Time-course data analysis software tools | Gene expression microarray

Assists biologists in understanding the temporal progression of genetic events and biological processes following a stimulus, based on gene expression microarray data. StepMiner includes features for identifying genes which undergo one or more binary transitions. This program permits users to extract three types of binary temporal patterns. Moreover, this tool is useful for researchers interested in binary models of gene expression time courses.

A 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.

Allows the analysis of gene expression data. EXPANDER gives the user access to a range of microarray analysis algorithms covering the complete analysis process: preprocessing (2) visualizing (3) clustering (4) biclustering and (5) performing downstream analysis of clusters and biclusters such as functional enrichment and promoter analysis. The software incorporates several conventional gene expression analysis algorithms.

Allows the analysis of multiple time course transcriptomics data. maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. The software incorporates a clustering function to visualize genes with similar profiles. maSigPro was initially developed for microarrays and later updated to model count data. It includes Iso-maSigPro, a functionality to study differential isoform usage in time course RNA-seq experiments.

Enables chromatin states prediction based on time-course epigenetic marks data. GATE is a hierarchical model with two layers: (1) a FMM, in which each component of the mixture represents a cluster of genomic segments that share temporal epigenomic patterns and (2) a bottom layer that models the time-course epigenomic data in each cluster. The software models epigenomic data as a dynamic continuum and directly utilizes temporal information to annotate epigenomic states. It was tested with simulated cell differentiation processes.