For each input sequence the server predicts the possibility of
non-classical or leaderless secretion.
For each input sequence three scores are generated by SecretomeP server
as shown below. The first score is the neural network output score.
This score is the score presented in the
paper. The second number represents the odds that the sequence in fact
is secreted. The third score is an estimated posterior probability that the
sequence is secreted. It is calculated be weighing the odds by a prior
probability of 0.2%, and should thus not be confused with the true probability as the prior probability in your
data set may be entirely different.
Even though SecretomeP is trained to predict
non-classical or leaderless secretion, it usually gives high score
to proteins entering the classical secretory pathway (through
the ER-Golgi apparatus). Therefore, for the proteins in which the presence
of a signal peptide is predicted by
a warning is added to the output.
In the example above the first protein, known to enter the non-classical
is correctly predicted as secretory. The second classical secreted protein,
is correctly predicted as secretory, but a warning that a signal peptide
is predicted is reported. The third protein,
a known nuclear protein, receives a low score, thus is correctly classified.