Bioinformatics

Modern proteomics laboratories are able to generate overwhelming quantities of data in short periods of time. Managing and extracting information from these data requires specialist software and high-performance computing hardware. Bioinformatics is a broad term used to describe the use of computing in this context.

Analysing protein separation data, usually in the form of two-dimensional electrophoresis (2DE) gels is a specialist task. We are able to accurately detect and match proteins across large numbers of 2DE gels. These data are then analysed using a number of statistical and data mining techniques to identify relationships between protein expression and/or post-translational modifications and the status of the tissues under investigation.

Mass spectrometry is used to identify and characterise separated proteins or peptides and relies heavily on specialist software for the data analysis. This is achieved through searching for similarities between observed peptide fragment masses and theoretical peptide masses calculated from sequence databases. This task would not be possible without modern computing due to both the number of sequences to be searched and the complexity with which peptide fragmentations occur.

Proteome Sciences has bioinformatics solutions, which address sample identification, image analysis, spot selection and cutting and mass spectrometry data collection and analysis. For our gel free proteomics toolkit (PST®, qPST™, TMT®), we have developed sophisticated bioinformatic tools which provide in silico reduction of data complexity ensuring maximum recovery of valuable protein sequence information.

Storing, retrieving and ensuring the integrity of laboratory-generated data are essential and routine tasks for bioinformaticians. There is a wealth of information in the public domain accumulating in databases such as SwissProt, NCBInr and dbEST. This is complemented by the substantive proprietary differential protein expression data and intellectual property that Proteome Sciences has amassed and developed since 1995 across the major human disease areas including neurosciences, cancer, cardiovascular disease, rejection and diabetes/obesity. In 2004, dbEST contained over 5 million human expressed sequence tags whilst the smaller, curated databases are extremely useful sources of information about proteins such as structure, modifications, regulation and cellular roles. Placing the experimental results from the laboratory in the context of data in these resources considerably enhances understanding and value from the studies undertaken.

DATA MINING