Rapid growth of genetic databases means huge savings from improvements in their data compression, what requires better inexpensive statistical models. This article proposes automatized optimizations e.g. of Markov-like models, especially context binning and model clustering. While it is popular to just remove low bits of the context, proposed context binning automatically optimizes such reduction as tabled: state=bin[context] determining probability distribution, this way extracting nearly all useful information also from very large contexts, into a relatively small number of states. The second proposed approach: model clustering uses k-means clustering in space of general statistical models, allowing to optimize a few models (as cluster centroids) to be chosen e.g. separately for each read. There are also briefly discussed some adaptivity techniques to include data non-stationarity.