Probabilistic annotation of protein sequences based on functional classifications

Details Probabilistic annotation of protein sequences based on functional classifications Probabilistic annotation of protein sequences based on functional classifications

2007-09-27

arxiv.org/abs/0709.4425v1

BMC Bioinformatics 6 (2005) 302

Biology

Quantitative Biology

Quantitative Methods

Scientific paper

10.1186/1471-2105-6-302

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.

Affiliated with

Also associated with

No associations

Rating

Probabilistic annotation of protein sequences based on functional classifications does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Probabilistic annotation of protein sequences based on functional classifications, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Probabilistic annotation of protein sequences based on functional classifications will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-482047