# The MEME Suite of motif-based sequence analysis tools

> **NIH NIH R01** · UNIVERSITY OF NEVADA RENO · 2020 · $185,538

## Abstract

Project summary/abstract
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 modifications 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 significant 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 scientific discovery and leading to
insights into a wide spectrum of fundamental processes in molecular biology and human disease.

## Key facts

- **NIH application ID:** 9960552
- **Project number:** 5R01GM103544-15
- **Recipient organization:** UNIVERSITY OF NEVADA RENO
- **Principal Investigator:** Timothy L Bailey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $185,538
- **Award type:** 5
- **Project period:** 2009-09-28 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9960552

## Citation

> US National Institutes of Health, RePORTER application 9960552, The MEME Suite of motif-based sequence analysis tools (5R01GM103544-15). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9960552. Licensed CC0.

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