title: Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis.
abstract: BACKGROUND
  Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public.
  
  
  RESULTS
  In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile.
  
  
  CONCLUSIONS
  Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.
authors.affiliation: Beijing NMR Center, Peking University, PR China. super_wt@sina.com
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.forename: Kang
authors.initials: K
authors.lastname: Shao
authors.forename: Qinying
authors.initials: Q
authors.lastname: Chu
authors.forename: Yanfei
authors.initials: Y
authors.lastname: Ren
authors.forename: Yiming
authors.initials: Y
authors.lastname: Mu
authors.forename: Lijia
authors.initials: L
authors.lastname: Qu
authors.forename: Jie
authors.initials: J
authors.lastname: He
authors.forename: Changwen
authors.initials: C
authors.lastname: Jin
authors.forename: Bin
authors.initials: B
authors.lastname: Xia
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy; D055432:Metabolomics / Q000379:methods*; D012984:Software