pockerman / hidden_markov_modeling Goto Github PK

We will be using pomgranate

we will use pomgranate.HMM module

we will use pomgranate.HMM module

We will be using pomgranate.HMM module

we will use pomgranate.HMM module

Develop train/fit-predict pipeline with pomgranate.HMM model and our data where the model has three states NORMAL-INSERT-DELETE

we need to think about how we model the expected distribution for each state:

i,e normal read depth, can be model as normal, poisson, negative binomial. with high enough read depth it approximates to normal, except can't take negative values, previously we were performing a negative binomial transformation to give a normal distribution. that was so we could use the distribution of the difference between 2 normal distributions. However we could model directly as negative binomial.

duplications and a single copy deletion will be similar but have different parameters.

2 copy deletions will be near uniform 0,

Alternatively we could use the distribution for the normal copy number as the only distribution and base the probabilities on distance from mean of that, i.e > mean and low p = high p of duplication.

We need a quality threshold for every read.

• Affects: json config and some scripts
• disambiguate: when the read is rejected does it get replaced from the reference sequence of simply ignored?

we will be using pomgranate.HMM module