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More details are needed.

Logging support is incomplete/nonfunctional, this needs to be implemented using the python standard logging module.

Model file compress with a factor of 10 to 20 with gzip, consider adding this directly to MGLEX or ensure streamability using, for instance, BASH syntax.

The code is currently slow, we can do much better using

• removing assertions in non-debug mode
• scaling data at the beginning to avoid likelihood calculations
• speeding up vector arithmetics with more advanced python packages/methods
• avoiding the time for parsing the data over and over again

If all likelihoods are zero (log-likelihood is inf), the corresponding row becomes nan after posterior transformation. It should stay zero instead!

We are lacking a submodel to encode information from assembly such as a graph or linked read information. Such a model will effectively transport scaffolding information from the assembler to MGLEX.

Implement adaptive smoothing (aka Laplace smoothing) in all models so that unlikely classifications are always > 0.