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Figure 5 | BMC Bioinformatics

Figure 5

From: A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector

Figure 5

Results using synthetic ~Poisson data. Synthetic data with Poisson distributed length statistics is shown in the upper trace. Emission broadening is introduced with an emission variance amplification factor of 4.5. This effectively broadens the noise band (thickness) seen in the upper trace by a factor of 4.5, which leads to a blurring between the upper and lower levels of blockade since the noise bands now overlap (i.e., here we purposefully over-project to lead to the typical toggling cross-over instability shown in the bottom trace). The middle trace shows the clean, highly accurate Viterbi parsing into the appropriate levels that is obtained with use of the HMM-with-Duration implementation. The lower trace shows the Viterbi parse with a simple HMM, that is uninformed about the underlying length distributions, thus giving rise to a Viterbi traceback parse that fails to penalize unlikely, very short duration, blockade events (seen as the unstable, rapid level-projection toggles).

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