Benjamin Audit

Are splicing mutations the most frequent cause of hereditary disease

Disease‐causing point mutations are assumed to act predominantly through subsequent individual changes in the amino acid sequence that impair the normal function of proteins. However, point mutations can have a more dramatic effect by altering the splicing pattern of the gene. Here, we describe an approach to estimate the overall importance of splicing mutations. This approach takes into account the complete set of genes known to be involved in disease and suggest that, contrary to current assumptions, many mutations causing disease may actually be affecting the splicing pattern of the genes.

Genome sequences and great expectations

To assess how automatic function assignment will contribute to genome annotation in the next five years, we have performed an analysis of 31 available genome sequences. An emerging pattern is that function can be predicted for almost two-thirds of the 73,500 genes that were analyzed. Despite progress in computational biology, there will always be a great need for large-scale experimental determination of protein function.

Probabilistic annotation of protein sequences based on functional classifications

BackgroundOne 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.ResultsHere, 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.ConclusionThe 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.

Beyond 100 genomes

By the end of 2002, we witnessed the landmark submission of the 100th complete genome sequence in the databases. An overview of these genomes reveals certain interesting trends and provides valuable insights into possible future developments.

CORRIE: enzyme sequence annotation with confidence estimates

Using a previously developed automated method for enzyme annotation, we report the re-annotation of the ENZYME database and the analysis of local error rates per class. In control experiments, we demonstrate that the method is able to correctly re-annotate 91% of all Enzyme Classification (EC) classes with high coverage (755 out of 827). Only 44 enzyme classes are found to contain false positives, while the remaining 28 enzyme classes are not represented. We also show cases where the re-annotation procedure results in partial overlaps for those few enzyme classes where a certain inconsistency might appear between homologous proteins, mostly due to function specificity. Our results allow the interactive exploration of the EC hierarchy for known enzyme families as well as putative enzyme sequences that may need to be classified within the EC hierarchy. These aspects of our framework have been incorporated into a web-server, called CORRIE, which stands for Correspondence Indicator Estimation and allows the interactive prediction of a functional class for putative enzymes from sequence alone, supported by probabilistic measures in the context of the pre-calculated Correspondence Indicators of known enzymes with the functional classes of the EC hierarchy. The CORRIE server is available at: http://www.genomes.org/services/corrie/.