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Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data Summary: Advances in high-throughput sequencing technologies now allow for large-scale charac- terization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized com- putational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for deter- mining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pres- sure, and calculating sequence chemical properties. All Change-O tools utilize a common data for- mat, which enables the seamless integration of multiple analyses into a single workflow. Availability and implementation: Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. Contact: steven.kleinstein@yale.edu (Alamyar et al., 2012), IgBLAST (Ye et al., 2013), iHMMune-align 1 Introduction (Gae ¨ta et al., 2007)]. However, extracting measures of biological Large-scale characterization of immunoglobulin (Ig) repertoires is and clinical interest from the resulting germline-annotated repertoire now feasible due to dramatic improvements in high-throughput remains a time-consuming and error-prone process that is often de- sequencing technology. Repertoire sequencing is a rapidly growing pendent upon custom analysis scripts. Here, we introduce Change- area, with applications including detection of minimum residual dis- O, a suite of utilities that cover a range of complex analysis tasks for ease, prognosis following transplant, monitoring vaccination re- Ig repertoire sequencing data. sponses, identification of neutralizing antibodies and inferring B cell trafficking patterns (Robins, 2013; Stern et al., 2014). We previ- ously developed the repertoire sequencing toolkit (pRESTO) for pro- 2 Features ducing assembled and error-corrected reads from high-throughput lymphocyte receptor sequencing experiments (Vander Heiden et al., The Change-O suite is composed of four software packages: a col- 2014), which may then be fed into existing methods for alignment lection of Python commandline tools (changeo-ctl) and three separ- against V(D)J germline databases [e.g. IMGT/HighV-QUEST ate R (R Core Team, 2015) packages (alakazam, shm, and tigger) V The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 3356 Change-O 3357 Table 1. Summary of Change-O features and Change-O also includes tools to tune the thresholds based on distance patterns in the repertoire (Glanville et al., 2009). Package Analysis tasks changeo-clt Parsing of V(D)J assignment output 2.3 Quantification of repertoire diversity Basic database manipulation To assess repertoire diversity, Change-O provides an implementa- Multiple alignment of sequence records q tion of the general diversity index ( D) proposed by Hill (1973), Assignment of sequences into clonal groups which encompasses a range of diversity measures as a smooth curve Calculation of CDR3 physiochemical properties over a single varying parameter q. Special cases of this general index alakazam Clonal diversity analysis of diversity correspond to the most popular diversity measures: spe- Lineage reconstruction cies richness (q¼ 0), the exponential Shannon-Weiner index (as shm SHM hot/cold-spot modeling q ! 1), the inverse of the Simpson index (q¼ 2), and the reciprocal Quantification of selection pressure abundance of the largest clone (as q!1). Resampling strategies tigger Inference of novel germline alleles Construction of personalized germline genotype are also provided to perform significance tests and allow compari- son across samples with varying sequencing depth (Wu et al., 2014; Stern et al., 2014). (Table 1). Data are passed to Change-O utilities in the form of a tab-delimited text file. Each utility identifies the relevant input data 2.4 Generation of B cell lineage trees based on standardized column names and adds new columns to the Lineage trees provide a means to trace the ancestral relationships of file with the output information to be carried through to the next cells within a clone. This information has been used to estimate mu- analysis step. Change-O provides tools to import data from the fre- tation rates (Kleinstein et al., 2003), infer B cell trafficking patterns quently used IMGT/HighV-QUEST (Alamyar et al., 2012) tool as (Stern et al., 2014) and trace the accumulation of mutations that well as a set of utilities to perform basic database operations, such drive affinity maturation (Uduman et al., 2014; Wu et al., 2012). as sorting, filtering and modifying annotations. Change-O provides a tool for generating lineage trees using The more computationally expensive components have built-in PHYLIP’s maximum parsimony algorithm (Felsenstein, 1989), with multiprocessing support. Each utility includes detailed help docu- modifications to meet the requirements of an Ig lineage tree (Barak mentation and optional logging to track errors. Example workflow et al., 2008; Stern et al., 2014). Trees may be viewed and exported scripts are provided on the website, which can easily be modified by into different file formats using the igraph (Csardi and Nepusz, adding, removing or reordering analysis steps to meet different ana- 2006) R package. lysis goals. As detailed later, several repertoire analyses may be car- ried out, depending on the nature of the study. 2.5 Somatic hypermutation hot/cold-spot motifs SHM is a process that operates in activated B cells and introduces 2.1 Inference of novel alleles and individual genotype point mutations into the DNA coding for the Ig receptor at a very Germline segment assignment tools, such as IMGT/HighV-QUEST, high rate (10 per base-pair per division) (Kleinstein et al., 2003; work by aligning each sequence against a database of known alleles. McKean et al., 1984). Accurate background models of SHM are However, this process is inaccurate for sequences that utilize previ- critical, since SHM displays intrinsic hot/cold-spot biases (Yaari ously undetected alleles. In this case, the sequence will be assigned to et al., 2013). Change-O provides utilities for estimating the mutabil- the closest known allele and any polymorphisms will be incorrectly ity and substitution rates of DNA motifs from large-scale Ig identified as somatic mutations. To address this problem, the Tool for sequencing data to construct hot/cold-spot motif models. Immunoglobulin Genotype Elucidation (TIgGER) (Gadala-Maria Furthermore, models may be generated based solely on silent muta- et al.,2015) has been implemented as an R package for inclusion in tions, thereby avoiding the confounding influence of selection pres- Change-O. TIgGER determines the complete set of variable region sures (Yaari et al., 2013). These tools can be used to build models of gene segments carried by an individual and identifies novel alleles, SHM targeting and gain insight into the relative contributions of dif- yielding a set of germline alleles personalized to an individual. The ferent error-prone repair pathways in SHM. germline variable region allele assignments are then adjusted based on this individual Ig genotype. This process significantly improves the 2.6 Analysis of selection pressure quality of germline assignments, thus increasing the confidence of For quantifying selection pressure in Ig sequences, Change-O in- downstream analysis dependent upon mutation profiles. cludes the BASELINe (Yaari et al., 2012) method, which has been implemented as an R package for inclusion in the suite. BASELINe quantifies deviations in the frequency of replacement mutations 2.2 Partitioning sequences into clonally related groups compared with a background model of SHM. Users may choose be- Identifying sequences that are descended from the same B cell (clo- tween published background models (Smith et al., 1996; Yaari nal groups) is important to virtually all Ig repertoire analyses. et al., 2013) or infer the background from their own data using the Clonal group sizes and lineage structures provide information on the SHM model building tools described above. underlying response, and clonally related sequences cannot be treated independently in statistical analyses and models. Change-O provides several methods for partitioning sequences into clones. 3 Conclusion Along with published methods based on hierarchical clustering (Ademokun et al., 2011; Chen et al., 2010; Glanville et al., 2009), Change-O is a suite of utilities implementing a wide range of B cell users also have the option to employ several published somatic repertoire analysis methods. Together these tools allow researchers hypermutation (SHM) hot/cold-spot targeting models as distance to quickly implement advanced analysis pipelines for large datasets metrics in the clustering methods (Smith et al., 1996; Yaari et al., generated by repertoire sequencing experiments. A simple tab- 2013; Stern et al., 2014). Users may alter the clustering thresholds, delimited file with standardized column names allows for 3358 N.T.Gupta et al. Gadala-Maria,D. et al. (2015) Automated analysis of high-throughput B-cell communication between the utilities and can easily be viewed using sequencing data reveals a high frequency of novel immunoglobulin V gene any spreadsheet application. This format also allows research segment alleles. Proc. Natl. Acad. Sci. USA, 112, 201417683. groups the flexibility to incorporate other analysis tools into their Gae ¨ ta,B. a. et al. (2007) iHMMune-align: hidden Markov model-based align- in-house analysis pipelines by simply adding additional columns of ment and identification of germline genes in rearranged immunoglobulin information to the central file. Change-O, along with pRESTO gene sequences. Bioinformatics, 23, 1580–1587. (Vander Heiden et al., 2014), provides key components of an analyt- Glanville,J. et al. (2009) Precise determination of the diversity of a combina- ical ecosystem that enables sophisticated analysis of high-through- torial antibody library gives insight into the human immunoglobulin reper- put Ig repertoire sequencing datasets. toire. Proc. Natl. Acad. Sci. USA, 106, 20216–20221. Hill,M.O. (1973) Diversity and evenness: a unifying notation and its conse- quences. Ecology, 54, 427. Acknowledgements Kleinstein,S.H. et al. (2003) Estimating hypermutation rates from clonal tree data. J. Immunol., 171, 4639–4649. The authors thank the Yale University Biomedical High Performance McKean,D. et al. (1984) Generation of antibody diversity in the immune re- Computing Center [funded by National Institutes of Health grants RR19895 sponse of BALB/c mice to influenza virus hemagglutinin. Proc. Natl. Acad. and RR029676-01] for use of their computing resources. The authors also Sci. USA, 81, 3180–3184. thank Chris Bolen, Moriah Cohen, Jingli Shan and Sonia Timberlake for test- R Core Team (2015) R: A Language and Environment for Statistical ing Change-O and providing helpful feedback. Computing. R Foundation for Statistical Computing, Vienna, Austria. Robins,H. (2013) Immunosequencing: applications of immune repertoire deep sequencing. Curr. Opin. Immunol., 25, 646–652 Funding Smith,D. et al. (1996) Di- and trinucleotide target preferences of somatic This work was supported by the National Institutes of Health [R01AI104739 mutagenesis in normal and autoreactive B cells. J. Immunol., 156, to S.H.K.; T15LM07056 to N.T.G., T15LM07056 to J.A.V.H. from 2642–2652. National Library of Medicine (NLM)] and by the United States-Israel Stern,J.N.H. et al. (2014) B cells populating the multiple sclerosis Binational Science Foundation [2013395 to G.Y. and S.H.K.]. brain mature in the draining cervical lymph nodes. Sci. Transl. Med., 6, 248ra107. Conflict of Interest: none declared. Uduman,M. et al. (2014) Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences. J. Immunol., 192, 867–874. References Vander Heiden,J.A. et al. (2014) pRESTO: a toolkit for processing high- Ademokun,A. et al. (2011) Vaccination-induced changes in human B-cell rep- throughput sequencing raw reads of lymphocyte receptor repertoires. ertoire and pneumococcal IgM and IgA antibody at different ages. Aging Bioinformatics, 30, 1930–1932 cell, 10, 922–930. Wu,Y.-C.B. et al. (2012) Age-related changes in human peripheral blood IGH Alamyar,E. et al. (2012) IMGT(V) tools for the nucleotide analysis of im- repertoire following vaccination. Front. Immunol., 3, 193. munoglobulin (IG) and T cell receptor (TR) V-(D)-J repertoires, polymorph- Wu,Y.-C.B. et al. (2014) Influence of seasonal exposure to grass pollen on isms, and IG mutations: IMGT/V-QUEST and IMGT/HighV-QUEST for local and peripheral blood IgE repertoires in patients with allergic rhinitis. NGS. Methods Mol. Biol., 882, 569–604. J. Allergy Clin. Immunol., 134, 604–612. Barak,M. et al. (2008) IgTree: creating immunoglobulin variable region gene Yaari,G. et al. (2012) Quantifying selection in high-throughput immunoglobu- lineage trees. J. Immunol. Methods, 338, 67–74. lin sequencing data sets. Nucleic Acids Res., 40, e134. Chen,Z. et al. (2010) Clustering-based identification of clonally-related im- Yaari,G. et al. (2013) Models of somatic hypermutation targeting and substi- munoglobulin gene sequence sets. Immunome Res., 6(Suppl. 1), S4. tution based on synonymous mutations from high-throughput immuno- Csardi,G. and Nepusz,T. (2006) The igraph software package for complex globulin sequencing data. Front. Immunol., 4, 358. network research. InterJournal, Complex Systems, 1695. Ye,J. et al. (2013) IgBLAST: an immunoglobulin variable domain sequence Felsenstein,J. (1989) PHYLIP - Phylogeny inference package (Version 3.2). analysis tool. Nucleic Acids Res., 41(Web Server Issue), W34–W40. Cladistics, 5, 164–166. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data

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Oxford University Press
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© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
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Abstract

Summary: Advances in high-throughput sequencing technologies now allow for large-scale charac- terization of B cell immunoglobulin (Ig) repertoires. The high germline and somatic diversity of the Ig repertoire presents challenges for biologically meaningful analysis, which requires specialized com- putational methods. We have developed a suite of utilities, Change-O, which provides tools for advanced analyses of large-scale Ig repertoire sequencing data. Change-O includes tools for deter- mining the complete set of Ig variable region gene segment alleles carried by an individual (including novel alleles), partitioning of Ig sequences into clonal populations, creating lineage trees, inferring somatic hypermutation targeting models, measuring repertoire diversity, quantifying selection pres- sure, and calculating sequence chemical properties. All Change-O tools utilize a common data for- mat, which enables the seamless integration of multiple analyses into a single workflow. Availability and implementation: Change-O is freely available for non-commercial use and may be downloaded from http://clip.med.yale.edu/changeo. Contact: steven.kleinstein@yale.edu (Alamyar et al., 2012), IgBLAST (Ye et al., 2013), iHMMune-align 1 Introduction (Gae ¨ta et al., 2007)]. However, extracting measures of biological Large-scale characterization of immunoglobulin (Ig) repertoires is and clinical interest from the resulting germline-annotated repertoire now feasible due to dramatic improvements in high-throughput remains a time-consuming and error-prone process that is often de- sequencing technology. Repertoire sequencing is a rapidly growing pendent upon custom analysis scripts. Here, we introduce Change- area, with applications including detection of minimum residual dis- O, a suite of utilities that cover a range of complex analysis tasks for ease, prognosis following transplant, monitoring vaccination re- Ig repertoire sequencing data. sponses, identification of neutralizing antibodies and inferring B cell trafficking patterns (Robins, 2013; Stern et al., 2014). We previ- ously developed the repertoire sequencing toolkit (pRESTO) for pro- 2 Features ducing assembled and error-corrected reads from high-throughput lymphocyte receptor sequencing experiments (Vander Heiden et al., The Change-O suite is composed of four software packages: a col- 2014), which may then be fed into existing methods for alignment lection of Python commandline tools (changeo-ctl) and three separ- against V(D)J germline databases [e.g. IMGT/HighV-QUEST ate R (R Core Team, 2015) packages (alakazam, shm, and tigger) V The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 3356 Change-O 3357 Table 1. Summary of Change-O features and Change-O also includes tools to tune the thresholds based on distance patterns in the repertoire (Glanville et al., 2009). Package Analysis tasks changeo-clt Parsing of V(D)J assignment output 2.3 Quantification of repertoire diversity Basic database manipulation To assess repertoire diversity, Change-O provides an implementa- Multiple alignment of sequence records q tion of the general diversity index ( D) proposed by Hill (1973), Assignment of sequences into clonal groups which encompasses a range of diversity measures as a smooth curve Calculation of CDR3 physiochemical properties over a single varying parameter q. Special cases of this general index alakazam Clonal diversity analysis of diversity correspond to the most popular diversity measures: spe- Lineage reconstruction cies richness (q¼ 0), the exponential Shannon-Weiner index (as shm SHM hot/cold-spot modeling q ! 1), the inverse of the Simpson index (q¼ 2), and the reciprocal Quantification of selection pressure abundance of the largest clone (as q!1). Resampling strategies tigger Inference of novel germline alleles Construction of personalized germline genotype are also provided to perform significance tests and allow compari- son across samples with varying sequencing depth (Wu et al., 2014; Stern et al., 2014). (Table 1). Data are passed to Change-O utilities in the form of a tab-delimited text file. Each utility identifies the relevant input data 2.4 Generation of B cell lineage trees based on standardized column names and adds new columns to the Lineage trees provide a means to trace the ancestral relationships of file with the output information to be carried through to the next cells within a clone. This information has been used to estimate mu- analysis step. Change-O provides tools to import data from the fre- tation rates (Kleinstein et al., 2003), infer B cell trafficking patterns quently used IMGT/HighV-QUEST (Alamyar et al., 2012) tool as (Stern et al., 2014) and trace the accumulation of mutations that well as a set of utilities to perform basic database operations, such drive affinity maturation (Uduman et al., 2014; Wu et al., 2012). as sorting, filtering and modifying annotations. Change-O provides a tool for generating lineage trees using The more computationally expensive components have built-in PHYLIP’s maximum parsimony algorithm (Felsenstein, 1989), with multiprocessing support. Each utility includes detailed help docu- modifications to meet the requirements of an Ig lineage tree (Barak mentation and optional logging to track errors. Example workflow et al., 2008; Stern et al., 2014). Trees may be viewed and exported scripts are provided on the website, which can easily be modified by into different file formats using the igraph (Csardi and Nepusz, adding, removing or reordering analysis steps to meet different ana- 2006) R package. lysis goals. As detailed later, several repertoire analyses may be car- ried out, depending on the nature of the study. 2.5 Somatic hypermutation hot/cold-spot motifs SHM is a process that operates in activated B cells and introduces 2.1 Inference of novel alleles and individual genotype point mutations into the DNA coding for the Ig receptor at a very Germline segment assignment tools, such as IMGT/HighV-QUEST, high rate (10 per base-pair per division) (Kleinstein et al., 2003; work by aligning each sequence against a database of known alleles. McKean et al., 1984). Accurate background models of SHM are However, this process is inaccurate for sequences that utilize previ- critical, since SHM displays intrinsic hot/cold-spot biases (Yaari ously undetected alleles. In this case, the sequence will be assigned to et al., 2013). Change-O provides utilities for estimating the mutabil- the closest known allele and any polymorphisms will be incorrectly ity and substitution rates of DNA motifs from large-scale Ig identified as somatic mutations. To address this problem, the Tool for sequencing data to construct hot/cold-spot motif models. Immunoglobulin Genotype Elucidation (TIgGER) (Gadala-Maria Furthermore, models may be generated based solely on silent muta- et al.,2015) has been implemented as an R package for inclusion in tions, thereby avoiding the confounding influence of selection pres- Change-O. TIgGER determines the complete set of variable region sures (Yaari et al., 2013). These tools can be used to build models of gene segments carried by an individual and identifies novel alleles, SHM targeting and gain insight into the relative contributions of dif- yielding a set of germline alleles personalized to an individual. The ferent error-prone repair pathways in SHM. germline variable region allele assignments are then adjusted based on this individual Ig genotype. This process significantly improves the 2.6 Analysis of selection pressure quality of germline assignments, thus increasing the confidence of For quantifying selection pressure in Ig sequences, Change-O in- downstream analysis dependent upon mutation profiles. cludes the BASELINe (Yaari et al., 2012) method, which has been implemented as an R package for inclusion in the suite. BASELINe quantifies deviations in the frequency of replacement mutations 2.2 Partitioning sequences into clonally related groups compared with a background model of SHM. Users may choose be- Identifying sequences that are descended from the same B cell (clo- tween published background models (Smith et al., 1996; Yaari nal groups) is important to virtually all Ig repertoire analyses. et al., 2013) or infer the background from their own data using the Clonal group sizes and lineage structures provide information on the SHM model building tools described above. underlying response, and clonally related sequences cannot be treated independently in statistical analyses and models. Change-O provides several methods for partitioning sequences into clones. 3 Conclusion Along with published methods based on hierarchical clustering (Ademokun et al., 2011; Chen et al., 2010; Glanville et al., 2009), Change-O is a suite of utilities implementing a wide range of B cell users also have the option to employ several published somatic repertoire analysis methods. Together these tools allow researchers hypermutation (SHM) hot/cold-spot targeting models as distance to quickly implement advanced analysis pipelines for large datasets metrics in the clustering methods (Smith et al., 1996; Yaari et al., generated by repertoire sequencing experiments. A simple tab- 2013; Stern et al., 2014). Users may alter the clustering thresholds, delimited file with standardized column names allows for 3358 N.T.Gupta et al. Gadala-Maria,D. et al. (2015) Automated analysis of high-throughput B-cell communication between the utilities and can easily be viewed using sequencing data reveals a high frequency of novel immunoglobulin V gene any spreadsheet application. This format also allows research segment alleles. Proc. Natl. Acad. Sci. USA, 112, 201417683. groups the flexibility to incorporate other analysis tools into their Gae ¨ ta,B. a. et al. (2007) iHMMune-align: hidden Markov model-based align- in-house analysis pipelines by simply adding additional columns of ment and identification of germline genes in rearranged immunoglobulin information to the central file. Change-O, along with pRESTO gene sequences. Bioinformatics, 23, 1580–1587. (Vander Heiden et al., 2014), provides key components of an analyt- Glanville,J. et al. (2009) Precise determination of the diversity of a combina- ical ecosystem that enables sophisticated analysis of high-through- torial antibody library gives insight into the human immunoglobulin reper- put Ig repertoire sequencing datasets. toire. Proc. Natl. Acad. Sci. USA, 106, 20216–20221. Hill,M.O. (1973) Diversity and evenness: a unifying notation and its conse- quences. Ecology, 54, 427. Acknowledgements Kleinstein,S.H. et al. (2003) Estimating hypermutation rates from clonal tree data. J. Immunol., 171, 4639–4649. The authors thank the Yale University Biomedical High Performance McKean,D. et al. (1984) Generation of antibody diversity in the immune re- Computing Center [funded by National Institutes of Health grants RR19895 sponse of BALB/c mice to influenza virus hemagglutinin. Proc. Natl. Acad. and RR029676-01] for use of their computing resources. The authors also Sci. USA, 81, 3180–3184. thank Chris Bolen, Moriah Cohen, Jingli Shan and Sonia Timberlake for test- R Core Team (2015) R: A Language and Environment for Statistical ing Change-O and providing helpful feedback. Computing. R Foundation for Statistical Computing, Vienna, Austria. Robins,H. (2013) Immunosequencing: applications of immune repertoire deep sequencing. Curr. Opin. Immunol., 25, 646–652 Funding Smith,D. et al. (1996) Di- and trinucleotide target preferences of somatic This work was supported by the National Institutes of Health [R01AI104739 mutagenesis in normal and autoreactive B cells. J. Immunol., 156, to S.H.K.; T15LM07056 to N.T.G., T15LM07056 to J.A.V.H. from 2642–2652. National Library of Medicine (NLM)] and by the United States-Israel Stern,J.N.H. et al. (2014) B cells populating the multiple sclerosis Binational Science Foundation [2013395 to G.Y. and S.H.K.]. brain mature in the draining cervical lymph nodes. Sci. Transl. Med., 6, 248ra107. Conflict of Interest: none declared. Uduman,M. et al. (2014) Integrating B cell lineage information into statistical tests for detecting selection in Ig sequences. J. Immunol., 192, 867–874. References Vander Heiden,J.A. et al. (2014) pRESTO: a toolkit for processing high- Ademokun,A. et al. (2011) Vaccination-induced changes in human B-cell rep- throughput sequencing raw reads of lymphocyte receptor repertoires. ertoire and pneumococcal IgM and IgA antibody at different ages. Aging Bioinformatics, 30, 1930–1932 cell, 10, 922–930. Wu,Y.-C.B. et al. (2012) Age-related changes in human peripheral blood IGH Alamyar,E. et al. (2012) IMGT(V) tools for the nucleotide analysis of im- repertoire following vaccination. Front. Immunol., 3, 193. munoglobulin (IG) and T cell receptor (TR) V-(D)-J repertoires, polymorph- Wu,Y.-C.B. et al. (2014) Influence of seasonal exposure to grass pollen on isms, and IG mutations: IMGT/V-QUEST and IMGT/HighV-QUEST for local and peripheral blood IgE repertoires in patients with allergic rhinitis. NGS. Methods Mol. Biol., 882, 569–604. J. Allergy Clin. Immunol., 134, 604–612. Barak,M. et al. (2008) IgTree: creating immunoglobulin variable region gene Yaari,G. et al. (2012) Quantifying selection in high-throughput immunoglobu- lineage trees. J. Immunol. Methods, 338, 67–74. lin sequencing data sets. Nucleic Acids Res., 40, e134. Chen,Z. et al. (2010) Clustering-based identification of clonally-related im- Yaari,G. et al. (2013) Models of somatic hypermutation targeting and substi- munoglobulin gene sequence sets. Immunome Res., 6(Suppl. 1), S4. tution based on synonymous mutations from high-throughput immuno- Csardi,G. and Nepusz,T. (2006) The igraph software package for complex globulin sequencing data. Front. Immunol., 4, 358. network research. InterJournal, Complex Systems, 1695. Ye,J. et al. (2013) IgBLAST: an immunoglobulin variable domain sequence Felsenstein,J. (1989) PHYLIP - Phylogeny inference package (Version 3.2). analysis tool. Nucleic Acids Res., 41(Web Server Issue), W34–W40. Cladistics, 5, 164–166.

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Bioinformatics – Oxford University Press

Published: Jun 10, 2015

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