RNA Home | Aalberts' Lab |
---|

A few technical details about our

1. Currently, only 7 letter DNA sequences (a,c,g,t) are permitted.

2. The method currently defaults to use the Yeo and Burge real and decoy data sets for human pre-mRNA donor splice sites. (The YB files list the last three bases of the exon and the first four bases of the intron after the conserved GT sequence which is omitted; for example,

3. When cross-validating, one-third of the total input data is randomly selected and reserved for testing; the remaining data can be used for training. We have found that it is interesting to study how the performance of the method scales with training data set size --- this reveals whether the method's performance has saturated --- so we allow the user to specify a percentage (0 to 100 percent) of the data to be used for training.

4. The performance measure the program reports is the optimal binary Pearson correlation coefficient between prediction and reality:

A common bioinformatics problem is how to determine whether a
local primary sequence *X* is more like examples in
real or decoy training data sets.

Many methods (Weight Matrix Method, Weight Array Method, Markov Models) have been developed to address this problem. A WMM-type approach uses the probabilities of inidividual letters [p(e)>p(t)>...>p(z)] at different positions. Correlations [e.g., qu or th] are included in more sophisticated models.

Our **Primary Sequence Ranking methods**
take a complementary
approach, more akin to referring to a dictionary to see whether the word
is known.
And if the real and decoy dictionaries are small (abridged), we also offer
several approaches to enhance the lexicon by making substitution mutations.
In the strange world of bioinformatics, local sequence *X* can appear
in both real and decoy dictionaries.
(In fact, in our paper
we show that in the case of pre-mRNA donor splice signals,
96% of *real* 7 letter sequences also appear as *decoys*
elsewhere where they are not spliced.)

The **PSR** approach is very simple.
We rank order sequences by the likelihood *X* is true or '+':

To accomodate finite training data, we also provide ways of enhancing
the data by including pseudocounts from nearest-neighbor *X'*
and next-nearest-neighbor *X''* sequences (one and two
substition mutations from *X*) .

**Quantifying Optimal Accuracy of Local Primary Sequence
Bioinformatics Methods**, Daniel P. Aalberts, Eric G. Daub '04, and
Jesse W. Dill '04, *Bioinformatics* **21**, 3347-3351 (2005).