94. Probabilistic Basecalling 

Terry Speed, Lei Li, Dave Nelson, and Simon Cawley 
University of California, Berkeley 
scawley@stat.berkeley.edu 

Basecalling is the process of converting raw data from automated DNA sequencing machines to a sequence of bases. The process is typically subdivided into the tasks of color separation, mobility shift correction, deconvolution and decoding. A probabilistic model of the process is presented, at center of which lies an Hidden Markov Model (HMM). The class of HMMs is chosen for its flexibility and for the availability of efficient algorithms for training and decoding. Performance of this approach to basecalling is compared with the standard available basecalling algorithms.