TR-IIS-08-004 Fulltext
MaXIC-Q: A Fully Automated Generic Tool Using Statistical and Computational
Methods for Protein Quantitation Based on Stable Isotope Labeling and LC-MS
Ethan Yin-Hao Tsui, Yi-Hwa Yian, Chih-Chiang Tsou, Paul Chuan-Yih Yu, Yi-Ju Chen, Ke-Shiuan Lynn, Wen-Chi Chou, Yu-Ju Chen, Ting-Yi Sung, and Wen-Lian Hsu
Summary
Isotope labeling combined with LC-MS/MS provided
a robust platform for analyzing differential protein expression in proteomics
experiments. Many software tools have been developed to support quantitation
analysis of large-scale mass spectral data. However, a tool that can accept
input data in versatile formats, produce highly-accurate analysis results quickly,
and present the results comprehensively is still lacking.
In this paper, we present such an automated tool, called MaXIC-Q, for quantitation
analysis of large-scale datasets generated in high-throughput proteomics experiments.
It is designed as a generic tool that is suitable for quantitation using many
differential isotope-labeling techniques, e.g., SILAC, ICAT and ICPL labelings,
and user-developed labeling methods. The tool can also accommodate search results
from SEQUEST and Mascot, as well as mzXML files converted from raw files produced
by various mass spectrometers. MaXIC-Q contains a filtering module that allows
users to filter out low-confidence search results. Statistical and computational
methods are applied to construct two kinds of elution profiles for each ion,
namely, PIMS (projected ion mass spectrum) and XIC (extracted ion chromatogram);
the latter is used to quantify the ion ratio. To ensure that the data used for
quantitation analysis is of high quality, MaXIC-Q defines validation criteria
for PIMSs so that ions interfered with by co-eluting peptides or noise can be
detected.
The performance of MaXIC-Q was evaluated on two datasets generated from a mixture
of nine standard proteins and a large-scale experiment on endothelial cells
using cICAT. MaXIC-Q¡¦s processing speed on the large-scale dataset is ten times
faster than existing tools. Moreover, it can generate promising quantitation
results, so manual validation efforts can be substantially reduced. MaXIC-Q
also provides powerful visualization tools and comprehensive reports in various
formats. In summary, MaXIC-Q is a user-friendly, interactive, robust, generic
tool for quantitation based on various stable isotope labeling techniques. MaXIC-Q
can be downloaded from http://ms.iis.sinica.edu.tw/MaXIC-Q/.