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
Hlavní autori: Balashova, Daria, van Schaik, Barbera D. C., Stratigopoulou, Maria, Guikema, Jeroen E. J., Caniels, Tom G., Claireaux, Mathieu, van Gils, Marit J., Musters, Anne, Anang, Dornatien C., de Vries, Niek, Greiff, Victor, van Kampen, Antoine H. C.
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Vydavateľské údaje: London BioMed Central 08.02.2024
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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.
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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  givenname: Barbera D. C.
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  givenname: Maria
  surname: Stratigopoulou
  fullname: Stratigopoulou, Maria
  organization: Cancer Center Amsterdam, Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention
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  givenname: Jeroen E. J.
  surname: Guikema
  fullname: Guikema, Jeroen E. J.
  organization: Cancer Center Amsterdam, Amsterdam UMC location University of Amsterdam, Pathology, Lymphoma and Myeloma Center Amsterdam
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  givenname: Tom G.
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  fullname: Claireaux, Mathieu
  organization: Amsterdam UMC location University of Amsterdam, Medical Microbiology and Infection Prevention, Amsterdam Infection and Immunity, Infectious Diseases
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  givenname: Marit J.
  surname: van Gils
  fullname: van Gils, Marit J.
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  surname: Musters
  fullname: Musters, Anne
  organization: Amsterdam UMC location University of Amsterdam, Experimental Immunology, Amsterdam Rheumatology & Immunology Center
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  givenname: Dornatien C.
  surname: Anang
  fullname: Anang, Dornatien C.
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  surname: Greiff
  fullname: Greiff, Victor
  organization: Department of Immunology, University of Oslo and Oslo University Hospital
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  givenname: Antoine H. C.
  surname: van Kampen
  fullname: van Kampen, Antoine H. C.
  email: a.h.vankampen@amsterdamumc.nl
  organization: Amsterdam UMC location University of Amsterdam, Epidemiology and Data Science, Amsterdam Public Health, Methodology, Amsterdam Infection and Immunity, Inflammatory Diseases, Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam
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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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Issue 1
Keywords B-cell shared clonal families
B-cell clonal family partitioning
AIRR-seq data simulation
B-cell receptor repertoire
AIRR-seq data
Language English
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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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Snippet 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...
Abstract The reconstruction of clonal families (CFs) in B-cell receptor (BCR) repertoire analysis is a crucial step to understand the adaptive immune system...
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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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