Abstract
BackgroundThe identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery.ResultsIn this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection.ConclusionsThe performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.
Highlights
The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research
Detected peaks consist of True Positives (TP) and False Positives (FP), which can be determined by comparing the detected peaks with the set of P “true peptides” obtained using some peak identification methods such as LC/MS/MS
The true positive rate is estimated as TP/(TP + False Negatives (FN)), which indicates the probability of detecting a true peak
Summary
The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. The inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery. The identification and quantification of proteins in biological samples play crucial roles in biological and biomedical research [1,2,3]. In biomarker discovery studies, the aim is to elucidate a set of proteins that can be used to reliably differentiate diseased and normal samples. Many researchers have pointed out that biologically meaningful proteins are often of low abundance [2], the percentage of low abundance proteins identified determines the likelihood of success in biomarker discovery studies.
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