MET-IDEA: data extraction tool for mass spectrometry-based metabolomics.

A current and significant limitation to metabolomics is the large-scale, high-throughput conversion of raw chromatographically coupled mass spectrometry datasets into organized data matrices necessary for further statistical processing and data visualization. This article describes a new data extraction tool, MET-IDEA (Metabolomics Ion-based Data Extraction Algorithm) which surmounts this void. MET-IDEA is compatible with a diversity of chromatographically coupled mass spectrometry systems, generates an output similar to traditional quantification methods, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized datasets by several orders of magnitude.

Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period.

Background: This study analyzes metabolomic data from a rice tillering (branching) developmental profile to define a set of biomarker metabolites that reliably captures the metabolite variance of this plant developmental event, and which has potential as a basis for rapid comparative screening of metabolite profiles in relation to change in development, environment, or genotype. Changes in metabolism, and in metabolite profile, occur as a part of, and in response to, developmental events. These changes are influenced by the developmental program, as well as external factors impinging on it.

Metabolomics spectral formatting, alignment and conversion tools (MSFACTs).

Motivation: The amplified interest in metabolic profiling has generated the need for additional tools to assist in the rapid analysis of complex data sets.

Results: A new program; metabolomics spectral formatting, alignment and conversion tools, (MSFACTs) is described here for the automated import, reformatting, alignment, and export of large chromatographic data sets to allow more rapid visualization and interrogation of metabolomic data. MSFACTs incorporates two tools: one for the alignment of integrated chromatographic peak lists and another for extracting information from raw chromatographic ASCII formatted data files.

Profiling phenolic metabolites in transgenic alfalfa modified in lignin biosynthesis.

Soluble phenolics, wall-bound phenolics and soluble and core lignin were analyzed in transgenic alfalfa with genetically down-regulated O-methyltransferase genes involved in lignin biosynthesis. High performance liquid chromatography and principal component analysis were used to distinguish metabolic phenotypes of different transgenic alfalfa genotypes growing under standard greenhouse conditions. Principal component analysis of HPLC chromatograms did not resolve differences in leaf metabolite profiles between wild-type and transgenic plants of the same genetic background, although stem phenolic profiles were clearly different between wild-type and transgenic plants.