, a detailed statistical description of indels is still lacking. Here we present a probabilistic inference tool to learn the statistics of indels from repertoire sequencing data, which overcomes the pitfalls and biases of standard annotation methods. The model includes antibody-specific maturation ages to account for variable mutational loads in the repertoire. After validation on synthetic data, we applied our tool to a large dataset of human immunoglobulin heavy chains. The inferred model allows us to identify universal statistical features of indels in heavy chains. We report distinct insertion and deletion hotspots, and show that the distribution of lengths of indels follows a geometric distribution, which puts constraints on future mechanistic models of the hypermutation process. Subjects: Genomics (q-bio.GN) Cite as: arXiv:2112.07953 [q-bio.GN] (or arXiv:2112.07953v2 [q-bio.GN] for this version) https://doi.org/10.48550/arXiv.2112.07953 Focus to learn more arXiv-issued DOI via DataCite Journal reference: PLoS Computational Biology 18, e1010167 (2022) Related DOI : https://doi.org/10.1371/journal.pcbi.1010167 Focus to learn more DOI(s) linking to related resources Submission history From: Thierry Mora [ view email ] [v1] Wed, 15 Dec 2021 08:24:58 UTC (2,148 KB) [v2] Mon, 4 Apr 2022 07:32:35 UTC (2,620 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning the statistics and landscape of somatic mutation-induced insertions and deletions in antibodies, by Cosimo Lupo and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.GN < prev | next > new | recent | 2021-12 Change to browse by: q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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