High-accuracy peak picking of proteomics data

Abstract

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. 
It is independent of the underlying machine or ionization method, and is able to resolve highly
convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting
the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data.
In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. 
In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks
of multiply charged peptides in ESI-MS data of low resolution. 
Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification
and quantification experiments. The method has been validated on a number of different annotated test cases, 
where it compares favorably in both runtime and accuracy with currently established techniques. 
An implementation of the algorithm is freely available in our open source framework OpenMS (www.open-ms.de).