BACKGROUND: Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homology-based transfer of information from existing database entries to the new target sequences. We have previously demonstrated that data mining in large sequence annotation databanks can help identify annotation items that are strongly associated with each other, and that exceptions from strong positive association rules often point to potential annotation errors. Here we investigate the applicability of negative association rule mining to revealing erroneously assigned annotation items. RESULTS: Almost all exceptions from strong negative association rules are connected to at least one wrong attribute in the feature combination making up the rule. The fraction of annotation features flagged by this approach as suspicious is strongly enriched in errors and constitutes about 0.6% of the whole body of the similarity-transferred annotation in the PEDANT genome database. Positive rule mining does not identify two thirds of these errors. The approach based on exceptions from negative rules is much more specific than positive rule mining, but its coverage is significantly lower. CONCLUSION: Mining of both negative and positive association rules is a potent tool for finding significant trends in protein annotation and flagging doubtful features for further inspection.

MOTIVATION: Millions of protein sequences currently being deposited to sequence databanks will never be annotated manually. Similarity-based annotation generated by automatic software pipelines unavoidably contains spurious assignments due to the imperfection of bioinformatics methods. Examples of such annotation errors include over- and underpredictions caused by the use of fixed recognition thresholds and incorrect annotations caused by transitivity based information transfer to unrelated proteins or transfer of errors already accumulated in databases. One of the most difficult and timely challenges in bioinformatics is the development of intelligent systems aimed at improving the quality of automatically generated annotation. A possible approach to this problem is to detect anomalies in annotation items based on association rule mining. RESULTS: We present the first large-scale analysis of association rules derived from two large protein annotation databases-Swiss-Prot and PEDANT-and reveal novel, previously unknown tendencies of rule strength distributions. Most of the rules are either very strong or very weak, with rules in the medium strength range being relatively infrequent. Based on dynamics of error correction in subsequent Swiss-Prot releases and on our own manual analysis we demonstrate that exceptions from strong rules are, indeed, significantly enriched in annotation errors and can be used to automatically flag them. We identify different strength dependencies of rules derived from different fields in Swiss-Prot. A compositional breakdown of association rules generated from PEDANT in terms of their constituent items indicates that most of the errors that can be corrected are related to gene functional roles. Swiss-Prot errors are usually caused by under-annotation owing to its conservative approach, whereas automatically generated PEDANT annotation suffers from over-annotation. AVAILABILITY: All data generated in this study are available for download and browsing at http://pedant.gsf.de/ARIA/index.htm.

Abstract Summary: The MIPS mammalian protein–protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental evidence that has been processed by human expert curators. We provide the full dataset for download and a flexible and powerful web interface for users with various requirements. Availability: The MPPI database is located at http://mips.gsf.de/proj/ppi/ Contact: [email protected]

MOTIVATION: Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. RESULTS: The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. AVAILABILITY: BFAB is available at http://mips.gsf.de/proj/bfab