pkDACLASS: Open source software for analyzing MALDI-TOF data

被引:9
作者
Ndukum, Juliet [1 ]
Atlas, Mourad [1 ,2 ]
Datta, Susmita [1 ]
机构
[1] Univ Louisville, Sch Publ Hlth & Informat Sci, Dept Bioinformat & Biostat, Louisville, KY 40202 USA
[2] FDA CDRH, Silver Spring, MD 20993 USA
关键词
MALDI-TOF; proteome research; complete data analysis; R package;
D O I
10.6026/97320630006045
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In recent years, mass spectrometry has become one of the core technologies for high throughput proteomic profiling in biomedical research. However, reproducibility of the results using this technology was in question. It has been realized that sophisticated automatic signal processing algorithms using advanced statistical procedures are needed to analyze high resolution and high dimensional proteomic data, e.g., Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) data. In this paper we present a software package-pkDACLASS based on R which provides a complete data analysis solution for users of MALDI-TOF raw data. Complete data analysis comprises data preprocessing, monoisotopic peak detection through statistical model fitting and testing, alignment of the monoisotopic peaks for multiple samples and classification of the normal and diseased samples through the detected peaks. The software provides flexibility to the users to accomplish the complete and integrated analysis in one step or conduct analysis as a flexible platform and reveal the results at each and every step of the analysis.
引用
收藏
页码:45 / 47
页数:3
相关论文
共 12 条
  • [1] Atlas M., 2009, J PROTEOM BIOINFORM, V2, P202
  • [2] Breen EJ, 2000, ELECTROPHORESIS, V21, P2243, DOI 10.1002/1522-2683(20000601)21:11<2243::AID-ELPS2243>3.0.CO
  • [3] 2-K
  • [4] Automated Peak Harvesting of MALDI-MS spectra for high throughput proteomics
    Breen, EJ
    Holstein, WL
    Hopwood, FG
    Smith, PE
    Thomas, ML
    Wilkins, MR
    [J]. SPECTROSCOPY-AN INTERNATIONAL JOURNAL, 2003, 17 (2-3): : 579 - 595
  • [5] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [6] Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching
    Du, Pan
    Kibbe, Warren A.
    Lin, Simon M.
    [J]. BIOINFORMATICS, 2006, 22 (17) : 2059 - 2065
  • [7] Comparison of feature selection and classification for MALDI-MS data
    Liu, Qingzhong
    Sung, Andrew H.
    Qiao, Mengyu
    Chen, Zhongxue
    Yang, Jack Y.
    Yang, Mary Qu
    Huang, Xudong
    Deng, Youping
    [J]. BMC GENOMICS, 2009, 10
  • [8] LIMPIC: a computational method for the separation of protein MALDI-TOF-MS signals from noise
    Mantini, Dante
    Petrucci, Francesca
    Pieragostino, Damiana
    Del Boccio, Piero
    Di Nicola, Marta
    Di Ilio, Carmine
    Federici, Giorgio
    Sacchetta, Paolo
    Comani, Silvia
    Urbani, Andrea
    [J]. BMC BIOINFORMATICS, 2007, 8 (1)
  • [9] Biomarker selection and sample prediction for multi-category disease on MALDI-TOF data
    Oh, Jung Hun
    Kim, Young Bun
    Gurnani, Prem
    Rosenblatt, Kevin P.
    Gao, Jean X.
    [J]. BIOINFORMATICS, 2008, 24 (16) : 1812 - 1818
  • [10] Peak selection from MALDI-TOF mass spectra using ant colony optimization
    Ressom, H. W.
    Varghese, R. S.
    Drake, S. K.
    Hortin, G. L.
    Abdel-Hamid, M.
    Loffredo, C. A.
    Goldman, R.
    [J]. BIOINFORMATICS, 2007, 23 (05) : 619 - 626