In a standard RNA, base frequencies are not equally
distributed. Especially in the archea subclass we find
extremely G+C rich sequences.
This yielded in a couple of new rate corrections, algorithms
and programs which:
-
calculate the average G+C content of all/two sequences
-
correct the distance.
But further research showed us that the G+C frequencies are
not equally distributed within a sequence. Especially helical
parts have a significant higher G+C content than non
helical parts.
One strait forward algorithm would calculate each frequency
independently for each column.
Especially for small datasets the resulting frequencies would
look like random data, as too few examples are analyzed.
In ARB we implemented a combination of the 2 approaches.
Lets say we want to estimate a Parameter 'P' with
a maximum variance 'maxvar', so we need a minimum
samples 'minsap'.
-
All sequence positions a clustered according to
-
helical/non helical region
-
variability
-
The size of the cluster is choosen with respect
to the variability of the sequences to get a
minimum of independent events.
-
The final parameter estimate for a column is a
weighted sum between the estimate for the
cluster and the estimate for the single position.
You can give your favorite method a higher weight by
controlling the smoothing parameter:
-
Less smoothing -> independent parameter estimates
-
Much smoothing -> clustered parameter estimates
To get a good tree we recommend you to try all selections.
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