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databases make it an uneasy task, considering that any given application will require careful
consideration regarding the best balance between sensitivity and specificity. Otherwise,
many of these methods use different approaches and focus on specific cell types and/or
signal sorting pathways. Some of the most popular tools for predicting protein signals use
Neural Networks (e.g. SignalP-NN and Predotar at http://www.inra.fr/predotar), Hidden
Markov models (e.g. SignalP-MM), Weight Matrices (e.g. Emboss Pscan at
http://www.hgmp.mrc.ac.uk) and/or Integrated Methods (e.g. TargetP and Psort), being the
last one an integrated approach of the methods mentioned above.
Our results suggest that it is advisable to compare the output of several programs to
increase the reliability of the overall data and to make a final decision. As reported earlier,
some programs, like SignalP, are useful for initial detection of signal peptides, but this
initial approach may have some shortcomings, as for example when predicting signal
peptides of type II membrane proteins, that prompt for further analysis with other
prediction methods/programs. However, the sequence analysis methods described above,
when used in meaningful combinations, can generally provide reliable predictions.
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