Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

Brief Bioinform. 2009 Jan;10(1):11-23. doi: 10.1093/bib/bbn045. Epub 2008 Oct 29.


The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Expert Systems*
  • Humans
  • Information Storage and Retrieval / methods*
  • Knowledge*
  • Molecular Sequence Data
  • Sequence Alignment / methods*
  • Software*