DOI: 10.5061/dryad.np5hqc044
title: B cell receptor parent-child pairs for studying somatic hypermutation

description: Somatic hypermutation (SHM) is the diversity-generating process in
 antibody affinity maturation. Probabilistic models of SHM are needed for
 analyzing rare mutations, understanding the selective forces guiding
 affinity maturation, and understanding the underlying biochemical process.
 High throughput data offers the potential to develop and fit SHM models on
 relevant data sets. Here we develop several out-of-frame and
 synonymous-mutations datasets using the strategy of  Spisak, N.,
 Walczak, A. M., & Mora, T. (2020). Learning the heterogeneous
 hypermutation landscape of immunoglobulins from high-throughput repertoire
 data. Nucleic Acids Research, 48(19), 10702–10712.
 https://doi.org/10.1093/nar/gkaa825 for inferring parent-child pairs of
 sequences.  We apply this to data from the following studies:
 Briney, B., Inderbitzin, A., Joyce, C., & Burton, D. R. (2019).
 Commonality despite exceptional diversity in the baseline human antibody
 repertoire. Nature. https://doi.org/10.1038/s41586-019-0879-y Jaffe, D.
 B., Shahi, P., Adams, B. A., Chrisman, A. M., Finnegan, P. M., Raman, N.,
 Royall, A. E., Tsai, F., Vollbrecht, T., Reyes, D. S., Hepler, N. L.,
 & McDonnell, W. J. (2022). Functional antibodies exhibit light
 chain coherence. Nature, 611(7935), 352–357.
 https://doi.org/10.1038/s41586-022-05371-z Tang, C., Krantsevich, A.,
 & MacCarthy, T. (2022). Deep learning model of somatic
 hypermutation reveals importance of sequence context beyond hotspot
 targeting. iScience, 25(1), 103668.
 https://doi.org/10.1016/j.isci.2021.103668 Vergani, S., Korsunsky, I.,
 Mazzarello, A. N., Ferrer, G., Chiorazzi, N., & Bagnara, D.
 (2017). Novel Method for High-Throughput Full-Length IGHV-D-J Sequencing
 of the Immune Repertoire from Bulk B-Cells with Single-Cell Resolution.
 Frontiers in Immunology, 8, 1157. https://doi.org/10.3389/fimmu.2017.01157
description: As described in the Materials and Methods section of
 https://www.biorxiv.org/content/10.1101/2024.11.26.625407v1
description: # B cell receptor parent-child pairs for studying somatic hypermutation
 [https://doi.org/10.5061/dryad.np5hqc044](https://doi.org/10.5061/dryad.np5hqc044) ## Description of the data and file structure This is reprocessed B-cell receptor (BCR) sequence data as described in [https://www.biorxiv.org/content/10.1101/2024.11.26.625407v1](https://www.biorxiv.org/content/10.1101/2024.11.26.625407v1) from the following studies: Briney, B., Inderbitzin, A., Joyce, C., & Burton, D. R. (2019). Commonality despite exceptional diversity in the baseline human antibody repertoire. *Nature* **566**, 393–397. [https://doi.org/10.1038/s41586-019-0879-y](https://doi.org/10.1038/s41586-019-0879-y) Jaffe, D.B., Shahi, P., Adams, B.A. *et al.* (2022). Functional antibodies exhibit light chain coherence. *Nature* **611**, 352–357. [https://doi.org/10.1038/s41586-022-05371-z](https://doi.org/10.1038/s41586-022-05371-z) Spisak, N., Walczak, A. M., & Mora, T. (2020). Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data. *Nucleic Acids Research*, *48*(19), 10702–10712. [https://doi.org/10.1093/nar/gkaa825](https://doi.org/10.1093/nar/gkaa825) Tang, C., Krantsevich, A., & MacCarthy, T. (2022). Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting. *iScience*, *25*(1), 103668. [https://doi.org/10.1016/j.isci.2021.103668](https://doi.org/10.1016/j.isci.2021.103668) Vergani, S., Korsunsky, I., Mazzarello, A. N., Ferrer, G., Chiorazzi, N., & Bagnara, D. (2017). Novel Method for High-Throughput Full-Length IGHV-D-J Sequencing of the Immune Repertoire from Bulk B-Cells with Single-Cell Resolution. *Frontiers in Immunology*, *8*, 1157. [https://doi.org/10.3389/fimmu.2017.01157](https://doi.org/10.3389/fimmu.2017.01157) BCR sequences are clustered into clonal families, and germline sequences are inferred. For each clonal family, phylogenetic tree inference and reconstruction of ancestral sequences are performed. The parent and child sequences of each branch of a tree form a "parent-child pair" (PCP), which is used for training or evaluating models. ### Files and variables #### File: for\_dryad.zip **Description:** Zip file containing all of the data sets. The roles of these data sets can be found in [the `shm_data.py` module of `thrifty-experiments-1`](https://github.com/matsengrp/thrifty-experiments-1/blob/main/shmex/shm_data.py). Below is a summary of each data set: | data set name | description | | ---------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `shmoof` | PCPs of out-of-frame BCR sequences from [Spisak *et al.*](https://doi.org/10.1093/nar/gkaa825), who processed [Briney *et al.*](https://doi.org/10.1038/s41586-019-0879-y) | | `val_curatedShmoofNotbigNoN` | A subset of individuals from the `shmoof` data, and additionally, sites with undetermined nucleotides at the 5' and 3' ends are truncated from sequences. | | `tangshm` | PCPs of out-of-frame BCR sequences from [Tang et al.](https://doi.org/10.1016/j.isci.2021.103668) | | `syntang` | PCPs of productive BCR sequences from [Tang et al.](https://doi.org/10.1016/j.isci.2021.103668) where sites that are **not** 4-fold degenerate are masked with the `N` symbol. Therefore, the unmasked sites only experience synonymous mutations. | | `syn10x` | PCPs of productive BCR sequences from [Jaffe et al.](https://doi.org/10.1038/s41586-022-05371-z) where sites that are **not** 4-fold degenerate are masked with the `N` symbol. Therefore, the unmasked sites only experience synonymous mutations. | Data files are CSV tables where each row corresponds to a PCP. The meaning of the columns is as follows: | column name | description | | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | | `sample_id` | sample label, where a sample corresponds to an individual | | `family` | clonal family label within a sample | | `parent_name`\* | label of the parent sequence | | `parent` | parent nucleotide sequence | | `child_name`\* | label of the child sequence | | `child` | child nucleotide sequence | | `branch_length` | branch length computed in IQ-TREE | | `depth`\* | number of edges away the child sequence is from the naive sequence in the inferred tree | | `distance`\* | sum of branch lengths of the child sequence from the naive sequence in the inferred tree | | `v_gene` | inferred germline V gene | | `cdr1_codon_start`\* | position of the first nucleotide of the first codon in CDR1 | | `cdr1_codon_end`\* | position of the first nucleotide of the last codon in CDR1 | | `cdr2_codon_start`\* | position of the first nucleotide of the first codon in CDR2 | | `cdr2_codon_end`\* | position of the first nucleotide of the last codon in CDR2 | | `cdr3_codon_start`\* | position of the first nucleotide of the first codon in CDR3 (i.e. *after* the conserved Cys); may not make sense in out-of-frame context | | `cdr3_codon_end`\* | position of the first nucleotide of the last codon in CDR3 (i.e. *before* the conserved Trp); may not make sense in out-of-frame context | | `parent_is_naive`\* | True/False whether the parent is the naive sequence | | `child_is_leaf` | True/False whether the child is a leaf node (i.e., observed sequence) | (*) the column is not in the `shmoof` and `val_curatedShmoofNotbigNoN` data files ## Code/software These data are meant to be run using our [thrifty-experiments-1](https://github.com/matsengrp/thrifty-experiments-1) pipeline. Software dependencies, etc, are described there. ## Version changes **2-June-2025:** Added `syntang` dataset described above.