Title
Computational quantification of peptides from LC-MS data.
Abstract
Liquid chromatography coupled to mass spectrometry (LC-MS) has become a major tool for the study of biological processes. High-throughput LC-MS experiments are frequently conducted in modern laboratories, generating an enormous amount of data per day. A manual inspection is therefore no longer a feasible task. Consequently, there is a need for computational tools that can rapidly provide information about mass, elution time, and abundance of the compounds in a LC-MS sample. We present an algorithm for the detection and quantification of peptides in LC-MS data. Our approach is flexible and independent of the MS technology in use. It is based on a combination of the sweep line paradigm with a novel wavelet function tailored to detect isotopic patterns of peptides. We propose a simple voting schema to use the redundant information in consecutive scans for an accurate determination of monoisotopic masses and charge states. By explicitly modeling the instrument inaccuracy, we are also able to cope with data sets of different quality and resolution. We evaluate our technique on data from different instruments and show that we can rapidly estimate mass, centroid of retention time, and abundance of peptides in a sound algorithmic framework. Finally, we compare the performance of our method to several other techniques on three data sets of varying complexity.
Year
DOI
Venue
2008
10.1089/cmb.2007.0117
JOURNAL OF COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
computational mass spectrometry,liquid chromatography-mass spectrometry,quantification,wavelets
Elution,Pattern recognition,Liquid chromatography–mass spectrometry,Monoisotopic mass,Artificial intelligence,Mass spectrometry,Machine learning,Mathematics,Sweep line algorithm,Wavelet
Journal
Volume
Issue
ISSN
15.0
7
1066-5277
Citations 
PageRank 
References 
4
0.52
8
Authors
7
Name
Order
Citations
PageRank
Ole B Schulz-Trieglaff126323.38
Rene Hussong21207.78
Clemens Gröpl333037.21
Andreas Leinenbach4413.74
Andreas Hildebrandt533332.00
Christian G. Huber66413.15
Knut Reinert71020105.87