Bioinformatic Method Development Projects

Summary of Projects


MUSCLE: Multi-platform Unbiased-optimisation of Spectrometry via Closed Loop Experimentation
In collaboration with researchers at the University of Manchester, this recently funded BBSRC project is developing and implementing a multi-platform, user-friendly software tool for the robust and automated multi-objective optimisation of mass spectrometry analyses. This includes the analysis of metabolites, peptides/proteins and other analytes. The novel software tool – MUSCLE – will enable the user to control and optimise automatically their chromatograph, ion source and mass spectrometer, regardless of the manufacturer. We will establish MUSCLE and its associated application control scripts as a community resource.

Profiling the metabolome using direct infusion FT-ICR mass spectrometry
We are developing signal processing strategies to maximise the quality of the metabolomics data obtained using our Thermo FT-ICR mass spectrometer and Triversa chip-based nanoelectrospray assembly. In addition we are developing sofware to aid the identification of metabolites.

  • R. J. M. Weber, M. R. Viant, MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways. Chemometrics and Intelligent Laboratory Systems 104, 75-82 (2010).
  • T. G. Payne, A. D. Southam, T. N. Arvanitis, M. R. Viant, A signal filtering method for improved quantification and noise discrimination in Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data. J. Amer. Soc. Mass Spectrom. 20, 1087-1095 (2009).
  • A. D. Southam, T. G. Payne, H. J. Cooper, T. N. Arvanitis, M. R. Viant, Spectral Stitching Method Increases the Dynamic Range and Mass Accuracy of Wide-scan Direct Infusion nano-Electrospray Fourier Transform Ion Cyclotron Resonance Mass Spectrometry-based Metabolomics. Anal. Chem. 79, 4595-4602 (2007).

FIMA software: Automated metabolite identification and quantification using 2-D J-Resolved Spectroscopy, including the Birmingham Metabolite Library of NMR spectra
The high spectral congestion typically observed in one-dimensional (1D) 1H NMR spectra of tissue extracts and biofluids limits the metabolic information that can be extracted.  We have promoted the application of 2D J-resolved (JRES) spectroscopy for metabolomics, including the measurement of a large library of NMR spectra of low molecular weight metabolites (see below). In addition we have developed software for the automated identification and quantification of metabolites in 2D JRES spectra.

  • C. Ludwig, J. M. Easton, A. Lodi, S. Tiziani, S. Manzoor, A. D. Southam, J. J. Byrne, L. M. Bishop, S. He, T. N. Arvanitis, U. L. Günther, M. R. Viant, Birmingham Metabolite Library: A publicly accessible database of 1-D 1H and 2-D 1H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics 8, 8-18 (2012).
  • S. Tiziani, A. Lodi, C. Ludwig, H. M. Parsons and M. R. Viant, Effects of the application of different window functions to processing of 1H J-resolved NMR spectra for metabolomics. Anal. Chim. Acta 610, 80-88 (2008).
  • M. R. Viant, Improved Methods for the Acquisition and Interpretation of NMR Metabolomic Data, Biochem. Biophys. Res. Comm. 310, 943-948 (2003).

ProMetab software: Optimised data processing for NMR metabolomics data
ProMetab software is a metabolomics data processing tool that converts raw Bruker NMR spectra into a format for multivariate chemometric analysis. It is written in MATLAB, which provides a technical computing environment for high-performance numeric computation and visualization. ProMetab can interpret both 1D and 2D J-resolved NMR spectra, and then segments the data into chemical shift bins of user-defined width. Following removal of unwanted spectral features, the User can compress specific groups of bins into single segments to minimize the effects of pH-induced shifting of the NMR peaks. Various normalisation strategies and data transformations are available to the User (including the generalised log transform). Multivariate analyses of the processed data can subsequently be performed using a MATLAB toolbox such as PLS_Toolbox (Eigenvector Research) or exported and analysed using other statistical packages. For further details see:

  • H. M. Parsons, C. Ludwig, U. L. Günther and M. R. Viant, Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logarithm transformation. BMC Bioinformatics 8, 234 (2007).
  • M. R. Viant, Improved Methods for the Acquisition and Interpretation of NMR Metabolomic Data, Biochem. Biophys. Res. Comm. 310, 943-948 (2003).