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Error prone PCR is a method to create a pool of amplicons with some random errors. For the best results the number of mutations and the spectrum of the mutations needs to be controlled, hence the need for a test library. The calculations of a test libray are slightly laborious and are affected by the very small sample size. This calculator tries to overcome these two issues by computing the mutational biases given a starting sequence and list of mutant genotypes, by calculating the mutations per sequence by fitting it to a Poisson distribution and by estimating the errors in the values. In particular, the errors are calculated using the assumption that a mutation and its complementary are equally likely in light of the double helix nature of DNA (e.g. A to G on one strand will result in T to C on the other). For the specific formulae used see this note about propagating errors.
The program can calculate mutation frequencies from the list of mutations found and the template sequence or it can also accept the frequencies directly. The 'Demo' values are from an actual experiment.

Mutational load

The simplest and most commonly employed estimate of the frequency of mutations per sequence is the average of the point mutations per sequence (m), however due to the small sample size this may be off.

The distribution of number of mutations per sequence follows a PCR distribution, which can approximated with a Poisson distribution (Sun, 1995).

Due to the fact that calculating the PCR efficiency using the total set PCR cycles is misleading, the distribution is omitted here, but would give similar results (cf. FAQ)

In the latter, the mean and the variance are the same (λ —unrelated to PCR efficiency—). The sample average and variance may differ, especially at low sampling. The number to trust the most is the λ_Poisson.