Algorithm created for LC-MS datasets

Scientists have created an algorithm to aid the analysis of multiple liquid chromatography-mass spectrometry (LC-MS) datasets.

Researchers from the Department of Electrical and Computer Engineering at the University of Texas at San Antonio, USA, suggested that identifying corresponding features, such as LC peaks registered by identical peptides, plays a crucial role in the analysis of peptides and protein mixtures so more needs to be done to enhance the efficiency of the process.

In a study published by the BMC Bioinformatics journal, the team proposed a Statistical Corresponding Feature Identification Algorithm (SCFIA) "based on both elution time shifts and peak shape correlations between corresponding features".

The scientists then tested the model, and the more convention warping function based methods, on publicly available datasets and found that both processes had an accuracy of 90 per cent.

They also found that corresponding features in multiple datasets were found in 90 per cent of peptides identified by Tandem MS.

Posted by Fiona Griffiths