Filtering erroneous protein annotation.

MOTIVATION: Automatically generated annotation on protein data of UniProt (Universal Protein Resource) is planned to be publicly available on the UniProt web pages in April 2004. It is expected that the data content of over 500,000 protein entries in the TrEMBL section will be enhanced by the output of an automated annotation pipeline. However, a part of the automatically added data will be erroneous, as are parts of the information coming from other sources. We present a post-processing system called Xanthippe that is based on a simple exclusion mechanism and a decision tree approach using the C4.5 data-mining algorithm. RESULTS: It is shown that Xanthippe detects and flags a large part of the annotation errors and considerably increases the reliability of both automatically generated data and annotation from other sources. As a cross-validation to Swiss-Prot shows, errors in protein descriptions, comments and keywords are successfully filtered out. Xanthippe is a contradictive application that can be combined seamlessly with predictive systems. It can be used either to improve the precision of automated annotation at a constant level of recall or increase the recall at a constant level of precision. AVAILABILITY: The application of the Xanthippe rules can be browsed at http://www.ebi.uniprot.org/

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.

Refractive index of LiF films as a function of time.

The optical properties and thickness of evaporated LiF films on silver or aluminum were determined by attenuated total reflection techniques as a function of time after deposition. With the LiF films maintained in vacuum, the refractive index increased from ~1.3 to ~1.4, or close to the crystalline value, in the course of a few days. This implies that the LiF, loosely packed when first deposited, anneals over a period of time to the more closely packed crystalline configuration. The LiF films were found to be optically isotropic in contrast to earlier reports of anisotropy.