Abstract
BackgroundNext-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. Accuracy and lengths of their reads, however, are yet to surpass those provided by the conventional Sanger sequencing method. This motivates the search for computationally efficient algorithms capable of reliable and accurate detection of the order of nucleotides in short DNA fragments from the acquired data.ResultsIn this paper, we consider Illumina’s sequencing-by-synthesis platform which relies on reversible terminator chemistry and describe the acquired signal by reformulating its mathematical model as a Hidden Markov Model. Relying on this model and sequential Monte Carlo methods, we develop a parameter estimation and base calling scheme called ParticleCall. ParticleCall is tested on a data set obtained by sequencing phiX174 bacteriophage using Illumina’s Genome Analyzer II. The results show that the developed base calling scheme is significantly more computationally efficient than the best performing unsupervised method currently available, while achieving the same accuracy.ConclusionsThe proposed ParticleCall provides more accurate calls than the Illumina’s base calling algorithm, Bustard. At the same time, ParticleCall is significantly more computationally efficient than other recent schemes with similar performance, rendering it more feasible for high-throughput sequencing data analysis. Improvement of base calling accuracy will have immediate beneficial effects on the performance of downstream applications such as SNP and genotype calling.ParticleCall is freely available at https://sourceforge.net/projects/particlecall.
Highlights
Next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, enabling routine sequencing tasks and taking us one step closer to personalized medicine
A modified version of the BaseCall algorithm named naiveBayesCall [9] performs base calling in a much more efficient way, but its accuracy deteriorates. Both BayesCall and naiveBayesCall rely on expectation-maximization (EM) framework that employs a Markov chain Monte Carlo (MCMC) sampling strategy to estimate the parameters of the statistical model describing the signal acquisition process
In section Results and discussion, we demonstrate the performance of the ParticleCall algorithm that relies on the Monte Carlo implementation of the EM algorithm (MCEM) parameter estimation scheme
Summary
Next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, enabling routine sequencing tasks and taking us one step closer to personalized medicine. Accuracy and lengths of their reads, are yet to surpass those provided by the conventional Sanger sequencing method This motivates the search for computationally efficient algorithms capable of reliable and accurate detection of the order of nucleotides in short DNA fragments from the acquired data. A modified version of the BaseCall algorithm named naiveBayesCall [9] performs base calling in a much more efficient way, but its accuracy deteriorates (albeit remains better than Bustard’s). Both BayesCall and naiveBayesCall rely on expectation-maximization (EM) framework that employs a Markov chain Monte Carlo (MCMC) sampling strategy to estimate the parameters of the statistical model describing the signal acquisition process. Accurate and practically feasible parameter estimation and base-calling remain a challenge that needs to be addressed
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