MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways.

ABSTRACT:

Motivation

Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance.

Results

We present an interactive a

SUBMITTER: Rusilowicz MJ 

PROVIDER: S-EPMC6205582 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature