Integrative bioinformatics

Integrative bioinformatics is a discipline of bioinformatics that focuses on problems of data integration for the life sciences.

With the rise of high-throughput (HTP) technologies in the life sciences, particularly in molecular biology, the amount of collected data has grown in an exponential fashion. Furthermore, the data are scattered over a plethora of both public and private repositories, and are stored using a large number of different formats. This situation makes searching these data and performing the analysis necessary for the extraction of new knowledge from the complete set of available data very difficult. Integrative bioinformatics attempts to tackle this problem by providing unified access to life science data.

In the Semantic Web approach, data from multiple websites or databases is searched via metadata. Metadata is machine-readable code, which defines the contents of the page for the program so that the comparisons between the data and the search terms are more accurate. This serves to decrease the number of results that are irrelevant or unhelpful. Some meta-data exists as definitions called ontologies, which can be tagged by either users or programs; these serve to facilitate searches by using key terms or phrases to find and return the data. Advantages of this approach include the general increased quality of the data returned in searches and with proper tagging, ontologies finding entries that may not explicitly state the search term but are still relevant. One disadvantage of this approach is that the results that are returned come in the format of the database of their origin and as such, direct comparisons may be difficult. Another problem is that the terms used in tagging and searching can sometimes be ambiguous and may cause confusion among the results. In addition, the semantic web approach is still considered an emerging technology and is not in wide-scale use at this time.

One of the current applications of ontology-based search in the biomedical sciences is GoPubMed, which searches the PubMed database of scientific literature. Another use of ontologies is within databases such as SwissProt, Ensembl and TrEMBL, which use this technology to search through the stores of human proteome-related data for tags related to the search term.

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