The DOI matches its Crossref record; 129 of the 132 references it cites were found in a registry or an index.
Bioinformatic and Statistical Analysis of Adaptive Immune Repertoires
Abstract
High-throughput sequencing (HTS) of immune repertoires has enabled the quantitative analysis of adaptive immune responses and offers the potential to revolutionize research in lymphocyte biology, vaccine profiling, and monoclonal antibody engineering. Advances in sequencing technology coupled to an exponential decline in sequencing costs have fueled the recent overwhelming interest in immune repertoire sequencing. This, in turn, has sparked the development of numerous methods for bioinformatic and statistics-driven interpretation and visualization of immune repertoires. Here, we review the current literature on bioinformatic and statistical analysis of immune repertoire HTS data and discuss underlying assumptions, applicability, and scope. We further highlight important directions for future research, which could propel immune repertoire HTS to becoming a standard method for measuring adaptive immune responses.
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“Owing to somatic hypermutation, B cells from clonal lineages are typically grouped with a Hamming distance-based homology of more than 80% in their CDR3 amino acid sequence175.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [175]
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3 of the 132 references couldn’t be matched to a known paper.
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
“Owing to inherent structural differences of the modalities, novel approaches such as TESSA 186 , mvTCR 187 or Conga 188 for TCR data and Benisse 189 for BCR data aim to integrate both modalities for easier joint annotations and visualizations.”
MethodAntigen receptor repertoire profiling from RNA-seq data
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Hackl, H., Charoentong, P., Finotello, F., & Trajanoski, Z. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.67 · Cited by 272
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Corcoran, M., Phad, G. E., Bernat, N. V., Stahl‐Hennig, C., Sumida, N., Persson, M. A. A., Martin, M., & Hedestam, G. B. K. (2016) · Nature Communications · doi:10.1038/ncomms13642 · Cited by 222
Metadata
- Authors
- Greiff, V., Miho, E., Menzel, U., & Reddy, S. T.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Reddy, Sai T.
- Venue
- Trends in Immunology
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 36
- Issue
- 11
- Pages
- 738-749
- ISSN
- 1471-4906 (ISSN-L)
- OpenAlex
- W1883768105
- Cited by
- 210
- References
- 132
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 26 · 2023: 15 · 2022: 28 · 2021: 23 · 2020: 26 · 2019: 14 · 2018: 15 · 2017: 17 · 2016: 12
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Immunology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
- Keywords
- immune repertoire sequencing, adaptive immune response, bioinformatics analysis, statistical analysis, B cell receptor repertoire, T cell receptor repertoire, lymphocyte biology
- MeSH terms
- Animals, Data Interpretation, Statistical, Humans, Computational Biology, Adaptive Immunity, High-Throughput Nucleotide Sequencing
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