Issue No. 02 - March-April (2017 vol. 14)
Ilona Kifer , Agilent Laboratories, Tel Aviv, Israel
Rui M. Branca , Karolinska Institute, Stockholm, Sweden
Amir Ben-Dor , Agilent Laboratories, Tel Aviv, Israel
Linhui Zhai , BPRC, Beijing Institute of Radiation Medicine, Beijing, P.R. China
Ping Xu , BPRC, Beijing Institute of Radiation Medicine, Beijing, P.R. China
Janne Lehtio , Karolinska Institute, Stockholm, Sweden
Zohar Yakhini , Agilent Laboratories, Tel Aviv, Israel
IEF LC-MS/MS is an analytical method that incorporates a two-step sample separation prior to MS identification of proteins. When analyzing complex samples this preparatory separation allows for higher analytical depth and improved quantification accuracy of proteins. However, cost and analysis time are greatly increased as each analyzed IEF fraction is separately profiled using LC-MS/MS. We propose an approach that selects a subset of IEF fractions for LC-MS/MS analysis that is highly informative in the context of a group of proteins of interest. Specifically, our method allows a significant reduction in cost and instrument time as compared to the standard protocol of running all fractions, with little compromise to coverage. We develop algorithmics to optimize the selection of the IEF fractions on which to run LC-MS/MS. We translate the fraction optimization task to Minimum Set Cover, a well-studied NP-hard problem. We develop heuristic solutions and compare them in terms of effectiveness and running times. We provide examples to demonstrate advantages and limitations of each algorithmic approach. Finally, we test our methodology by applying it to experimental data obtained from IEF LC-MS/MS analysis of yeast and human samples. We demonstrate the benefit of this approach for analyzing complex samples with a focus on different protein sets of interest.
Proteins, Peptides, Standards, Proteomics, Algorithm design and analysis, Focusing, Approximation methods
I. Kifer et al., "Optimizing Analytical Depth and Cost Efficiency of IEF-LC/MS Proteomics," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 2, pp. 272-281, 2017.