TY - GEN
T1 - Using a spike-in experiment to evaluate analysis of LC-MS data
AU - Tuli, Leepika
AU - Tsai, Tsung Heng
AU - Varghese, Rency S.
AU - Cheema, Amrita
AU - Ressom, Habtom W.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to newer approaches for measuring changes in peptide/protein abundances. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization, and transformation. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. In this paper, we use a spike-in experiment to evaluate the performance of three software tools in accurately detecting changes in peptide abundances from LC-MS data obtained by a label-free LC-MS method. We observe that tools that incorporate peptide isotope cluster and multiple charge information lead to more accurate difference detection with fewer false positives.
AB - Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to newer approaches for measuring changes in peptide/protein abundances. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization, and transformation. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards. In this paper, we use a spike-in experiment to evaluate the performance of three software tools in accurately detecting changes in peptide abundances from LC-MS data obtained by a label-free LC-MS method. We observe that tools that incorporate peptide isotope cluster and multiple charge information lead to more accurate difference detection with fewer false positives.
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U2 - 10.1109/BIBMW.2010.5703775
DO - 10.1109/BIBMW.2010.5703775
M3 - Conference contribution
AN - SCOPUS:79952019839
SN - 9781424483044
T3 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
SP - 67
EP - 72
BT - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
T2 - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
Y2 - 18 December 2010 through 21 December 2010
ER -