STEM分析.pdf

BMC Bioinformatics BioMed Central Software Open Access STEM: a tool for the analysis of short time series gene expression data Jason Ernst* and Ziv Bar-Joseph Address: Center for Automated and Learning and Discovery, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA Email: Jason Ernst* - jernst@; Ziv Bar-Joseph - zivbj@ * Corresponding author Published: 05 April 2006 Received: 12 December 2005 Accepted: 05 April 2006 BMC Bioinformatics2006, 7:191 doi:10.1186/1471-2105-7-191 This article is available from: /1471-2105/7/191 © 2006Ernst and Bar-Joseph; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Time 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.