The broad goal of this project is to develop, apply and support computational tools for detecting, modeling and understanding biologically important sequence patterns, called motifs, encoded in the genome, in RNA and in proteins. Sequence motifs carry much of the information essential to the correct functioning of cells. For example, motifs in genomic DNA contain information that helps to regulate gene expression. Sequence motifs in RNA encode splice junctions and regulatory information such as microRNA binding sites. At the protein level, sequence motifs may participate in enzymatic binding sites, provide anchors for protein structures or mediate post-translational modi?cations such as phosphorylation by kinases. The MEME Suite provides a range of software tools for modeling biological sequence patterns using statis- tical models that capture local sequence patterns while allowing for naturally occurring variability. The MEME Suite webserver constitutes an important and heavily used resource for basic and applied biological research. In 2016 alone, more than 38,000 unique users utilized the MEME Suite web portal, and the number of users has been steadily growing. As of June 19, 2017, the papers describing the MEME Suite have been cited 14,388 times, according to Google scholar. In the proposed project, we aim to add signi?cant new functionality to the MEME Suite and to improve the robustness, reliability and usability of the software. In particular, we will enhance the MEME motif discovery algorithm to greatly improve its ability to discover subtle motifs of any width in any type of biosequence, and we will expand and improve the MEME Suite's motif analysis pipeline by incorporating knowledge of the genome, gene expression and chromatin contacts for model organisms. This will allow, among other things, for improved prediction of the target genes regulated by transcription factor motifs. We will also carry out a series of software engineering and usability improvements that will greatly enhance the overall user experience. Our software can be locally installed or run remotely through our web portal to perform a diverse set of analyses on large, complex genomic and proteomic data sets. It is in widespread use by scientists around the world.
We aim to continue to maintain and develop this software, facilitating scienti?c discovery and leading to insights into a wide spectrum of fundamental processes in molecular biology and human disease.

Public Health Relevance

This project will improve the existing, widely-used MEME Suite software ( that enables biologists to discover and understand how nature uses patterns, called motifs, in DNA, RNA and protein molecules. Identifying and accurately characterizing functional motifs allows scientists to understand how genes are turned on and off and how proteins carry out their functions in the cell. Such knowledge will help us build models of the basic molecular mechanisms of the cell, and in particular, to build molecular-scale models of disease processes.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Ravichandran, Veerasamy
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Nevada Reno
Schools of Medicine
United States
Zip Code
Ilsley, Melissa D; Gillinder, Kevin R; Magor, Graham W et al. (2017) Krüppel-like factors compete for promoters and enhancers to fine-tune transcription. Nucleic Acids Res 45:6572-6588
Overman, Jeroen; Fontaine, Frank; Moustaqil, Mehdi et al. (2017) Pharmacological targeting of the transcription factor SOX18 delays breast cancer in mice. Elife 6:
Grant, Charles E; Johnson, James; Bailey, Timothy L et al. (2016) MCAST: scanning for cis-regulatory motif clusters. Bioinformatics 32:1217-9
O'Connor, Timothy; Bodén, Mikael; Bailey, Timothy L (2016) CisMapper: predicting regulatory interactions from transcription factor ChIP-seq data. Nucleic Acids Res :
Gillinder, Kevin R; Ilsley, Melissa D; Nébor, Danitza et al. (2016) Promiscuous DNA-binding of a mutant zinc finger protein corrupts the transcriptome and diminishes cell viability. Nucleic Acids Res :
Bailey, Timothy L; Johnson, James; Grant, Charles E et al. (2015) The MEME Suite. Nucleic Acids Res 43:W39-49
Lim, Jonathan W C; Donahoo, Amber-Lee S; Bunt, Jens et al. (2015) EMX1 regulates NRP1-mediated wiring of the mouse anterior cingulate cortex. Development 142:3746-57
Ma, Wenxiu; Noble, William S; Bailey, Timothy L (2014) Motif-based analysis of large nucleotide data sets using MEME-ChIP. Nat Protoc 9:1428-50
Lesluyes, Tom; Johnson, James; Machanick, Philip et al. (2014) Differential motif enrichment analysis of paired ChIP-seq experiments. BMC Genomics 15:752
Tanaka, Emi; Bailey, Timothy L; Keich, Uri (2014) Improving MEME via a two-tiered significance analysis. Bioinformatics 30:1965-73

Showing the most recent 10 out of 13 publications