Systematic evaluation of B-cell clonal family inference approaches
The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recomb...
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| Vydané v: | BMC immunology Ročník 25; číslo 1; s. 13 - 22 |
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| Hlavní autori: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
08.02.2024
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1471-2172, 1471-2172 |
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| Abstract | The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method. |
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| AbstractList | The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method. Abstract The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method. The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method. Keywords: B-cell receptor repertoire, B-cell clonal family partitioning, AIRR-seq data, AIRR-seq data simulation, B-cell shared clonal families The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method.The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system and how it responds to antigens. The BCR repertoire of an individual is formed throughout life and is diverse due to several factors such as gene recombination and somatic hypermutation. The use of Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) using next generation sequencing enabled the generation of full BCR repertoires that also include rare CFs. The reconstruction of CFs from AIRR-seq data is challenging and several approaches have been developed to solve this problem. Currently, most methods use the heavy chain (HC) only, as it is more variable than the light chain (LC). CF reconstruction options include the definition of appropriate sequence similarity measures, the use of shared mutations among sequences, and the possibility of reconstruction without preliminary clustering based on V- and J-gene annotation. In this study, we aimed to systematically evaluate different approaches for CF reconstruction and to determine their impact on various outcome measures such as the number of CFs derived, the size of the CFs, and the accuracy of the reconstruction. The methods were compared to each other and to a method that groups sequences based on identical junction sequences and another method that only determines subclones. We found that after accounting for data set variability, in particular sequencing depth and mutation load, the reconstruction approach has an impact on part of the outcome measures, including the number of CFs. Simulations indicate that unique junctions and subclones should not be used as substitutes for CF and that more complex methods do not outperform simpler methods. Also, we conclude that different approaches differ in their ability to correctly reconstruct CFs when not considering the LC and to identify shared CFs. The results showed the effect of different approaches on the reconstruction of CFs and highlighted the importance of choosing an appropriate method. |
| ArticleNumber | 13 |
| Audience | Academic |
| Author | Balashova, Daria de Vries, Niek Greiff, Victor Stratigopoulou, Maria Musters, Anne van Schaik, Barbera D. C. Caniels, Tom G. Claireaux, Mathieu Anang, Dornatien C. van Gils, Marit J. Guikema, Jeroen E. J. van Kampen, Antoine H. C. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38331731$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.chom.2018.05.001 10.1186/s12864-020-6571-7 10.3389/fimmu.2014.00010 10.1093/nargab/lqac049 10.18637/jss.v067.i01 10.3389/fimmu.2022.915687 10.1093/bioinformatics/btaa158 10.3389/fimmu.2019.00987 10.1093/nar/gkt382 10.1111/j.2006.0030-1299.14714.x 10.1093/bioinformatics/bty235 10.1038/s41590-019-0581-0 10.1136/annrheumdis-2018-214898 10.1371/journal.pcbi.1010723 10.1093/bioinformatics/btv359 10.1371/journal.pgen.1010652 10.1101/2022.12.22.521661 10.1098/rstb.2014.0239 10.1016/0022-5193(70)90124-4 10.1371/journal.pcbi.1005086 10.18637/jss.v082.i13 10.3389/fimmu.2022.1014439 10.1101/2022.04.21.489084 10.1371/journal.pcbi.1007837 10.1101/pdb.prot5633 10.1038/s41586-019-0879-y 10.1111/imm.12865 10.1093/bioinformatics/btac612 10.1084/jem.132.2.211 10.1371/journal.pcbi.1010411 10.1038/s41586-019-1595-3 10.1371/journal.pcbi.1007977 10.1093/bioinformatics/btx533 10.1007/978-0-387-98141-3 10.1182/blood.2020007039 10.1016/j.jneuroim.2022.577932 10.1080/19420862.2020.1729683 10.1016/j.chom.2020.09.002 10.1101/pdb.top115 10.1038/s41586-019-0934-8 10.1038/ncomms14049 10.1136/annrheumdis-2012-202861 10.4049/jimmunol.1900666 10.1101/2022.11.09.463832 10.3389/fimmu.2018.02149 10.1101/gr.154815.113 10.1038/s41467-022-32232-0 10.1016/j.it.2015.09.006 10.1093/nar/gkaa1160 10.1038/nbt.2492 10.1146/annurev-immunol-020711-075032 10.21105/joss.03139 10.1016/j.celrep.2017.04.054 10.2307/1934145 10.1111/oik.07202 10.1186/s13073-015-0169-8 10.1093/bioinformatics/btw631 10.3389/fimmu.2013.00358 |
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| Keywords | B-cell shared clonal families B-cell clonal family partitioning AIRR-seq data simulation B-cell receptor repertoire AIRR-seq data |
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| References | M Ghraichy (600_CR10) 2018; 153 Y Safonova (600_CR57) 2019; 3 KM Roskin (600_CR44) 2020; 21 GXY Zheng (600_CR63) 2017; 8 V Greiff (600_CR6) 2015; 7 RJM Bashford-Rogers (600_CR36) 2019; 574 600_CR39 RJM Bashford-Rogers (600_CR35) 2013; 23 C Soto (600_CR34) 2019; 566 I Setliff (600_CR50) 2018; 23 A Agathangelidis (600_CR11) 2021; 137 C Zhang (600_CR23) 2022; 6 N Nouri (600_CR15) 2018; 34 V Greiff (600_CR28) 2015; 36 J Ye (600_CR38) 2013; 41 600_CR19 L Jost (600_CR30) 2006; 113 A Fowler (600_CR37) 2020; 21 600_CR25 600_CR24 B Briney (600_CR8) 2019; 566 AD Yermanos (600_CR22) 2018; 2 600_CR62 GD Victora (600_CR3) 2012; 30 600_CR61 600_CR60 K Hutcheson (600_CR32) 1970; 29 U Hershberg (600_CR4) 2015; 370 BJ DeKosky (600_CR43) 2013; 31 600_CR1 600_CR16 600_CR59 SCA Nielsen (600_CR9) 2020; 28 600_CR18 600_CR2 TT Wu (600_CR26) 1970; 132 600_CR5 600_CR56 600_CR55 600_CR14 600_CR58 M Claireaux (600_CR42) 2022; 13 600_CR52 600_CR54 600_CR53 T Andreani (600_CR20) 2022; 4 SH Hurlbert (600_CR31) 1971; 52 M Roswell (600_CR29) 2021; 130 L van der Weele (600_CR12) 2022; 370 A Musters (600_CR41) 2022; 27 A Yermanos (600_CR21) 2017; 33 ME Doorenspleet (600_CR27) 2014; 73 NT Gupta (600_CR13) 2015; 31 600_CR49 V Greiff (600_CR7) 2017; 19 600_CR48 600_CR45 O Lindenbaum (600_CR17) 2021; 49 600_CR47 600_CR46 D Lüdecke (600_CR51) 2021; 6 600_CR40 S Pollastro (600_CR33) 2019; 78 |
| References_xml | – volume: 23 start-page: 845 issue: 6 year: 2018 ident: 600_CR50 publication-title: Cell Host Microbe. doi: 10.1016/j.chom.2018.05.001 – volume: 21 start-page: 176 issue: 1 year: 2020 ident: 600_CR37 publication-title: BMC Genomics. doi: 10.1186/s12864-020-6571-7 – ident: 600_CR5 doi: 10.3389/fimmu.2014.00010 – volume: 4 start-page: lqac049 issue: 3 year: 2022 ident: 600_CR20 publication-title: NAR Genom Bioinformat. doi: 10.1093/nargab/lqac049 – ident: 600_CR46 doi: 10.18637/jss.v067.i01 – volume: 27 start-page: 915687 issue: 13 year: 2022 ident: 600_CR41 publication-title: Front Immunol. doi: 10.3389/fimmu.2022.915687 – ident: 600_CR45 doi: 10.1093/bioinformatics/btaa158 – volume: 3 start-page: 987 issue: 10 year: 2019 ident: 600_CR57 publication-title: Front Immunol. doi: 10.3389/fimmu.2019.00987 – volume: 41 start-page: W34 issue: W1 year: 2013 ident: 600_CR38 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt382 – volume: 113 start-page: 363 issue: 2 year: 2006 ident: 600_CR30 publication-title: Oikos. doi: 10.1111/j.2006.0030-1299.14714.x – volume: 34 start-page: i341 issue: 13 year: 2018 ident: 600_CR15 publication-title: Bioinformat. doi: 10.1093/bioinformatics/bty235 – volume: 21 start-page: 199 issue: 2 year: 2020 ident: 600_CR44 publication-title: Nat Immunol. doi: 10.1038/s41590-019-0581-0 – volume: 78 start-page: 1339 issue: 10 year: 2019 ident: 600_CR33 publication-title: Ann Rheum Dis. doi: 10.1136/annrheumdis-2018-214898 – ident: 600_CR19 doi: 10.1371/journal.pcbi.1010723 – volume: 31 start-page: 3356 issue: 20 year: 2015 ident: 600_CR13 publication-title: Bioinformat. doi: 10.1093/bioinformatics/btv359 – ident: 600_CR55 doi: 10.1371/journal.pgen.1010652 – ident: 600_CR62 doi: 10.1101/2022.12.22.521661 – ident: 600_CR49 – volume: 370 start-page: 20140239 issue: 1676 year: 2015 ident: 600_CR4 publication-title: Phil Trans R Soc B. doi: 10.1098/rstb.2014.0239 – volume: 29 start-page: 151 issue: 1 year: 1970 ident: 600_CR32 publication-title: J Theor Biol. doi: 10.1016/0022-5193(70)90124-4 – ident: 600_CR14 doi: 10.1371/journal.pcbi.1005086 – ident: 600_CR47 doi: 10.18637/jss.v082.i13 – volume: 6 start-page: 1014439 issue: 13 year: 2022 ident: 600_CR23 publication-title: Front Immunol. doi: 10.3389/fimmu.2022.1014439 – ident: 600_CR61 doi: 10.1101/2022.04.21.489084 – ident: 600_CR58 doi: 10.1371/journal.pcbi.1007837 – ident: 600_CR59 doi: 10.1101/pdb.prot5633 – volume: 566 start-page: 393 issue: 7744 year: 2019 ident: 600_CR8 publication-title: Nature. doi: 10.1038/s41586-019-0879-y – volume: 153 start-page: 145 issue: 2 year: 2018 ident: 600_CR10 publication-title: Immunol. doi: 10.1111/imm.12865 – ident: 600_CR52 – ident: 600_CR24 doi: 10.1093/bioinformatics/btac612 – volume: 132 start-page: 211 issue: 2 year: 1970 ident: 600_CR26 publication-title: J Exp Med. doi: 10.1084/jem.132.2.211 – ident: 600_CR60 doi: 10.1371/journal.pcbi.1010411 – volume: 574 start-page: 122 issue: 7776 year: 2019 ident: 600_CR36 publication-title: Nature. doi: 10.1038/s41586-019-1595-3 – ident: 600_CR16 doi: 10.1371/journal.pcbi.1007977 – volume: 33 start-page: 3938 issue: 24 year: 2017 ident: 600_CR21 publication-title: Bioinformat. doi: 10.1093/bioinformatics/btx533 – ident: 600_CR53 doi: 10.1007/978-0-387-98141-3 – volume: 137 start-page: 1365 issue: 10 year: 2021 ident: 600_CR11 publication-title: Blood. doi: 10.1182/blood.2020007039 – volume: 370 start-page: 577932 year: 2022 ident: 600_CR12 publication-title: J Neuroimmunol. doi: 10.1016/j.jneuroim.2022.577932 – ident: 600_CR48 – ident: 600_CR1 doi: 10.1080/19420862.2020.1729683 – volume: 28 start-page: 516 issue: 4 year: 2020 ident: 600_CR9 publication-title: Cell Host Microbe. doi: 10.1016/j.chom.2020.09.002 – ident: 600_CR2 – ident: 600_CR25 doi: 10.1101/pdb.top115 – volume: 566 start-page: 398 issue: 7744 year: 2019 ident: 600_CR34 publication-title: Nature. doi: 10.1038/s41586-019-0934-8 – volume: 8 start-page: 14049 issue: 1 year: 2017 ident: 600_CR63 publication-title: Nat Commun. doi: 10.1038/ncomms14049 – volume: 73 start-page: 756 issue: 4 year: 2014 ident: 600_CR27 publication-title: Ann Rheum Dis. doi: 10.1136/annrheumdis-2012-202861 – ident: 600_CR18 doi: 10.4049/jimmunol.1900666 – ident: 600_CR40 doi: 10.1101/2022.11.09.463832 – volume: 2 start-page: 2149 issue: 9 year: 2018 ident: 600_CR22 publication-title: Front Immunol. doi: 10.3389/fimmu.2018.02149 – volume: 23 start-page: 1874 issue: 11 year: 2013 ident: 600_CR35 publication-title: Genome Res. doi: 10.1101/gr.154815.113 – volume: 13 start-page: 4539 issue: 1 year: 2022 ident: 600_CR42 publication-title: Nat Commun. doi: 10.1038/s41467-022-32232-0 – volume: 36 start-page: 738 issue: 11 year: 2015 ident: 600_CR28 publication-title: Trends Immunol. doi: 10.1016/j.it.2015.09.006 – volume: 49 start-page: e21 issue: 4 year: 2021 ident: 600_CR17 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkaa1160 – volume: 31 start-page: 166 issue: 2 year: 2013 ident: 600_CR43 publication-title: Nat Biotechnol. doi: 10.1038/nbt.2492 – volume: 30 start-page: 429 issue: 1 year: 2012 ident: 600_CR3 publication-title: Annu Rev Immunol. doi: 10.1146/annurev-immunol-020711-075032 – volume: 6 start-page: 3139 issue: 60 year: 2021 ident: 600_CR51 publication-title: JOSS. doi: 10.21105/joss.03139 – volume: 19 start-page: 1467 issue: 7 year: 2017 ident: 600_CR7 publication-title: Cell Rep. doi: 10.1016/j.celrep.2017.04.054 – volume: 52 start-page: 577 issue: 4 year: 1971 ident: 600_CR31 publication-title: Ecology. doi: 10.2307/1934145 – ident: 600_CR54 – volume: 130 start-page: 321 issue: 3 year: 2021 ident: 600_CR29 publication-title: Oikos. doi: 10.1111/oik.07202 – volume: 7 start-page: 49 issue: 1 year: 2015 ident: 600_CR6 publication-title: Genome Med. doi: 10.1186/s13073-015-0169-8 – ident: 600_CR56 doi: 10.1093/bioinformatics/btw631 – ident: 600_CR39 doi: 10.3389/fimmu.2013.00358 |
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| SubjectTerms | AIRR-seq data AIRR-seq data simulation Allergology Analysis Antigens B cells B-cell clonal family partitioning B-cell receptor B-cell receptor repertoire B-cell shared clonal families Biomedical and Life Sciences Biomedicine Clonal selection Cloning Computational linguistics Cytokines and Growth Factors Datasets DNA sequencing Gene mutations Genes Health aspects Immune system Immunology Language processing Leukocytes Lymphocytes B Methods Mutation Natural language interfaces Next-generation sequencing Nucleotide sequencing Somatic hypermutation Testing Vaccine |
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| Title | Systematic evaluation of B-cell clonal family inference approaches |
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