Data Processing Has Major Impact on the Outcome of Quantitative Label-Free LC-MS Analysis

High-throughput
multiplexed protein quantification using mass spectrometry
is steadily increasing in popularity, with the two major techniques
being data-dependent acquisition (DDA) and targeted acquisition using
selected reaction monitoring (SRM). However, both techniques involve
extensive data processing, which can be performed by a multitude of
different software solutions. Analysis of quantitative LC-MS/MS data
is mainly performed in three major steps: processing of raw data,
normalization, and statistical analysis. To evaluate the impact of
data processing steps, we developed two new benchmark data sets, one
each for DDA and SRM, with samples consisting of a long-range dilution
series of synthetic peptides spiked in a total cell protein digest.
The generated data were processed by eight different software workflows
and three postprocessing steps. The results show that the choice of
the raw data processing software and the postprocessing steps play
an important role in the final outcome. Also, the linear dynamic range
of the DDA data could be extended by an order of magnitude through
feature alignment and a charge state merging algorithm proposed here.
Furthermore, the benchmark data sets are made publicly available for
further benchmarking and software developments.