2015
DOI: 10.1186/s12859-015-0561-9
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Comparative proteomics: assessment of biological variability and dataset comparability

Abstract: BackgroundComparative proteomics in bacteria are often hampered by the differential nature of dataset quality and/or inherent biological deviations. Although common practice compensates by reproducing and normalizing datasets from a single sample, the degree of certainty is limited in comparison of multiple dataset. To surmount these limitations, we introduce a two-step assessment criterion using: (1) the relative number of total spectra (R TS) to determine if two LC-MS/MS datasets are comparable and (2) nine … Show more

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Cited by 4 publications
(2 citation statements)
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“…Proteomics approaches are commonly used method for measuring the relative abundance of proteins in different biological conditions like during different phases of the cell cycle or after chromatin activation/repression [85, 86, 87]. To understand how complexes are assembled, regulated, and modulated it is useful to study, not only the conservation of networks and complexes, but also perturbed networks and complexes.…”
Section: Expert Commentarymentioning
confidence: 99%
“…Proteomics approaches are commonly used method for measuring the relative abundance of proteins in different biological conditions like during different phases of the cell cycle or after chromatin activation/repression [85, 86, 87]. To understand how complexes are assembled, regulated, and modulated it is useful to study, not only the conservation of networks and complexes, but also perturbed networks and complexes.…”
Section: Expert Commentarymentioning
confidence: 99%
“…Overall, anomaly detection in proteomics is a complex and essential area of research that has implications for understanding protein structure, protein–protein interactions, and identifying defective proteins; the literature encloses benchmarks and ad hoc analysis sequences for this task [ 7 , 8 , 9 ]. However, identifying anomalies of protein expression levels within small-dimensional datasets presents additional difficulties because the dataset is more susceptible to variability and noise, making it challenging to distinguish between actual biological variations and random fluctuations; anomalies may be masked by inherent variability [ 10 ]. Adopting specific solutions to match the characteristics of the data under exam could ensure the reliability of anomaly detection in small datasets; indeed, small datasets may not adequately represent the diverse biological conditions, making it challenging to distinguish between normal variation and true anomalies.…”
Section: Introductionmentioning
confidence: 99%