Abstract
Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.
Highlights
From the ‡Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden; §Department of Plant Protection Biology, Swedish Agricultural University, 23053 Alnarp, Sweden; ¶Department of Astronomy and Theoretical Physics, Lund University, 22362 Lund, Sweden
Data from a label-free sample consists of LC-MS files that can be visualized individually as three-dimensional maps where the dimensions correspond to mass-to-charge ratio, retention time and intensity
The output from feature detection algorithms consists of feature lists containing basic information for every feature such as mass-to-charge ratio, the start and end as well as apex time points for the elution profile, charge and some form of abundance measure
Summary
Marianne Sandin‡, Ashfaq Ali§, Karin Hansson‡, Olle Månsson¶, Erik Andreasson§, Svante Resjo §, and Fredrik Levander‡¶ሻ. Two fundamental steps that need to be performed in any precursor-based label-free pipeline are the extraction of peptide information from the maps (feature detection) and matching of corresponding peptides between maps for subsequent differential expression analysis (alignment). Precision and recall are intuitive metrics with a well-defined range, the conventional application of them is as an output given at the end of the analysis, forcing the user to repeat the entire analysis to improve the quality of the performance Such repeated analysis is time-consuming but can be difficult if many user-defined parameters need optimization. Quality control has been incorporated into the algorithm, not merely as an output and as a regulator of the parameters, guaranteeing the user an optimized performance based on the precision and recall metrics. We show that a combination of an adaptive algorithm and rigorous quality control leads to more reliable and reproducible data analysis by extensive comparison to common label-free analysis approaches
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