Alignment free identification of clones in B cell receptor repertoires

Lindenbaum, Ofir; Nouri, Nima; Kluger, Yuval; Kleinstein, Steven H.

doi:10.1101/2020.03.30.017384

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Hence, sequences randomly sampled from multiple unrelated individuals, i.e. negation sequences , can be introduced and used to define a threshold by computing the distribution of distances between negation sequences and their closest counterparts within the considered individual ( 42 ) ( Figure 2A ). In practice, a threshold is chosen that allows a fraction of false-positive sequences roughly equal to a tolerance

to be below the chosen threshold.…”

Section: Resultsmentioning

confidence: 99%

“…Data were downloaded from their original study: GC Data ( 36 ) hepatitis B vaccination data ( 8 ) and simulated repertoire data ( 42 ). Additionally, a set of the negation sequences was generated by randomly sampling sequences from multiple unrelated individuals.…”

Section: Methodsmentioning

confidence: 99%

“…Alignment-free: All B cells sequences were first truncated from their

end to a fixed number of nucleotides (

), and then encoded into a numerical vector using their

-mer representation reweighted with the term frequency-inverse document frequency ( tf-idf ) weighting scheme (see section 4.2.2). Following on from previous work ( 42 ), we set

and

as this combination was found to yield optimal performance in terms of clonal identification.…”

Section: Methodsmentioning

confidence: 99%

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Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires

et al. 2023

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The adaptive immune system has the extraordinary ability to produce a broad range of immunoglobulins that can bind a wide variety of antigens. During adaptive immune responses, activated B cells duplicate and undergo somatic hypermutation in their B-cell receptor (BCR) genes, resulting in clonal families of diversified B cells that can be related back to a common ancestor. Advances in high-throughput sequencing technologies have enabled the high-throughput characterization of B-cell repertoires, however, the accurate identification of clonally related BCR sequences remains a major challenge. In this study, we compare three different clone identification methods on both simulated and experimental data, and investigate their impact on the characterization of B-cell diversity. We observe that different methods lead to different clonal definitions, which affects the quantification of clonal diversity in repertoire data. Our analyses show that direct comparisons between clonal clusterings and clonal diversity of different repertoires should be avoided if different clone identification methods were used to define the clones. Despite this variability, the diversity indices inferred from the repertoires’ clonal characterization across samples show similar patterns of variation regardless of the clonal identification method used. We find the Shannon entropy to be the most robust in terms of the variability of diversity rank across samples. Our analysis also suggests that the traditional germline gene alignment-based method for clonal identification remains the most accurate when the complete information about the sequence is known, but that alignment-free methods may be preferred for shorter sequencing read lengths. We make our implementation freely available as a Python library cdiversity.

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to be below the chosen threshold.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Alignment-free: All B cells sequences were first truncated from their

end to a fixed number of nucleotides (

), and then encoded into a numerical vector using their

-mer representation reweighted with the term frequency-inverse document frequency ( tf-idf ) weighting scheme (see section 4.2.2). Following on from previous work ( 42 ), we set

and

as this combination was found to yield optimal performance in terms of clonal identification.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires

et al. 2023

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“…Therefore, much effort is put into more advanced and alternative strategies that make use of different grouping criteria or thresholds to infer clones, including ones based on probabilistic models or spectral clustering with adaptive thresholds ( Ralph and Matsen, 2016 ; Nouri and Kleinstein, 2018 ; 2020 ). Just recently, Lindenbaum and colleagues introduced an alignment-free method bypassing initial V- and J-gene assignments ( Lindenbaum et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Csepregi¹,

Ehling²,

Wagner³

et al. 2020

iScience

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Advances in reading, writing, and editing DNA are providing unprecedented insights into the complexity of immunological systems. This combination of systems and synthetic biology methods is enabling the quantitative and precise understanding of molecular recognition in adaptive immunity, thus providing a framework for reprogramming immune responses for translational medicine. In this review, we will highlight state-of-the-art methods such as immune repertoire sequencing, immunoinformatics, and immunogenomic engineering and their application toward adaptive immunity. We showcase novel and interdisciplinary approaches that have the promise of transforming the design and breadth of molecular and cellular immunotherapies.

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Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data

Gervásio

Ferreira

Felicori

2022

Preprint

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Next-generation sequencing technologies revolutionize our understanding of immunoglobulin (Ig) profiles in different immune states. Clonotyping (grouping Ig sequences into B cell clones) allows the investigation of the diversity of repertoires and how they change upon antigen exposure. Despite its importance, there is no consensus on the best method for clonotyping, and the methods developed for that are computationally intractable for large sequencing datasets. This is the case of Change-O, which compares sequences through hamming distance and uses hierarchical clustering for this task. We propose implementing an approach to identify B cell clones from Ig repertoire data, named Yclon, that makes an alignment-free comparison of the sequences, focusing on reducing the runtime and computer memory usage. Overall, we find that a hierarchical clustering approach grouped Ig sequences into B cell clones similarly to Change-O. However, we observed that Yclon was around 35 times faster and even was able to process larger than 2 million sequences Ig repertoire, which is a critical part of repertoire studies and enables understanding antibody repertoire structure and affinity maturation. YClon is written in Python3 and is available on GitHub (https://github.com/jao321/YClon.git) .

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Alignment free identification of clones in B cell receptor repertoires

Cited by 5 publications

References 33 publications

Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires

Exploring the impact of clonal definition on B-cell diversity: implications for the analysis of immune repertoires

Immune Literacy: Reading, Writing, and Editing Adaptive Immunity

Yclon: Ultrafast clustering of B cell clones from high-throughput immunoglobulin repertoire sequencing data

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