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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Related Experiment Video

Updated: May 2, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

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STEME: a robust, accurate motif finder for large data sets.

John E Reid1, Lorenz Wernisch1

  • 1MRC Biostatistics Unit, Institute of Public Health, Cambridge, United Kingdom.

Plos One
|March 15, 2014
PubMed
Summary

STEME is an efficient motif finder suitable for large datasets, offering comparable performance to existing tools and finding long degenerate motifs. Its source code is open-source and accessible via a web interface.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif finding is crucial in biological sequence analysis but challenged by large datasets from next-generation sequencing.
  • Existing motif finders like MEME, while popular, are often unsuitable for massive datasets.
  • The Expectation-Maximization (EM) algorithm is central to many motif finders but computationally intensive.

Purpose of the Study:

  • To introduce STEME, an efficient motif finder designed for large-scale biological data analysis.
  • To present extensions to STEME that enhance its capabilities, making it a fully-featured motif discovery tool.
  • To provide a robust and accessible tool for identifying DNA motifs, including complex degenerate patterns.

Main Methods:

  • Developed STEME, an efficient approximation to the EM algorithm for motif discovery.

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  • Integrated several extensions to STEME, drawing inspiration from the MEME algorithm.
  • Extended a prior method for efficient calculation of motif E-values.
  • Evaluated STEME against prominent discriminative motif finders (DREME, Trawler) using mouse ES cell transcription factor binding data.

    • Main Results:

    • STEME demonstrates performance comparable to DREME and Trawler on transcription factor binding data.
    • STEME successfully identifies long degenerate motifs, a capability lacking in some discriminative motif finders.
    • The extended STEME tool functions as a fully-fledged motif finder with properties similar to MEME.

    Conclusions:

    • STEME is a powerful and efficient tool for motif finding in large biological datasets.
    • STEME offers advantages in identifying complex motif patterns, such as long degenerate motifs.
    • The open-source availability and web interface of STEME promote its accessibility and adoption in biological research.