The Short Time-series Expression Miner (STEM) is a Java program for
clustering, comparing, and visualizing short time series gene
expression data from microarray experiments
(~8 time points or fewer). STEM allows
researchers to identify significant temporal expression profiles and the
genes associated
with these profiles and to compare the behavior of these genes across
multiple conditions.
STEM is fully integrated with the
Gene Ontology (GO)
database supporting GO category
gene enrichment analyses for sets of genes having the same temporal
expression pattern. STEM also supports the ability to easily determine
and visualize the behavior of genes belonging to a given GO category
or user defined gene set,
identifying which
temporal expression profiles were enriched for these genes. (Note:
While
STEM is designed primarily to analyze data from short time course
experiments it
can be used to analyze data from
any small set of experiments which can naturally be ordered sequentially
including dose
response experiments.)
The Short Time-series Expression Miner (STEM)
version 1.3.8 (version log)
is available for free to academic and non-profit users
under a non-commercial research use license. Academic and non-profit
users -
click
here to register and download STEM.
Commercial users - click here to
download STEM under a free evaluation license.
STEM requires Java
1.4 or later to also be
installed.
Email any questions or comments about STEM to
Jason Ernst (jernst@cs.cmu.edu).
Click here for answers to frequently asked
questions.
Individuals contributing to the development of STEM are: Jason Ernst,
Dima Patek, and Ziv Bar-Joseph
The interface of
STEM makes use of the open source software Piccolo and
Batik. The Piccolo
and Batik libraries that STEM uses are included with the STEM download.
Funding for STEM was supported in part by NIH grant NO1 AI-5001
and NSF CAREER award 0448453 to ZBJ