Search

BCR sequences diversify through mutations introduced by purpose-built cellular machinery. A recent paper has concluded that a “templated mutagenesis” process is a major contributor to somatic hypermutation and therefore Ig diversification in mice and humans. In this proposed process, mutations in the Ig locus are introduced by copying short segments from other Ig genes. If true, this would overturn decades of research on B cell diversification and would require a complete rewrite of computational methods to analyze B cell data for these species. In this paper, we re-evaluate the templated mutagenesis hypothesis. By applying the original inferential method using potential donor templates absent from B cell genomes, we obtain estimates of the methods’ false positive rates. We find false positive rates of templated mutagenesis in murine and human Ig loci that are similar to or even higher than the original rate inferences, and by considering the bases used in substitution, we find evidence that if templated mutagenesis occurs, it is at a low rate. We also show that the statistically significant results in the original paper can easily result from a slight misspecification of the null model.

Mutations that compromise mismatch repair (MMR) or DNA polymerase exonuclease domains produce mutator phenotypes capable of fueling cancer evolution. Tandem defects in these pathways dramatically increase mutation rate. Here, we model how mutator phenotypes expand genetic heterogeneity in budding yeast cells using a single-cell resolution approach that tallies all replication errors arising from individual divisions. The distribution of count data from cells lacking MMR and polymerase proofreading was broader than expected for a single rate, consistent with volatility of the mutator phenotype. The number of mismatches that segregated to the mother and daughter cells after the initial round of replication co-varied, suggesting that mutagenesis in each division is governed by a different underlying rate. The distribution of “fixed” mutation counts that cells inherit is further broadened by an unequal sharing of mutations due to semiconservative replication and Mendelian segregation. Modeling suggests that this asymmetric segregation may diversify mutation burden in mutator-driven tumors.

Probabilistic modeling is fundamental to the statistical analysis of complex data. In addition to forming a coherent description of the data-generating process, probabilistic models enable parameter inference about given data sets. This procedure is well-developed in the Bayesian perspective, in which one infers probability distributions describing to what extent various possible parameters agree with the data. In this paper we motivate and review probabilistic modeling for adaptive immune receptor repertoire data then describe progress and prospects for future work, from germline haplotyping to adaptive immune system deployment across tissues. The relevant quantities in immune sequence analysis include not only continuous parameters such as gene use frequency, but also discrete objects such as B cell clusters and lineages. Throughout this review, we unravel the many opportunities for probabilistic modeling in adaptive immune receptor analysis, including settings for which the Bayesian approach holds substantial promise (especially if one is optimistic about new computational methods). From our perspective the greatest prospects for progress in probabilistic modeling for repertoires concern ancestral sequence estimation for B cell receptor lineages, including uncertainty from germline genotype, rearrangement, and lineage development., Comment: in press, Immunological Reviews

Stochastic precipitation generators (SPGs) produce synthetic precipitation data and are frequently used to generate inputs for physical models throughout many scientific disciplines. Especially for large data sets, statistical parameter estimation is difficult due to the high dimensionality of the likelihood function. We propose techniques to estimate SPG parameters for spatiotemporal precipitation occurrence based on an emerging set of methods called Approximate Bayesian computation (ABC), which bypass the evaluation of a likelihood function. Our statistical model employs a thresholded Gaussian process that reduces to a probit regression at single sites. We identify appropriate ABC penalization metrics for our model parameters to produce simulations whose statistical characteristics closely resemble those of the observations. Spell length metrics are appropriate for single sites, while a variogram-based metric is proposed for spatial simulations. We present numerical case studies at sites in Colorado and Iowa where the estimated statistical model adequately reproduces local and domain statistics.

The adaptive immune system generates an incredible diversity of antigen receptors for B and T cells to keep dangerous pathogens at bay. The DNA sequences coding for these receptors arise by a complex recombination process followed by a series of productivity-based filters, as well as affinity maturation for B cells, giving considerable diversity to the circulating pool of receptor sequences. Although these datasets hold considerable promise for medical and public health applications, the complex structure of the resulting adaptive immune receptor repertoire sequencing (AIRR-seq) datasets makes analysis difficult. In this paper we introduce sumrep, an R package that efficiently performs a wide variety of repertoire summaries and comparisons, and show how sumrep can be used to perform model validation. We find that summaries vary in their ability to differentiate between datasets, although many are able to distinguish between covariates such as donor, timepoint, and cell type for BCR and TCR repertoires. We show that deletion and insertion lengths resulting from V(D)J recombination tend to be more discriminative characterizations of a repertoire than summaries that describe the amino acid composition of the CDR3 region. We also find that state-of-the-art generative models excel at recapitulating gene usage and recombination statistics in a given experimental repertoire, but struggle to capture many physiochemical properties of real repertoires.

Probabilistic models of adaptive immune repertoire sequence distributions can be used to infer the expansion of immune cells in response to stimulus, differentiate genetic from environmental factors that determine repertoire sharing, and evaluate the suitability of various target immune sequences for stimulation via vaccination. Classically, these models are defined in terms of a probabilistic V(D)J recombination model which is sometimes combined with a selection model. In this paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by deep neural networks to T cell receptor (TCR) repertoires. We show that simple VAE models can perform accurate cohort frequency estimation, learn the rules of VDJ recombination, and generalize well to unseen sequences. Further, we demonstrate that VAE-like models can distinguish between real sequences and sequences generated according to a recombination-selection model, and that many characteristics of VAE-generated sequences are similar to those of real sequences.