B cell receptor (BCR) repertoire analysis has grown tremendously in the last 10 years, transforming our understanding disease and enabling the discovery of novel therapeutics. We at Alchemab love the huge growth in this space and have curated a list of papers across several areas. This is not an exhaustive list, so please let us know if we've missed your favourite paper.

We intend this to be a growing list, so if you'd like to contribute, or suggest any new categories, feel free to submit a pull request.

• Reviews 📚
• BCR repertoire datasets 🧬
• Machine learning 🤖
• Clonotyping Methods 🚀
• Structural annotations 🔮

• Pertseva et al., Applications of Machine and Deep Learning in Adaptive Immunity. Annu Rev Chem Biomol Eng (2021).
• Raybould et al., Current strategies for detecting functional convergence across B-cell receptor repertoires. mAbs (2021).
• Snapkov et al., Progress and challenges in mass spectrometry-based analysis of antibody repertoires. Trends Biotechnol (2021).
• Cspregi et al., Immune Literacy: Reading, Writing, and Editing Adaptive Immunity. iScience (2020).
• Greiff et al., Mining adaptive immune receptor repertoires for biological and clinical information using machine learning. Curr Opin Sys Biol (2020).
• Marks and Deane, How repertoire data are changing antibody science. J Biol Chem (2020).
• Rees, Understanding the human antibody repertoire. mAbs, (2020).
• Teraguchi et al., Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J (2020).
• Brown et al., Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires. Mol Syst Des Eng (2019).
• Davis and Boyd, Recent progress in the analysis of αβT cell and B cell receptor repertoires. Curr Opin Immunol (2019).
• Bashford-Rogers et al., Antibody repertoire analysis in polygenic autoimmune diseases. Immunology (2018).
• Chaudhary and Wesemann, Analyzing Immunoglobulin Repertoires. Front Immunol (2018).
• Collins and Watson, Immunoglobulin Light Chain Gene Rearrangements, Receptor Editing and the Development of a Self-Tolerant Antibody Repertoire. Front Immunol (2018).
• Ghraichy et al., B-cell receptor repertoire sequencing in patients with primary immunodeficiency: a review. Immunology (2018).
• Miho et al., Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires. Front Immunol (2018).
• Parola et al., Integrating high-throughput screening and sequencing for monoclonal antibody discovery and engineering Immunology (2018).
• Yermanos et al., Tracing Antibody Repertoire Evolution by Systems Phylogeny. Front Immunol (2018).
• Friedensohn et al., Advanced Methodologies in High-Throughput Sequencing of Immune Repertoires. Trends Biotechnol (2017).
• Kovaltsuk et al., How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data . Front Immunol (2017).
• Yaari and Kleinstein, Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Med (2015).
• Boyd et al., Deep sequencing and human antibody repertoire analysis. Curr Opin Immunol (2016).
• Greiff et al., Bioinformatic and Statistical Analysis of Adaptive Immune Repertoires . Trends Immunol (2015).
• Hershberg and Luning Prak, The analysis of clonal expansions in normal and autoimmune B cell repertoires. Philos Trans R Soc Lond B Biol Sci (2015).
• Kearney et al., Natural antibody repertoires: development and functional role in inhibiting allergic airway disease. Annu Rev Immunol (2015).
• Lavinder et al., Next-generation sequencing and protein mass spectrometry for the comprehensive analysis of human cellular and serum antibody repertoires. Curr Opin Chem Biol (2015).
• Robinson, Sequencing the functional antibody repertoire--diagnostic and therapeutic discovery. Nat Rev Rheumatol (2015).
• Galson et al., Studying the antibody repertoire after vaccination: practical applications. Trends Immunol (2014).
• Georgiou et al., The promise and challenge of high-throughput sequencing of the antibody repertoire . Nat Biotechnol (2014).
• Six et al. The past, present, and future of immune repertoire biology – the rise of next-generation repertoire analysis Front Immunol (2013).
• Reddy et al. Systems analysis of adaptive immunity by utilization of high-throughput technologies. Curr Opin Biotechnol (2011).

We refer readers to the Observed Antibody Space (OAS) database and the Paired OAS database as the most straight-forward resources for obtaining amino acid sequence datasets for e.g. machine learning applications.

For simplicity, we're mentioning articles that are not already covered by OAS. We focus on papers where authors have uploaded datasets as part of the publication (with no login/registration required) or via public repositories such as the NIH SRA, requiring minimal admin overhead. Publications that only provide V gene + J gene + CDR3 are not mentioned here.

• Aizik et al. Antibody Repertoire Analysis of Tumor-Infiltrating B Cells Reveals Distinct Signatures and Distributions Across Tissues Front Immunol (2021). Data available in SRA.
• Ehling et al. SARS-CoV-2 reactive and neutralizing antibodies discovered by single-cell sequencing of plasma cells and mammalian display. Cell Rep (2021). Data available in SRA.
• Harris et al. Tumor-Infiltrating B Lymphocyte Profiling Identifies IgG-Biased, Clonally Expanded Prognostic Phenotypes in Triple-Negative Breast Cancer. Caner Res (2021). Data available directly from publication.
• Ohm-Laursen et al. B Cell Mobilization, Dissemination, Fine Tuning of Local Antigen Specificity and Isotype Selection in Asthma. Front Immunol (2021). Data available in SRA.
• Turner et al. SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses. Nature (2021). Data available in SRA.
• Sokal et al. Maturation and persistence of the anti-SARS-CoV-2 memory B cell response. Cell (2021). Data available in EBI ArrayExpress.
• Stewart et al. Single-Cell Transcriptomic Analyses Define Distinct Peripheral B Cell Subsets and Discrete Development Pathways. Data available in EBI ArrayExpress.
• Wieland et al. Defining HPV-specific B cell responses in patients with head and neck cancer. Nature (2021). Data available via GEO.
• Yang et al. Large-scale analysis of 2,152 Ig-seq datasets reveals key features of B cell biology and the antibody repertoire. Cell Rep (2021). Data available in SRA.
• Galson et al. Deep Sequencing of B Cell Receptor Repertoires From COVID-19 Patients Reveals Strong Convergent Immune Signatures. Front Immunol (2020). Data available directly from publication.
• Li et al. Mucosal or systemic microbiota exposures shape the B cell repertoire. Nature (2020). Data in SRA.
• Ramesh et al. A pathogenic and clonally expanded B cell transcriptome in active multiple sclerosis. PNAS (2020). Data in SRA and GEO.
• Roskin et al. Aberrant B cell repertoire selection associated with HIV neutralizing antibody breadth. Nat Immunol (2020). Data in SRA.
• Woodruff et al. Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19. Nat Immunol (2020). Data in SRA.

The remit of this section is to cover how machine learning (ML) methods have been trained using BCR repertoire data, or applying existing ML tools for enhancing repertoire analyses. Due to the nature of the way things are moving in this space, quite a few publications are just pre-prints!

• Ghraichy et al. Different B cell subpopulations show distinct patterns in their IgH repertoire metrics. eLife (2021).
• Kim et al. Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences. biorXiv (2021).
• Leem et al. Deciphering the language of antibodies using self-supervised learning. biorXiv (2021).
• Marks et al. Humanization of antibodies using a machine learning approach on large-scale repertoire data. Bioinformatics (2021).
• Ostrovsky-Berman et al. Immune2vec: Embedding B/T Cell Receptor Sequences in ℝN Using Natural Language Processing. Front Immunol (2021).
• Pavlovic et al. The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat Mach Intell (2021).
• Prihoda et al. BioPhi: A platform for antibody design, humanization and humanness evaluation based on natural antibody repertoires and deep learning. biorXiv (2021).
• Richardson et al. A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies. mAbs (2021).
• Ruffolo et al. Antibody structure prediction using interpretable deep learning. Patterns (2021a).
• Ruffolo et al. Deciphering antibody affinity maturation with language models and weakly supervised learning. arXiv (2021b).
• Shemesh et al. Machine Learning Analysis of Naïve B-Cell Receptor Repertoires Stratifies Celiac Disease Patients and Controls. Front Immunol (2021).
• Shuai et al. Generative Language Modeling for Antibody Design biorXiv (2021).
• Amimeur et al. Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks. biorXiv (2020).
• Fridensohn et al. Convergent selection in antibody repertoires is revealed by deep learning. biorXiv (2020).
• Widrich et al. Modern Hopfield Networks and Attention for Immune Repertoire Classification. NeurIPS Proceedings (2020).
• Wollacott et al. Quantifying the nativeness of antibody sequences using long short-term memory networks. Protein Eng Des Sel (2019).

The remit of this section is to cover "methods papers" that focus on how to identify clonotypes from immune BCR repertoire datasets.

• Lindenbaum et al., Alignment free identification of clones in B cell receptor repertoires. Nucleic Acids Res (2021).
• Nouri and Kleinstein, A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data. Bioinformatics (2018).
• Gupta et al., Hierarchical clustering can identify B cell clones with high confidence in Ig repertoire sequencing data. J Immunol (2017).

The remit of this section is to cover how structure prediction methods (such as ABodyBuilder) can be used to interrogate and understand "structural convergence" of the BCR repertoire. We'd like to point readers to the latest structure prediction tools in this space, including ABLooper, DeepAb, and of course AlphaFold2.

• Raybould et al. Public Baseline and shared response structures support the theory of antibody repertoire functional commonality. PLoS Comput Biol (2021).
• Robinson et al. Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies. PLoS Comput Biol (2021).
• Kovaltsuk et al. Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice. PLoS Comput Biol (2020).
• Krawczyk et al. Structurally Mapping Antibody Repertoires. Front Immunol (2018).