Sequential methods for DNA sequencing

Methods for determining the letters of our genetic code, known as DNA sequencing, currently depend on clever use of electrophoresis to generate data sets indicative of the underlying sequence. Typically the subsequent off-line data processing is carried out less intelligently using a combination of heuristic methods with little mathematical rigour. In this paper, we present a new robust model which is able to accurately predict the effect of the many biological processes which are involved, and moreover, which is usable on-line. Off-line methods have been hampered by the need for processing in as little time as possible after the data is generated; performing the processing on-line has enabled a more advanced algorithm to be used with associated improved performance. The algorithm is framed within a Bayesian probabilistic framework, thereby allowing representation of the random nature of the generative process, and relies on new advances in the burgeoning field of Sequential Monte Carlo Methods to perform the required highly non-linear filtering and model selection operations.