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Computational mass spectrometry
Multi-Q
Multi-Q is a fully automated tool for multiplexed iTRAQ-based quantitation in protein profiling , which is designed as a generic platform that can accommodate various input data formats from search engines and mass spectrometer manufacturers. Experiment results demonstrate the high accuracy, full automation, and high-throughput capability of Multi-Q as a large-scale quantitati ve proteomics tool. These features allow rapid interpretation of output from large proteomic datasets without the need for manual validation.

Lin, W. T.; Hung, W. N.; Yian, Y. H.; Wu, K. P.; Han, C. L.; Chen, Y. R.; Chen, Y. J.; Sung, T. Y.; Hsu, W. L., Multi-Q: A Fully Automated Tool for Multiplexed Protein Quantitation. J Proteome Res 2006, 5, (9), 2328-2338.

MaXIC-Q

MaXIC-Q is an automated quantitation tool, which utilizes XICs acquired from isotope labeling techniques for quantitation analysis. As a generic computation platform for high-throughput quantitative proteomics, MaXIC-Q offers the following features: (1) It accepts the mzXML (24) spectral format, which can be converted from raw files of various mass spectrometers by existing tools, as well as search results from commonly used search engines, including Mascot and SEQUEST. (2) It allows user-defined isotope codes, which cover a very broad range of quantitation strategies for various in vivo and in vitro labeling techniques, and even user-developed labeling methods. To the best of our knowledge, MaXIC-Q is currently the only tool that defines stringent criteria for the validation of both XIC and mass spectra to achieve high accuracy in an unattended manner. Furthermore, MaXIC-Q provides graphic interfaces, Elution3D, an XIC viewer, and an ion mass spectrum viewer that allow flexible user-activated interactive modification based on simultaneous 3D visualization of the m/z, elution time and intensity.

Chih-Chiang Tsou, Yin-Hao Tsui, Yi-Hwa Yian, Yi-Ju Chen, Han-Yin Yang, Chuan-Yih Yu, Ke-Shiuan Lynn, Yu-Ju Chen, Ting-Yi Sung, and Wen-Lian Hsu, "MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC¡VMS," Nucleic Acids Research, 2009.

IDEAL-Q

IDEAL-Q is an automated analysis tool for label-free quantitative proteomics. It accepts generic input format including mzXML raw data format and Mascot, SEQUEST, PeptideProphet/ProteinProphet for search result. IDEAL-Q uses an algorithm, called IDEAL (ID-based Elution time prediction by frAgmentaL regression), to predict the elution time based on confident peptide identification (ID) result, and thus the predicted elution time together with precursor m/z to can be used to locate the peptide signal in other LC-MS runs. In comparison with conventional identity-based label-free quantitation analysis, the quantitation coverage in terms of percentage of identified peptides and proteins can be much increased. Furthermore, the tool adopts an stringent validation stelp on Signal-to-noise ratio, Charge state, Isotopic distribution (SCI validation) and modification type using computational and statistical methods so that quantitation accuracy can be ensured even with increased quantitation coverage. IDEAL-Q provides variously optional normalization tools for flexible workflow design such as addition of fractionation strategies and multiple spiked internal standards. Furthermore, many user-friendly interfaces and statistical charts are provided in IDEAL-Q for user to conveniently inspect and validate the quantitation result and also enable to manually re-quantify data. It also provides comprehensible output reports in various formats with useful visualization diagrams and statistical analysis.

Chih-Chiang Tsou, Chia-Feng Tsai, Ying-Hao Tsui, Putty-Reddy Sudhir, Yi-Ting Wang, Yu-Ju Chen, Jeou-Yuan Chen, Ting-Yi Sung, and Wen-Lian Hsu, ¡§IDEAL-Q: An automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation¡¨, Molecular & Cellular Proteomics, Vol. 9, pp. 131-144, 2010.
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Wen-Lian Hsu
Professor, IEEE Fellow
Research Fellow
Institute of Information Science ,
Academia Sinica, Taipei,
Taiwan, R. O. C.
Phone:
886-2-27883799 ext.1804
Fax:
886-2-27824814
E-mail: hsu@iis.sinica.edu.tw

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Ting-Yi Sung
Research Fellow
Institute of Information Science ,
Academia Sinica, Taipei,
Taiwan, R. O. C.
Phone:
886-2-27883799 ext.1711
Fax:
886-2-27824814
E-mail:
 tsungiis.sinica.edu.tw

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Intelligent Agent Systems Lab., Institute of Information Science, Academia Sinica.
128 Academia Road, Sec.2, Nankang, Taipei, Taiwan, ROC
Tel: +886-2-2788-3799, Fax: 886-2-2782-4814, 886-2-2651-8660