Semantic Data Cleansing in Genome Databases

The abundance of errors in genome databases is a well-known fact. Major problems are errors in genome annotation. Performing biological experiments to eliminate them is time consuming and expensive. As a viable alternative, we introduce novel data cleansing methods for (semi-) automatic detection and correction of erroneous entries. Using a simple example we show the applicability of this cleansing approach. Our approach forms a sound basis for the solution of many open questions such as the efficient identification of erroneous entries, the specification of the cleansing process, management of alternative solutions until the correct one is identified, and efficient management of dependencies to react on changes to base data and avoid outdated data. 1. Genome Data is Dirty 1.1 Problems in Genome Databases Increasing interest in genome data has lead to the availability of a multitude of public available genome databases today. By genome data we mean nucleic acid (DNA and RNA), amino acid (protein) sequence data, and their structural and functional classification (annotation). The process of assigning meaning to sequence data by identifying regions of interest and determine function for them is defined as genome annotation [MGMB+03]. Two of the most daunting problems within this process are the integrated access of multiple sources and the quality of the retrieved data. The former problem is common to all integrated databases and is regarded elsewhere [SL90]. The latter problem, on the other hand, has so far been studied only marginally in the context of genome data, 1 for a comprehensive listing see http://nar.oupjournals. org/cgi/content/full/31/1/1/DC1 despite of the importance of high data quality for ongoing genome research. Errors in genome data can result in improper target selection for biological experiments or pharmaceutical research, in turn resulting in loss of money. Missing, incomplete or erroneous information hinders the automatic processing and analysis of data. This leads to a loss in confidence and a rise in effort and frustration for the biologist. Fuzzy or incomplete knowledge in conjunction with erroneous base data makes annotation a highly error prone process. In [Bor00] it is stated that an average of 70% accuracy in predicting functional and structural features must be considered a success! Several studies show the existence of errors in genome databases, e.g. [Bre99, ITAE+03]. In [Bre99] the error rate is estimated to be over 8% by comparing analysis results of three independent research groups annotating the proteome of Mycoplasma genitalium and counting the number of discrepancies between them. In [ITAE+03] the authors generate a highly reliable set of annotations by carefully using automatic methods and experimental evidence. They compare their results with existing annotations and with the results of solely automatically performed annotations. For the original annotations only 63% of functional assignments within both datasets are in total agreement, while for the solely automatic annotations the precision is estimated to be 74% for the most reliable set of predictions. 1.2 Errors in Genome Data Production Producing incorrect data is intrinsic to the current process of generating genome data. The main causes for poor data quality in genome databases are Experimental errors due to unnoticed experimental setup failure or systematic errors. These errors are hard to detect by observation because of the diminutiveness of samples. Analysis errors due to the absence of fixed rules and knowledge guiding the annotation process which leads to misinterpretations and incomplete or invalid information (miss-annotation). Transformation errors while performing transformations of information from one representation into another or one medium to another, e.g., data input or translation of DNA sequences into protein sequences. Propagated errors, when erroneous data is used for the generation of new data, e.g., within the process of functional annotation of proteins. Stale data, i.e., unnoticed changes to base data on which a data item depends and that falsify it. Genome databases often contain integrated or derived data. Changes to the original data often remain unnoticed hindering the update of the derived information, leading to stale (outdated) data. Propagation of errors is considered to be the major problem in genome data production, because existing data is very often used within the production process. In [GADTO02] a dynamic probabilistic model for error propagation in data annotation is developed. The authors show that the iterative annotation approach leads to a systematic deterioration of database quality. Concluding, there is a great need for data cleansing in genome databases to prevent loss of money in pharmaceutical research due to decisions based on erroneous data and to avoid further source pollution by error propagation.