A Bayesian framework for molecular strain identification from mixed diagnostic samples

Abstract

We provide a mathematical formulation and develop a computational framework for identifying multiple strains of microorganisms from mixed samples of DNA. Our method is applicable in public health domains where efficient identification of pathogens is paramount, e.g., for the monitoring of disease outbreaks. We formulate strain identification as an inverse problem that aims at simultaneously estimating a binary matrix (encoding presence or absence of mutations in each strain) and a real-valued vector (representing the mixture of strains) such that their product is approximately equal to the measured data vector. The problem at hand has a similar structure to blind deconvolution, except for the presence of binary constraints, which we enforce in our approach. Following a Bayesian approach, we derive a posterior density. We present two computational methods for solving the non-convex maximum a posteriori estimation problem. The first one is a local optimization method that is made efficient and scalable by decoupling the problem into smaller independent subproblems, whereas the second one yields a global minimizer by converting the problem into a convex mixed-integer quadratic programming problem. The decoupling approach also provides an efficient way to integrate over the posterior. This provides useful information about the ambiguity of the underdetermined problem and, thus, the uncertainty associated with numerical solutions. We evaluate the potential and limitations of our framework in silico using synthetic and experimental data with available ground truths.

Development of a new barcode-based, multiplex-PCR, next-generation-sequencing assay and data processing and analytical pipeline for multiplicity of infection detection of Plasmodium falciparum

“…A previously developed mathematical algorithm, StrainRecon [ 17 ], was used for identifying haplotypes and enumerating strains based on frequency results of individual 24-SNP barcodes generated from NGS. Briefly, the basic StrainRecon algorithm accepts a vector of SNP frequencies as input and a parameter n denoting the anticipated number of strains.…”

“…Whole genome deep sequencing has improved MOI estimation at the population level by detecting genomic signatures and minority strains of parasites through newly developed analytical tools, StrainRecon [ 17 ] and DEploid [ 18 ]. The main difference between StrainRecon and DEploid is that DEploid requires a reference panel of strains to be provided as a prior for potential haplotypes present in the sample, whereas StrainRecon, discussed below, requires no templates or priors.…”

“…Described below is the development and validation of an advanced laboratory assay and unique data processing pipeline with B4Screening pathway for strain disambiguation using three multiplex PCRs followed by MiSeq deep sequencing based on the published panel of 24 SNPs of P. falciparum [ 19 ]. Further validation of MOI estimation was conducted on field samples collected over time from Kenya using the recently published algorithm StrainRecon [ 17 ] and a novel threshold-calibrated MOI estimation method, StrainRecon Thresholding for Infection Multiplicity (STIM) is presented below. The methods developed in this study offer malaria researchers the ability to target multiple genetic loci in sufficient depth to link across sites based on frequency of SNP reads at each site using the non-template approach.…”

“…The methods developed in this study offer malaria researchers the ability to target multiple genetic loci in sufficient depth to link across sites based on frequency of SNP reads at each site using the non-template approach. The consistency of these SNP frequencies determines the ability of the algorithm to successfully assign frequencies to haplotypes and disentangle samples that comprise of multiple strains [ 17 ].…”

Metagenomic Approaches for Public Health Surveillance of Foodborne Infections: Opportunities and Challenges

“…These proportions can be used to group alleles from different genes by their strain of origin. This concept is illustrated in Figure 2 and one potential implementation is described in Mustonen et al (2018). This approach may also distinguish whether reads generated from mutational AMR determinants belong to the pathogen or background bacteria since studies have shown that health adults carry AMR genes in their gut microflora (Fitzpatrick and Walsh, 2016).…”