BACKGROUND: Next-generation DNA sequencing platforms are capable of generating millions of reads in a matter of days at rapidly reducing costs. Despite its proliferation and technological improvements, the performance of next-generation sequencing remains adversely affected by the imperfections in the underlying biochemical and signal acquisition procedures. To this end, various techniques, including statistical methods, are used to improve read lengths and accuracy of these systems. Development of high performing base calling algorithms that are computationally efficient and scalable is an ongoing challenge. RESULTS: We develop model-based statistical methods for fast and accurate base calling in Illumina's next-generation sequencing platforms. In particular, we propose a computationally tractable parametric model which enables dynamic programming formulation of the base calling problem. Forward-backward and soft-output Viterbi algorithms are developed, and their performance and complexity are investigated and compared with the existing state-of-the-art base calling methods for this platform. A C code implementation of our algorithm named Softy can be downloaded from https://sourceforge.net/projects/dynamicprog. CONCLUSION: We demonstrate high accuracy and speed of the proposed methods on reads obtained using Illumina's Genome Analyzer II and HiSeq2000. In addition to performing reliable and fast base calling, the developed algorithms enable incorporation of prior knowledge which can be utilized for parameter estimation and is potentially beneficial in various downstream applications.

MOTIVATION: Next-generation DNA sequencing platforms are becoming increasingly cost-effective and capable of providing enormous number of reads in a relatively short time. However, their accuracy and read lengths are still lagging behind those of conventional Sanger sequencing method. Performance of next-generation sequencing platforms is fundamentally limited by various imperfections in the sequencing-by-synthesis and signal acquisition processes. This drives the search for accurate, scalable and computationally tractable base calling algorithms capable of accounting for such imperfections. RESULTS: Relying on a statistical model of the sequencing-by-synthesis process and signal acquisition procedure, we develop a computationally efficient base calling method for Illumina's sequencing technology (specifically, Genome Analyzer II platform). Parameters of the model are estimated via a fast unsupervised online learning scheme, which uses the generalized expectation-maximization algorithm and requires only 3 s of running time per tile (on an Intel i7 machine @3.07GHz, single core)-a three orders of magnitude speed-up over existing parametric model-based methods. To minimize the latency between the end of the sequencing run and the generation of the base calling reports, we develop a fast online scalable decoding algorithm, which requires only 9 s/tile and achieves significantly lower error rates than the Illumina's base calling software. Moreover, it is demonstrated that the proposed online parameter estimation scheme efficiently computes tile-dependent parameters, which can thereafter be provided to the base calling algorithm, resulting in significant improvements over previously developed base calling methods for the considered platform in terms of performance, time/complexity and latency. AVAILABILITY: A C code implementation of our algorithm can be downloaded from http://www.cerc.utexas.edu/OnlineCall/.