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An automated pipeline for high-throughput label-free quantitative proteomics


Weisser, Hendrik; Nahnsen, Sven; Grossmann, Jonas; Nilse, Lars; Quandt, Andreas; Brauer, Hendrik; Sturm, Marc; Kenar, Erhan; Kohlbacher, Oliver; Aebersold, Ruedi; Malmström, Lars (2013). An automated pipeline for high-throughput label-free quantitative proteomics. Journal of Proteome Research, 12(4):1628-1644.

Abstract

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

Abstract

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Functional Genomics Center Zurich
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2013
Deposited On:24 Jan 2014 15:43
Last Modified:05 Apr 2016 17:31
Publisher:American Chemical Society
ISSN:1535-3893
Publisher DOI:https://doi.org/10.1021/pr300992u
PubMed ID:23391308

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