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Tools for Label-free Peptide Quantification
<span title="2012-12-17">2012</span>
<i title="American Society for Biochemistry & Molecular Biology (ASBMB)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/63atlvwyorhorfip35rcvk6bey" style="color: black;">Molecular & Cellular Proteomics</a>
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The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically,
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1074/mcp.r112.025163">doi:10.1074/mcp.r112.025163</a>
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... do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data. Molecular & Cellular
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