AIRR repertoire results

The repertoires per subject after clonal analysis can be found in the subdirectory clonal_analysis/define_clones/all_reps_clone_report/repertoires.

Additionally, html reports summarizing the results are provided:

A full description of the pipeline results can be found on the Output section of the nf-core/airrflow website.

Number of sequences

Sequence assembly steps

Number of reads for each of the samples and number of sequences left after performing sequence assembly and alignment to reference data. The full table can be found under Table_sequences_assembly.

V(D)J gene assignment and QC

Number of sequences for each of the samples after each of the downstream filtering and clonal analysis steps. The full table can be found under Table_sequences_assembled.

V gene usage

V gene family usage

The V gene usage (in percentage) in each of the samples is represented below. All plots and tables can be found here.

Gene family usage is normalized by the number of clones.

V gene usage

The V gene usage (in percentage) in each of the samples is represented below. All plots and tables can be found here.

By clones

By sequences

Citations

If you use nf-core/airrflow for your analysis, please cite it using the following DOI: 10.5281/zenodo.3607408

In addition, citations for the tools and data used in this pipeline are as follows:

  • pRESTO

    Vander Heiden, J. A., Yaari, G., Uduman, M., Stern, J. N. H., O’Connor, K. C., Hafler, D. A., … Kleinstein, S. H. (2014). pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics, 30(13), 1930–1932.

  • SHazaM, Change-O

    Gupta, N. T., Vander Heiden, J. A., Uduman, M., Gadala-Maria, D., Yaari, G., & Kleinstein, S. H. (2015). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data: Table 1. Bioinformatics, 31(20), 3356–3358.

  • IgBLAST

    Ye, J., Ma, N., Madden, T. L., & Ostell, J. M. (2013). IgBLAST: An immunoglobulin variable domain sequence analysis tool. Nucleic Acids Research, 41(Web Server issue), W34.

  • Alakazam

    Stern, J. N. H., Yaari, G., Vander Heiden, J. A., Church, G., Donahue, W. F., Hintzen, R. Q., … O’Connor, K. C. (2014). B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Science Translational Medicine, 6(248), 248ra107.

  • SCOPer

    Nouri N, Kleinstein S (2018). “A spectral clustering-based method for identifying clones from high-throughput B cell repertoire sequencing data.” Bioinformatics, i341-i349.

    Nouri N, Kleinstein S (2020). “Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data.” PLOS Computational Biology, 16(6), e1007977.

    Gupta N, Adams K, Briggs A, Timberlake S, Vigneault F, Kleinstein S (2017). “Hierarchical clustering can identify B cell clones with high confidence in Ig repertoire sequencing data.” The Journal of Immunology, 2489-2499.

  • Dowser

    Hoehn K, Pybus O, Kleinstein S (2022). “Phylogenetic analysis of migration, differentiation, and class switching in B cells.” PLoS Computational Biology.

  • IgPhyML

    Hoehn K, Van der Heiden J, Zhou J, Lunter G, Pybus O, Kleinstein S (2019). “Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination.” PNAS.

  • RAxML

    Stamatakis A. (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30(9): 1312-1313.

  • TIgGER

    Gadala-maria, D., Yaari, G., Uduman, M., & Kleinstein, S. H. (2015). Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proceedings of the National Academy of Sciences, 112(8), 1–9.

  • FastQC

  • Fastp

    Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics. 2018 Sept 1; 34(17):i884–i890.

  • MultiQC

    Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

Software packaging/containerisation tools

  • Anaconda

    Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

  • Bioconda

    Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

  • BioContainers

    da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

  • Docker

  • Singularity

    Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.