  BACKGROUND: One of the most evident achievements of bioinformatics is the
development of methods that transfer biological knowledge from characterised
proteins to uncharacterised sequences. This mode of protein function assignment
is mostly based on the detection of sequence similarity and the premise that
functional properties are conserved during evolution. Most automatic approaches
developed to date rely on the identification of clusters of homologous proteins
and the mapping of new proteins onto these clusters, which are expected to
share functional characteristics. RESULTS: Here, we inverse the logic of this
process, by considering the mapping of sequences directly to a functional
classification instead of mapping functions to a sequence clustering. In this
mode, the starting point is a database of labelled proteins according to a
functional classification scheme, and the subsequent use of sequence similarity
allows defining the membership of new proteins to these functional classes. In
this framework, we define the Correspondence Indicators as measures of
relationship between sequence and function and further formulate two Bayesian
approaches to estimate the probability for a sequence of unknown function to
belong to a functional class. This approach allows the parametrisation of
different sequence search strategies and provides a direct measure of
annotation error rates. We validate this approach with a database of enzymes
labelled by their corresponding four-digit EC numbers and analyse specific
cases. CONCLUSION: The performance of this method is significantly higher than
the simple strategy consisting in transferring the annotation from the highest
scoring BLAST match and is expected to find applications in automated
functional annotation pipelines.

Quantitative Methods