An Algorithm for Feature Finding in LC/MS Raw Data

Abstract

Liquid chromatography coupled with mass spectrometry is an established
  method in shotgun proteomics.  A key step in the data processing pipeline is
  to transform the raw data acquired by the mass spectrometer into a list of
  features.  In this context, a emph{feature} is defined as the
  two-dimensional integration with respect to retention time (RT) and
  mass-over-charge (m/z) of the eluting signal belonging to a single charge
  variant of a measurand (e.g., a peptide).  Features are characterized by attributes
  like average mass-to-charge ratio, centroid retention time, intensity, and quality.
  We present a new
  algorithm for feature finding which has been developed as a part of a
  combined experimental and algorithmic approach to absolutely quantify
  proteins from complex samples with unprecedented precision.  The method was
  applied to the analysis of myoglobin in human blood serum, which is an
  important diagnostic marker for myocardial infarction.  Our approach was
  able to determine the absolute amount of myoglobin in a serum sample through
  a series of standard addition experiments with a relative error of 2.5\%.  It
  compares favorably to a manual analysis of the same data set since we could
  improve the precision and conduct the whole analysis pipeline in a small
  fraction of the time.  We anticipate that our automatic quantitation method
  will facilitate further absolute or relative quantitation of even more
  complex peptide samples.  The algorithm was implemented in the publicly
  available software framework OpenMS (www.OpenMS.de)