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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where the...
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Related Experiment Video

Updated: Nov 11, 2025

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

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STREME: accurate and versatile sequence motif discovery.

Timothy L Bailey1

  • 1Department of Pharmacology, University of Nevada, Reno, NV 89557, USA.

Bioinformatics (Oxford, England)
|March 24, 2021
PubMed
Summary
This summary is machine-generated.

The STREME algorithm enhances ab initio motif discovery for DNA, RNA, and protein sequences. It offers superior accuracy, sensitivity, and versatility compared to existing tools, with features for large datasets and various alphabets.

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Last Updated: Nov 11, 2025

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence motif discovery algorithms identify functional patterns in biological sequences like DNA, RNA, and proteins.
  • Identifying binding sites for DNA- and RNA-binding proteins is a key application.

Purpose of the Study:

  • To present the STREME algorithm, an advancement in ab initio motif discovery.
  • To demonstrate STREME's superior accuracy, sensitivity, and versatility compared to existing methods.

Main Methods:

  • Utilized in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data for motif discovery.
  • Validated discovered motifs against reference motifs from in vitro data.
  • Compared STREME's performance against DREME, HOMER, MEME, Peak-motifs, ProSampler, and Weeder.

Main Results:

  • STREME demonstrated higher accuracy, sensitivity, and thoroughness than compared algorithms.
  • STREME can analyze large datasets (hundreds of thousands of sequences) and discover motifs of varying lengths (3-30 positions).
  • The algorithm supports differential motif discovery and works with diverse alphabets (DNA, RNA, protein, user-defined).

Conclusions:

  • STREME represents the state-of-the-art in ab initio motif discovery.
  • The algorithm provides statistically significant motif estimates, enhancing reliability.
  • STREME is user-friendly, available via web server or command line, and integrated with the MEME Suite.