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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

Detecting correlations among functional-sequence motifs.

Davide Pirino1, Jacopo Rigosa, Alice Ledda

  • 1LEM, Scuola Superiore Sant'Anna, 56127 Pisa, Italy. d.pirino@sssup.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to analyze DNA sequence motifs and their interactions. The findings reveal new insights into genome maintenance functions and interactions among motifs.

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Last Updated: May 18, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Peptide-based Identification of Functional Motifs and their Binding Partners
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Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sequence motifs are functional DNA elements crucial for biological processes like gene regulation.
  • Identifying motif functions often involves statistical modeling and analyzing their genomic distribution.
  • Understanding interactions between motifs and other genomic features provides deeper biological insights.

Purpose of the Study:

  • To develop and apply a novel statistical model for analyzing sequence motif occurrences and their interactions within a genome.
  • To investigate excitatory and inhibitory relationships among DNA motifs and between motifs and gene occurrences.
  • To uncover novel biological functions of specific motifs, particularly those involved in genome maintenance.

Main Methods:

  • Utilized a log-linear multivariate intensity Poisson model.
  • Employed the expectation-maximization algorithm for model parameter estimation.
  • Analyzed a set of sequence motifs across the E. coli K12 genome.

Main Results:

  • Successfully estimated the proposed statistical model on genomic data.
  • Confirmed known interaction patterns between motifs and genomic features.
  • Identified specific motifs with significant roles in genome maintenance functions through their interaction structures.

Conclusions:

  • The developed log-linear multivariate intensity Poisson model effectively captures complex interactions among genomic sequence motifs.
  • The findings provide new perspectives on the functional roles of motifs in genome maintenance.
  • The methodology is extensible to analyzing other genomic features and complex biological patterns.