Immunoglobulin Classification Using the Colored Antibody Graph

Bonissone, Stefano; Pevzner, Pavel A.

doi:10.1007/978-3-319-16706-0_7

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…If we store whether each edge occurs in the first string, the second string, or both, then the result is a coloured de Bruijn graph. Coloured de Bruijn graphs were introduced by Iqbal et al [9] for detecting variations between individuals' genomes, and are now also used in other areas of genomics (see, e.g., [2]). We can view the coloured de Bruijn graph as the union of each graph consisting of edges of the same colour.…”

Section: A De Bruijn Graphsmentioning

confidence: 99%

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Boucher

Bowe

Gagie

et al. 2015

String Processing and Information Retrieval

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Section: A De Bruijn Graphsmentioning

confidence: 99%

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Boucher

Bowe

Gagie

et al. 2015

String Processing and Information Retrieval

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“…Processed raw sequences were collapsed and error corrected using IgRepertoireConstructor [31]. Repertoire sequences were VJ labeled using alignment [4] to rabbit gene references from IMGT. VJ labeling reports complementarity-determining regions (CDRs), and sequence differences from gene references (i.e., somatic hypermutation events).…”

Section: Informaticsmentioning

confidence: 99%

Serum proteomics expands on high-affinity antibodies in immunized rabbits than deep B-cell repertoire sequencing alone

Bonissone

Lima

K³

et al. 2019

Preprint

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Rabbits are a model for immunology studies, and monoclonal antibodies developed from rabbits have been highly sought after to empower immunoassays in a variety of other applications. Highthroughput characterization of circulating serum antibodies in response to specific antigens is highly impactful for both immunology studies and antibody development. A combination of high throughput sequencing of antibody transcripts from B cells and proteomic analysis of serum antibodies, referred to as immunoproteogenomics, is applied to profile the immune response of rabbits to β-galactosidase (Beta-gal) in both recombinant antigen and peptide antigen immunization formats. The use of recombinant antigen resulted in observing 56.3% more heavy chains clones in serum than immunization with peptide antigens. Additionally, sampling peripheral blood mononuclear cells (PBMCs) for B-cell repertoire sequencing at different time points throughout the immunization was found to capture 47.8%-72.8% of total proteomically observed heavy chain clones, and would serve well in replacing sequencing the B cell rich, but more difficult to access spleen or bone marrow compartments. Despite B-cell repertoire sequencing to depths of 2M to 10M reads, we found proteomic evidence supporting at least 10% of serum antibodies are still missed. Further improvements to proteomic analysis techniques would enable more precise characterization of antibodies circulating in serum and better estimates of antibodies missed by repertoire sequencing.

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“…De Bruijn graphs are a key algorithmic technique in genome assembly [23,9,12,56,61,6]. In addition, de Bruijn graphs have been used for Sequencing by Hybridization [43], repeat classification [45], de novo protein sequencing [4,5,21], synteny block construction [38,46], multiple sequence alignment [48], genotyping [25], and immunoglobulin classification [13]. A-Bruijn graphs are even more general than de Bruijn graphs, e.g., they include breakpoint graphs, the workhorse of genome-rearrangement studies [42,35].…”

Section: Introductionmentioning

confidence: 99%

Assembly of Long Error-Prone Reads Using de Bruijn Graphs

Lin

Yuan

Kolmogorov

et al. 2016

Preprint

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The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.de Bruijn graph | genome assembly | single-molecule sequencing T he key challenge to the success of single-molecule sequencing (SMS) technologies lies in the development of algorithms for assembling genomes from long but inaccurate reads. The pioneer in long reads technologies, Pacific Biosciences, now produces accurate assemblies from long error-prone reads (1, 2). Goodwin et al. (3) and Loman et al. (4) demonstrated that high-quality assemblies can be obtained from even less-accurate Oxford Nanopore reads. Advances in assembly of long errorprone reads recently resulted in the accurate reconstructions of various genomes (5-10). However, as illustrated in Booher et al. (11), the problem of assembling long error-prone reads is far from being resolved even in the case of relatively small bacterial genomes.Previous studies of SMS assemblies were based on the overlaplayout-consensus (OLC) approach (12) or a similar string graph approach (13), which require an all-against-all comparison of reads (14) and remain computationally challenging (see refs. 15-17 for a discussion of the pros and cons of this approach). Moreover, there is an assumption that the de Bruijn graph approach, which has dominated genome assembly for the last decade, is inapplicable to long reads. This is a misunderstanding, because the de Bruijn graph approach, as well as its variation called the A-Bruijn graph approach, was developed to assemble rather long Sanger reads (18). There is also a misunderstanding that the de Bruijn graph approach can only assemble highly accurate reads and fails when assembling long error-prone reads. Although this is true for the original de Bruijn graph approach to assembly (15-17), the A-Bruijn graph approach was originally designed to assemble inaccurate reads as long as any similarities between reads can be reliably identified. Moreover, A-Bruijn graphs have proven to be useful even for assembling mass spectra, which represent highly inaccurate fingerprints of amino acid sequences of peptides (19,20). However, although A-Bruijn graphs have proven to be useful in assembling Sanger reads and mass spectra, the question of how to apply A-Bruijn graphs for assembling long error-prone reads remains open.de Bruijn graphs are a key algorithmic technique in genome assembly (15,(21)(22)(23)(24). In addition, de Bruijn graphs have been used for sequencing by hybridization (...

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Immunoglobulin Classification Using the Colored Antibody Graph

Cited by 8 publications

References 23 publications

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Serum proteomics expands on high-affinity antibodies in immunized rabbits than deep B-cell repertoire sequencing alone

Assembly of Long Error-Prone Reads Using de Bruijn Graphs

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