Universität zu Köln

Habermann, Daniel ORCID: 0000-0003-3685-7287, Kharimzadeh, Hadi, Walker, Andreas, Li, Yang, Yang, Rongge, Kaiser, Rolf, Brumme, Zabrina L., Timm, Jorg, Roggendorf, Michael and Hoffmann, Daniel ORCID: 0000-0003-2973-7869 (2022). HAMdetector: a Bayesian regression model that integrates information to detect HLA-associated mutations. Bioinformatics, 38 (9). S. 2428 - 2437. OXFORD: OXFORD UNIV PRESS. ISSN 1460-2059

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Abstract

Motivation: A key process in anti-viral adaptive immunity is that the human leukocyte antigen (HLA) system presents epitopes as major histocompatibility complex I (MHC I) protein-peptide complexes on cell surfaces and in this way alerts CD8+ cytotoxic T-lymphocytes (CTLs). This pathway exerts strong selection pressure on viruses, favoring viral mutants that escape recognition by the HLA/CTL system. Naturally, such immune escape mutations often emerge in highly variable viruses, e.g. HIV or HBV, as HLA-associated mutations (HAMs), specific to the hosts MHC I proteins. The reliable identification of HAMs is not only important for understanding viral genomes and their evolution, but it also impacts the development of broadly effective anti-viral treatments and vaccines against variable viruses. By their very nature, HAMs are amenable to detection by statistical methods in paired sequence/HLA data. However, HLA alleles are very polymorphic in the human host population which makes the available data relatively sparse and noisy. Under these circumstances, one way to optimize HAM detection is to integrate all relevant information in a coherent model. Bayesian inference offers a principled approach to achieve this. Results: We present a new Bayesian regression model for the detection of HAMs that integrates a sparsity-inducing prior, epitope predictions and phylogenetic bias assessment, and that yields easily interpretable quantitative information on HAM candidates. The model predicts experimentally confirmed HAMs as having high posterior probabilities, and it performs well in comparison to state-of-the-art models for several datasets from individuals infected with HBV, HDV and HIV. Availability and implementation: The source code of this software is available at https://github.com/HAMdetector/ Escape.jl under a permissive MIT license. The data underlying this article were provided by permission. Data will be shared on request to the corresponding author with permission of the respective co-authors. Contact: daniel.habermann@uni-due.de or daniel.hoffmann@uni-due.de Supplementary information: Supplementary data are available at Bioinformatics online.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Habermann, Daniel UNSPECIFIED orcid.org/0000-0003-3685-7287 UNSPECIFIED Kharimzadeh, Hadi UNSPECIFIED UNSPECIFIED UNSPECIFIED Walker, Andreas UNSPECIFIED UNSPECIFIED UNSPECIFIED Li, Yang UNSPECIFIED UNSPECIFIED UNSPECIFIED Yang, Rongge UNSPECIFIED UNSPECIFIED UNSPECIFIED Kaiser, Rolf UNSPECIFIED UNSPECIFIED UNSPECIFIED Brumme, Zabrina L. UNSPECIFIED UNSPECIFIED UNSPECIFIED Timm, Jorg UNSPECIFIED UNSPECIFIED UNSPECIFIED Roggendorf, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoffmann, Daniel UNSPECIFIED orcid.org/0000-0003-2973-7869 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-691993
DOI:	10.1093/bioinformatics/btac134
Journal or Publication Title:	Bioinformatics
Volume:	38
Number:	9
Page Range:	S. 2428 - 2437
Date:	2022
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1460-2059
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HIV-1 GAG; ESCAPE; ADAPTATION; HORSESHOE; EVOLUTION; SELECTION; PRESSURE; EPITOPES; POLYMORPHISMS; MECHANISMS Multiple languages Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/69199