Abstract:Analysis of antibody repertoire development and specific antibody responses important for e.g. autoimmune conditions, allergy, and protection against disease is supported by high throughput sequencing and associated bioinformatics pipelines that describe the diversity of the encoded antibody variable domains. Proper assignment of sequences to germline genes are important for many such processes, for instance in the analysis of somatic hypermutation. Germline gene inference from antibody-encoding transcriptomes… Show more
“…Germline gene inference was performed using TIgGER [6] and IgDiscover [7]. Additional bioinformatics analysis was performed as outlined elsewhere [1] including analysis performed using GIgGle (release 0.2) that is available under Apache License at https://github.com/ukirik/giggle. Immunoglobulin gene names and sequence numbering complies with the nomenclature defined by the International ImMunoGeneTics information system® (IMGT) (http://www.imgt.org) [8], [9].…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…2 in Ref. [1]) and peripheral blood (PB). † defines that the name of only one of a set of different alleles of the gene that cannot be differentiated by the analysis approach is shown.Fig.…”
Data that defines IGHV (immunoglobulin heavy chain variable) germline gene inference using sequences of IgM-encoding transcriptomes obtained by Illumina MiSeq sequencing technology are described. Such inference is used to establish personalized germline gene sets for in-depth antibody repertoire studies and to detect new antibody germline genes from widely available immunoglobulin-encoding transcriptome data sets. Specifically, the data has been used to validate (Parallel antibody germline gene and haplotype analyses support the validity of immunoglobulin germline gene inference and discovery (DOI: 10.1016/j.molimm.2017.03.012) (Kirik et al., 2017) [1]) the inference process. This was accomplished based on analysis of the inferred germline genes’ association to the donors’ different haplotypes as defined by their different, expressed IGHJ alleles and/or IGHD genes/alleles. The data is important for development of validated germline gene databases containing entries inferred from immunoglobulin-encoding transcriptome sequencing data sets, and for generation of valid, personalized antibody germline gene repertoires.
“…Germline gene inference was performed using TIgGER [6] and IgDiscover [7]. Additional bioinformatics analysis was performed as outlined elsewhere [1] including analysis performed using GIgGle (release 0.2) that is available under Apache License at https://github.com/ukirik/giggle. Immunoglobulin gene names and sequence numbering complies with the nomenclature defined by the International ImMunoGeneTics information system® (IMGT) (http://www.imgt.org) [8], [9].…”
Section: Experimental Design Materials and Methodsmentioning
confidence: 99%
“…2 in Ref. [1]) and peripheral blood (PB). † defines that the name of only one of a set of different alleles of the gene that cannot be differentiated by the analysis approach is shown.Fig.…”
Data that defines IGHV (immunoglobulin heavy chain variable) germline gene inference using sequences of IgM-encoding transcriptomes obtained by Illumina MiSeq sequencing technology are described. Such inference is used to establish personalized germline gene sets for in-depth antibody repertoire studies and to detect new antibody germline genes from widely available immunoglobulin-encoding transcriptome data sets. Specifically, the data has been used to validate (Parallel antibody germline gene and haplotype analyses support the validity of immunoglobulin germline gene inference and discovery (DOI: 10.1016/j.molimm.2017.03.012) (Kirik et al., 2017) [1]) the inference process. This was accomplished based on analysis of the inferred germline genes’ association to the donors’ different haplotypes as defined by their different, expressed IGHJ alleles and/or IGHD genes/alleles. The data is important for development of validated germline gene databases containing entries inferred from immunoglobulin-encoding transcriptome sequencing data sets, and for generation of valid, personalized antibody germline gene repertoires.
“…Haplotype inference has been shown to be a useful tool for cutting down germline sets from such a candidate pool. 5,51 This works by assuming limited or no gene duplication of individual genes in a germline set, and then using joint gene usage under VDJ recombination to infer which alleles lie on which haplotype. One can then review the gene assignments and reject suspicious inferences, since having many alleles of a given gene inferred to lie on a single haplotype casts doubt on their authenticity.…”
Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given datasets. This procedure is well developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper, we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency but also discrete objects such as B-cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective, the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B-cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development.
“…Because of the difficulty in performing physical sequencing of these loci, several computational tools have been developed for haplotype inference from AIRR-seq data (Kidd et al, 2012;Kirik et al, 2017). Haplotyping can be computationally inferred from antibody repertoire sequencing data, using a heterozygous V/D/J gene as an "anchor" to define the chromosomes.…”
Section: Introductionmentioning
confidence: 99%
“…Haplotyping can be computationally inferred from antibody repertoire sequencing data, using a heterozygous V/D/J gene as an "anchor" to define the chromosomes. So far, statistical framework for haplotyping has been developed for J6 (Kidd et al, 2012;Kirik et al, 2017), which is heterozygous in ∼ 30% of people (alleles J6*02 and *03 ). Here, we show that reliable haplotyping can also be performed using D or V genes as anchors.…”
Analysis of antibody repertoires by high throughput sequencing is of major importance in understanding adaptive immune responses. Our knowledge of variations in the genomic loci encoding antibody genes is incomplete, mostly due to technical difficulties in aligning short reads to these highly repetitive loci. The partial knowledge results in conflicting V-D-J gene assignments between different algorithms, and biased genotype and haplotype inference. Previous studies have shown that haplotypes can be inferred by taking advantage of IGHJ6 heterozygosity, which is observed in approximately one third of the population. Here, we propose a robust novel method for determining V-D-J haplotypes by adapting a Bayesian framework. In addition to IGHJ-based inference, our method extends haplotype inference also to IGHD-and IGHV -based, in heterozygous individuals. We can thus infer complex genetic events like deletions and copy number variations in a much larger fraction of the population. We tested our method on the largest data set, to date, of naïve B-cell repertoires. We present evidence for allele usage bias, as well as a mosaic deletion pattern of IGHD and IGHV genes across the population. The inferred haplotypes and deletion patterns may have clinical implications for genetic predispositions to diseases. Our findings greatly expand the knowledge that can be extracted from antibody repertoire sequencing data.
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