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

Jason Ernst,Ziv Bar-Joseph

doi:10.1186/1471-2105-7-191

Jason Ernst, Ziv Bar-Joseph

Open Access

https://doi.org/10.1186/1471-2105-7-191

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Journal: BMC Bioinformatics	Publication Date: Apr 5, 2006
Citations: 1285	License type: cc-by

Affiliation: Carnegie Mellon University

Abstract

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 .

Highlights

Time series microarray experiments are widely used to study dynamical biological processes
STEM, a new software package for analyzing short time series expression data
STEM presents its analysis of the data in a highly visual and interactive manner, and the integration with Gene Ontology (GO) allows for efficient biological interpretations of the data

Summary

Introduction

Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Microarray time series gene expression experiments are widely used to study a range of biological processes such as the cell cycle [1], development [2], and immune response [3]. Clinical studies, the availability of biological material can limit the number of time points collected. Data from short time series gene expression experiments poses (page number not for citation purposes)

Results

Discussion

Conclusion