CiiiDER: A tool for predicting and analysing transcription factor binding sites

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

doi:10.1371/journal.pone.0215495

CiiiDER: A tool for predicting and analysing transcription factor binding sites

PLoS One. 2019 Sep 4;14(9):e0215495. doi: 10.1371/journal.pone.0215495. eCollection 2019.

Authors

Linden J Gearing^{1

2}, Helen E Cumming^{1

2}, Ross Chapman^{1

2}, Alexander M Finkel^{1

2}, Isaac B Woodhouse^{1

2}, Kevin Luu^{1

2}, Jodee A Gould^{1

2}, Samuel C Forster^{1

2}, Paul J Hertzog^{1

2}

Affiliations

¹ Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, Victoria, Australia.
² Department of Molecular Translational Science, Monash University, Clayton, Victoria, Australia.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Binding Sites*
Chromatin Immunoprecipitation Sequencing
Computational Biology / methods*
Protein Binding
Software*
Transcription Factors / metabolism*
Workflow

Substances

Transcription Factors

Grants and funding

The authors would like to acknowledge the support and contributions of the Bio21 Undergraduate Research Opportunity Program (A.M.F and S.C.F.) and the Victorian Life Sciences Computation Initiative (S.C.F.) for support in this project. Research at Hudson Institute of Medical Research is supported by the Victorian Government’s Operational Infrastructure Support Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.