Date Published: September 4, 2019
Publisher: Public Library of Science
Author(s): Linden J. Gearing, Helen E. Cumming, Ross Chapman, Alexander M. Finkel, Isaac B. Woodhouse, Kevin Luu, Jodee A. Gould, Samuel C. Forster, Paul J. Hertzog, Manuela Helmer-Citterich.
The availability of large amounts of high-throughput genomic, transcriptomic and epigenomic data has provided opportunity to understand regulation of the cellular transcriptome with an unprecedented level of detail. As a result, research has advanced from identifying gene expression patterns associated with particular conditions to elucidating signalling pathways that regulate expression. There are over 1,000 transcription factors (TFs) in vertebrates that play a role in this regulation. Determining which of these are likely to be controlling a set of genes can be assisted by computational prediction, utilising experimentally verified binding site motifs. Here we present CiiiDER, an integrated computational toolkit for transcription factor binding analysis, written in the Java programming language, to make it independent of computer operating system. It is operated through an intuitive graphical user interface with interactive, high-quality visual outputs, making it accessible to all researchers. CiiiDER predicts transcription factor binding sites (TFBSs) across regulatory regions of interest, such as promoters and enhancers derived from any species. It can perform an enrichment analysis to identify TFs that are significantly over- or under-represented in comparison to a bespoke background set and thereby elucidate pathways regulating sets of genes of pathophysiological importance.
Contemporary transcriptomic technologies such as microarrays and RNA-sequencing provide reliable methods to identify genes differentially expressed across cell types, tissues or in response to different stimuli. These methods reveal many co-expressed genes or gene networks that are together predicted to determine the observed biological responses.
CiiiDER is an intuitive new tool for analysing TFBSs in regulatory regions of interest. It can efficiently scan sequences for potential TFBSs and identify TFBSs that are statistically under- or over- represented. It is user-friendly and produces quality visual outputs to assist researchers to uncover signalling pathways and their controlling TFs in a wide variety of biological contexts. The program, user manual and example data are available at http://www.ciiider.org.