repgenHMM: a dynamic programming tool to infer the rules of immune receptor generation from sequence data

Abstract

Motivation: The diversity of the immune repertoire is initially generated by random rearrangements of the receptor gene during early T and B cell development. Rearrangement scenarios are composed of random events-choices of gene templates, base pair deletions and insertionsdescribed by probability distributions. Not all scenarios are equally likely, and the same receptor sequence may be obtained in several different ways. Quantifying the distribution of these rearrangements is an essential baseline for studying the immune system diversity. Inferring the properties of the distributions from receptor sequences is a computationally hard problem, requiring enumerating every possible scenario for every sampled receptor sequence.Results: We present a Hidden Markov model, which accounts for all plausible scenarios that can generate the receptor sequences. We developed and implemented a method based on the Baum-Welch algorithm that can efficiently infer the parameters for the different events of the rearrangement process. We tested our software tool on sequence data for both the alpha and beta chains of the T cell receptor. To test the validity of our algorithm, we also generated synthetic sequences produced by a known model, and confirmed that its parameters could be accurately inferred back from the sequences. The inferred model can be used to generate synthetic sequences, to calculate the probability of generation of any receptor sequence, as well as the theoretical diversity of the repertoire. We estimate this diversity to be %10 23 for human T cells. The model gives a baseline to investigate the selection and dynamics of immune repertoires. Availability and implementation: Source code and sample sequence files are available at https://bit bucket.org/yuvalel/repgenhmm/downloads.

Insights into immune system development and function from mouse T-cell repertoires

“…In previous work, we used human sequence repertoires to develop methods for inferring the details of the stochastic process of VDJ recombination (9,38) and for characterizing the statistical effects of thymic selection (10). In this paper, we apply these methods to T-cell β-chain sequence data collected from mice at several stages of development, from embryos to young adults, to study the T-cell repertoire maturation process.…”

The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

“…59 There is also clear evidence of interaction between gene use and trimming length. 4,60 The rearrangement processes change with age, a phenomenon recently quantified in mouse. 61 In addition to the usual rearrangement process, oddities such as VH replacement 62-64 and inverted and multiple D genes do occur, although recent analyses indicate that these are rare in the overall repertoire.…”

“…Indeed, many probabilistic models have been formulated, with the hidden Markov model (HMM) framework being particularly popular. 6,60,65,67,68 Various implementations of the HMM differ in the parameterization of gene choice and trimming distributions, with the trend being toward parameter-rich categorical distributions for trimming. Such rich distributions are justified by the observation that although trimming distributions are different between genes, strong concordance between individuals shows that the models are not simply fitting noise.…”

“…Such rich distributions are justified by the observation that although trimming distributions are different between genes, strong concordance between individuals shows that the models are not simply fitting noise. 6,60,69 Recent work has extended this to a more general modeling framework expressible in terms of an arbitrary Bayesian network. 69 For the insertion sequences, applying an HMM has shown dependence of the next base on the previous one.…”

“…69 For the insertion sequences, applying an HMM has shown dependence of the next base on the previous one. 60 Despite substantial progress, there is still work to be done describing the rearrangement process using probabilistic models.…”