These authors contributed equally to the paper.
LFQuant: A label-free fast quantitative analysis tool for high-resolution LC-MS/MS proteomics data
Article first published online: 17 DEC 2012
© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Volume 12, Issue 23-24, pages 3475–3484, December 2012
How to Cite
Zhang, W., Zhang, J., Xu, C., Li, N., Liu, H., Ma, J., Zhu, Y. and Xie, H. (2012), LFQuant: A label-free fast quantitative analysis tool for high-resolution LC-MS/MS proteomics data. Proteomics, 12: 3475–3484. doi: 10.1002/pmic.201200017
Colour Online: See the article online to view Fig. 1 in colour.
- Issue published online: 17 DEC 2012
- Article first published online: 17 DEC 2012
- Accepted manuscript online: 19 OCT 2012 01:48AM EST
- Manuscript Accepted: 1 OCT 2012
- Manuscript Revised: 25 SEP 2012
- Manuscript Received: 15 JAN 2012
- National Natural Science Foundation of China. Grant Numbers: 31000587, 31000591
- Chinese National Key Program of Basic Research. Grant Numbers: 2010CB912700, 2011CB910601
- State Key Laboratory of Proteomics. Grant Number: SKLP-O201004
- Analysis tool;
- Extracted ion chromatogram;
- Label-free quantification
Database searching based methods for label-free quantification aim to reconstruct the peptide extracted ion chromatogram based on the identification information, which can limit the search space and thus make the data processing much faster. The random effect of the MS/MS sampling can be remedied by cross-assignment among different runs. Here, we present a new label-free fast quantitative analysis tool, LFQuant, for high-resolution LC-MS/MS proteomics data based on database searching. It is designed to accept raw data in two common formats (mzXML and Thermo RAW), and database search results from mainstream tools (MASCOT, SEQUEST, and X!Tandem), as input data. LFQuant can handle large-scale label-free data with fractionation such as SDS-PAGE and 2D LC. It is easy to use and provides handy user interfaces for data loading, parameter setting, quantitative analysis, and quantitative data visualization. LFQuant was compared with two common quantification software packages, MaxQuant and IDEAL-Q, on the replication data set and the UPS1 standard data set. The results show that LFQuant performs better than them in terms of both precision and accuracy, and consumes significantly less processing time. LFQuant is freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/lfquant/.