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Per-sample immunoglobulin germline inference from B cell receptor deep sequencing data
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 15, Iss 7, p e1007133 (2019)
- Publication Year :
- 2019
- Publisher :
- Public Library of Science, 2019.
-
Abstract
- The collection of immunoglobulin genes in an individual’s germline, which gives rise to B cell receptors via recombination, is known to vary significantly across individuals. In humans, for example, each individual has only a fraction of the several hundred known V alleles. Furthermore, the currently-accepted set of known V alleles is both incomplete (particularly for non-European samples), and contains a significant number of spurious alleles. The resulting uncertainty as to which immunoglobulin alleles are present in any given sample results in inaccurate B cell receptor sequence annotations, and in particular inaccurate inferred naive ancestors. In this paper we first show that the currently widespread practice of aligning each sequence to its closest match in the full set of IMGT alleles results in a very large number of spurious alleles that are not in the sample’s true set of germline V alleles. We then describe a new method for inferring each individual’s germline gene set from deep sequencing data, and show that it improves upon existing methods by making a detailed comparison on a variety of simulated and real data samples. This new method has been integrated into the partis annotation and clonal family inference package, available at https://github.com/psathyrella/partis, and is run by default without affecting overall run time.<br />Author summary Antibodies are an important component of the adaptive immune system, which itself determines our response to both pathogens and vaccines. They are produced by B cells through somatic recombination of germline DNA, which results in a vast diversity of antigen binding affinities across the B cell repertoire. We typically learn about the development of this repertoire, and its history of interaction with antigens, by sequencing large numbers of the DNA sequences from which antibodies are derived. In order to understand such data, it is necessary to determine the combination of germline V, D, and J genes that was rearranged to form each such B cell receptor sequence. This is difficult, however, because the immunoglobulin locus exhibits an extraordinary level of diversity across individuals—encompassing both allelic variation and gene duplication, deletion, and conversion—and because the locus’s large size and repetitive structure make germline sequencing very difficult. In this paper we describe a new computational method that avoids this difficulty by inferring each individual’s set of immunoglobulin germline genes directly from expressed B cell receptor sequence data.
- Subjects :
- 0301 basic medicine
Physiology
Inference
Biochemistry
Germline
Database and Informatics Methods
0302 clinical medicine
Immune Physiology
Databases, Genetic
Medicine and Health Sciences
Biology (General)
Data Management
Genetics
0303 health sciences
Immune System Proteins
Ecology
Genes, Immunoglobulin
High-Throughput Nucleotide Sequencing
Phylogenetic Analysis
3. Good health
Phylogenetics
Computational Theory and Mathematics
Modeling and Simulation
Mutation (genetic algorithm)
Sequence Analysis
Research Article
Computer and Information Sciences
QH301-705.5
Bioinformatics
B-cell receptor
Immunology
Sequence Databases
Receptors, Antigen, B-Cell
Sequence alignment
Computational biology
Biology
Research and Analysis Methods
Deep sequencing
Antibodies
Set (abstract data type)
03 medical and health sciences
Cellular and Molecular Neuroscience
Sequence Motif Analysis
Point Mutation
Humans
Evolutionary Systematics
Computer Simulation
Allele
Quantitative Biology - Populations and Evolution
Molecular Biology
Gene
Ecology, Evolution, Behavior and Systematics
Alleles
030304 developmental biology
Sequence (medicine)
Taxonomy
Evolutionary Biology
Models, Genetic
Populations and Evolution (q-bio.PE)
Models, Immunological
Biology and Life Sciences
Proteins
Computational Biology
030104 developmental biology
Biological Databases
Germ Cells
Genetic Loci
FOS: Biological sciences
Mutation
Sequence Alignment
030217 neurology & neurosurgery
Software
030215 immunology
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 15
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....17b8ce3b6e85316031d1fa7d05f08925