Software Notes: 

FunPat works with dynamic expression data comparing two experimental conditions (e.g. treatment vs. control) or a single condition vs. a baseline (e.g. time 0).
FunPat offers the following types of analyses:

– differential expression analysis of dynamic expression data;

– model-based clustering of temporal expression profiles, without requiring the user to fix either a priori or a posteriori the number of clusters;

– Integrated selection-clustering analysis to identify differentially expressed genes which share common dynamic expression profiles and annotations to a specific biological concept, e.g. a pathway or a Gene Ontology term.

FunPat allows a user-friendly organization of the outcome and an advanced visualization through HTML pages for an immediate interpretation of the results. The vignette shows into details the application of FunPat functions to a toy example with simulated RNA-seq data. Since FunPat is not constrained by any specific statistical distributions, the same analysis pipeline can be also applied to data from different technologies, such as microarrays, as well.


This software is written in R language, so it is necessary to have R installed on your computer. For more information and download of R, please refer to For more information about the installation of R packages, please refer to R version 3.0.3 or later is required to be able to install and run FunPat. This package is dependent on Bioconductor package tseries.

To install FunPat from the tar ball, open an R session and type:

install.packages("path_to_FunPat/FunPat_0.99.0.tar.gz",repos=NULL, type="source")

How to get help:

Most questions about FunPat will hopefully be answered by the documentation and references. We always appreciate receiving suggestions for possible improvements of the package, as well as reports of bugs in the package functions or in the documentation. For any questions, please contact us by sending an email to Tiziana Sanavia (tiziana.sanavia @ and Barbara Di Camillo (barbara.dicamillo @


If you use the software for your research, please refer to the original FunPat paper with the citation below.

Sanavia T, Finotello F, Di Camillo B. FunPat: function-based pattern analysis on RNA-seq time series data. BMC Genomics. 2015 Jun; 16(Suppl 6):S2.

Other publications illustrating the methods used in FunPat and possible applications are:

Di Camillo B, Irving BA, Schimke J, Sanavia T, Toffolo G, Cobelli C, Nair KS. Function-based discovery of significant transcriptional temporal patterns in insulin stimulated muscle cells. PLoS One. 2012; 7(3):e32391.

Di Camillo B, Toffolo G, Nair SK, Greenlund LJ, Cobelli C. Significance analysis of microarray transcript levels in time series experiments. BMC Bioinformatics. 2007; 8(Suppl 1):S10.