for substitution types while the latter only utilized information in one location thus the background error rate estimation is off. For one sample low-frequency SNV calling, UDT-Seq [15] tabulated the error rate based on substitution types, strand and location on the read to derive an empirical background error rate, then use binomial model to distinguish signal from error, and the candidate SNVs are further refined by 7 filters. This method is context-aware but also ad-hoc, thus the ability to adapt to different sequencing technologies is limited. A brief summary of the tools mentioned above is included in Additional file 1.
