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In this work we present a flexible, probabilistic and reference-free methodof error correction for high throughput DNA sequencing data. The key is toexploit the high coverage of sequencing data and model short sequence outputsas independent realizations of a Hidden Markov Model (HMM). We pose the problemof error correction of reads as one of maximum likelihood sequence detectionover this HMM. While time and memory considerations rule out an implementationof the optimal Baum-Welch algorithm (for parameter estimation) and the optimalViterbi algorithm (for error correction), we propose low-complexity approximateversions of both. Specifically, we propose an approximate Viterbi and asequential decoding based algorithm for the error correction. Our results showthat when compared with Reptile, a state-of-the-art error correction method,our methods consistently achieve superior performances on both simulated andreal data sets.

PREMIER - PRobabilistic Error-correction using Markov Inference in Errored Reads