Time-course data analysis software tools | Gene expression microarray
The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series.
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.
Serves to parse metric binary outputs by Illumina sequencers. Illuminate is a Python module that parses metric binaries from Illumina sequencer runs and provides usable data in the form of python dictionaries and dataframes. Users can print sequencing run metrics to the command line and work with the data programmatically at the same time. The reading of active sequencing runs for tile, index and quality metrics is also supported.
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.
A package to identify differentially expressed genes in microarray time-course data. BETR explicitly uses the time-dependent structure of the data, employing an empirical Bayes procedure to stabilize estimates derived from the small sample sizes typical in microarray experiments. It is applicable to one- or two-color replicated microarray data, and can be used to detect differences between two conditions or changes from baseline in a single condition. BETR outperforms three commonly used techniques in the analysis of time-course data. This advantage is particularly noticeable for genes with a small but sustained differential expression signal. When the magnitude of differential expression is of similar magnitude to background noise, it is difficult to identify by examining each time point in isolation. These patterns of differential expression become easier to identify when the time series structure of the data is taken into account; a small, noisy signal becomes identifiable if it is sustained across several adjoining time points.
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.