STEM: a tool for the analysis of short time series gene expression data

By Jason Ernst, Ziv Bar-Joseph. BMC Bioinformatics, 2006

1,557 citations recorded by the source corpus.

BackgroundTime series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.ResultsWe introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology.ConclusionThe unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at http://www.cs.cmu.edu/~jernst/stem.

Subfields: Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6e806ba4d67238ab5a4b5c8600445b61a43397df

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