Quality controls in integrative approaches to detect errors and inconsistencies in biological databases

Abstract

SummaryNumerous biomolecular data are available, but they are scattered in many databases and only some of them are curated by experts. Most available data are computationally derived and include errors and inconsistencies. Effective use of available data in order to derive new knowledge hence requires data integration and quality improvement. Many approaches for data integration have been proposed. Data warehousing seams to be the most adequate when comprehensive analysis of integrated data is required. This makes it the most suitable also to implement comprehensive quality controls on integrated data. We previously developed GFINDer (http://www.bioinformatics.polimi.it/GFINDer/), a web system that supports scientists in effectively using available information. It allows comprehensive statistical analysis and mining of functional and phenotypic annotations of gene lists, such as those identified by high-throughput biomolecular experiments. GFINDer backend is composed of a multi-organism genomic and proteomic data warehouse (GPDW). Within the GPDW, several controlled terminologies and ontologies, which describe gene and gene product related biomolecular processes, functions and phenotypes, are imported and integrated, together with their associations with genes and proteins of several organisms. In order to ease maintaining updated the GPDW and to ensure the best possible quality of data integrated in subsequent updating of the data warehouse, we developed several automatic procedures. Within them, we implemented numerous data quality control techniques to test the integrated data for a variety of possible errors and inconsistencies. Among other features, the implemented controls check data structure and completeness, ontological data consistency, ID format and evolution, unexpected data quantification values, and consistency of data from single and multiple sources. We use the implemented controls to analyze the quality of data available from several different biological databases and integrated in the GFINDer data warehouse. By doing so, we identified in these data a variety of different types of errors and inconsistencies; this enables us to ensure good quality of the data in the GFINDer data warehouse. We reported all identified data errors and inconsistencies to the curators of the original databases from where the data were retrieved, who mainly corrected them in subsequent updating of the original database. This contributed to improve the quality of the data available, in the original databases, to the whole scientific community.

EMBnet.journal 18 Suppl. B

“…We import them from several well known public databases, including Entrez Gene, UniProt, IntAct, MINT, BioCyc, KEGG, Reactome, GO, GOA and OMIM, The very numerous data integrated, which regard biomolecular entities (mainly genes and proteins) and their biomedical features and associations, are all checked for data correctness and consistency (Ghisalberti et al, 2010). By leveraging imported similarity and historical evaluation data available, we identify different IDs from different data sources as representing the same entity.…”

Artificial Intelligence and Bioinformatics

“…In [Chen et al, 2007] authors propose an ontology-based framework to detect the inconsistency in biological databases. This task is approached as a quality control problem in [Ghisalberti et al, 2010]. In [Park et al, 2011], as many as 27 databases are used to check GO and, besides syntactic errors, semantic inconsistencies are checked concerning redundancy and use of species-specific definitions.…”

Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge Extraction

“…In order to ease maintenance and extension of the integrated data schema defined, each feature module is internally organized in two levels: an import tier and an aggregation tier. The import tier allows structuring and locating together originally distributed data, while thoroughly checking their consistency and quality [22], as well as identifying the feature they refer to and their main attributes, and associating each feature entry with a unique OID. The import tier is composed of separated subschemas, each one for every single data source considered which provides data for that feature, individually structured as in the original data source, i.e.…”