842 research outputs found
Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data
B cells develop high affinity receptors during the course of affinity
maturation, a cyclic process of mutation and selection. At the end of affinity
maturation, a number of cells sharing the same ancestor (i.e. in the same
"clonal family") are released from the germinal center, their amino acid
frequency profile reflects the allowed and disallowed substitutions at each
position. These clonal-family-specific frequency profiles, called "substitution
profiles", are useful for studying the course of affinity maturation as well as
for antibody engineering purposes. However, most often only a single sequence
is recovered from each clonal family in a sequencing experiment, making it
impossible to construct a clonal-family-specific substitution profile. Given
the public release of many high-quality large B cell receptor datasets, one may
ask whether it is possible to use such data in a prediction model for
clonal-family-specific substitution profiles. In this paper, we present the
method "Substitution Profiles Using Related Families" (SPURF), a penalized
tensor regression framework that integrates information from a rich assemblage
of datasets to predict the clonal-family-specific substitution profile for any
single input sequence. Using this framework, we show that substitution profiles
from similar clonal families can be leveraged together with simulated
substitution profiles and germline gene sequence information to improve
prediction. We fit this model on a large public dataset and validate the
robustness of our approach on an external dataset. Furthermore, we provide a
command-line tool in an open-source software package
(https://github.com/krdav/SPURF) implementing these ideas and providing easy
prediction using our pre-fit models.Comment: 23 page
Application of Protein Membranes with Magnetic Nanoparticles for Co-Cultivation of Cell Cultures by Levitation in a Magnetic Field
Financial support was received from Ural Center for Modern Nanotechnologies of the Ural Federal University (UCMN, Yekaterinburg, Russia). This work was supported by the Russian Foundation for Basic Research, project 19-74-00081
Survival analysis of DNA mutation motifs with penalized proportional hazards
Antibodies, an essential part of our immune system, develop through an
intricate process to bind a wide array of pathogens. This process involves
randomly mutating DNA sequences encoding these antibodies to find variants with
improved binding, though mutations are not distributed uniformly across
sequence sites. Immunologists observe this nonuniformity to be consistent with
"mutation motifs", which are short DNA subsequences that affect how likely a
given site is to experience a mutation. Quantifying the effect of motifs on
mutation rates is challenging: a large number of possible motifs makes this
statistical problem high dimensional, while the unobserved history of the
mutation process leads to a nontrivial missing data problem. We introduce an
-penalized proportional hazards model to infer mutation motifs and
their effects. In order to estimate model parameters, our method uses a Monte
Carlo EM algorithm to marginalize over the unknown ordering of mutations. We
show that our method performs better on simulated data compared to current
methods and leads to more parsimonious models. The application of proportional
hazards to mutation processes is, to our knowledge, novel and formalizes the
current methods in a statistical framework that can be easily extended to
analyze the effect of other biological features on mutation rates
A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis.
The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial "V(D)J" rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or "naive") sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling
An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics
Phylodynamics focuses on the problem of reconstructing past population size
dynamics from current genetic samples taken from the population of interest.
This technique has been extensively used in many areas of biology, but is
particularly useful for studying the spread of quickly evolving infectious
diseases agents, e.g.,\ influenza virus. Phylodynamics inference uses a
coalescent model that defines a probability density for the genealogy of
randomly sampled individuals from the population. When we assume that such a
genealogy is known, the coalescent model, equipped with a Gaussian process
prior on population size trajectory, allows for nonparametric Bayesian
estimation of population size dynamics. While this approach is quite powerful,
large data sets collected during infectious disease surveillance challenge the
state-of-the-art of Bayesian phylodynamics and demand computationally more
efficient inference framework. To satisfy this demand, we provide a
computationally efficient Bayesian inference framework based on Hamiltonian
Monte Carlo for coalescent process models. Moreover, we show that by splitting
the Hamiltonian function we can further improve the efficiency of this
approach. Using several simulated and real datasets, we show that our method
provides accurate estimates of population size dynamics and is substantially
faster than alternative methods based on elliptical slice sampler and
Metropolis-adjusted Langevin algorithm
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