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Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Published on: May 31, 2011

Direct vs 2-stage approaches to structured motif finding.

Maria Federico1, Mauro Leoncini, Manuela Montangero

  • 1Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena e Reggio Emilia, 41125 Modena, Via Campi 213/b, Italy. leoncini@unimore.it.

Algorithms for Molecular Biology : AMB
|August 23, 2012
PubMed
Summary
This summary is machine-generated.

We introduce SISMA, a novel two-stage computational tool for discovering DNA structured motifs. SISMA effectively identifies Transcription Factor Binding Sites (TFBSs) and outperforms existing methods on benchmark datasets.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • DNA motifs abstractly model Transcription Factor Binding Sites (TFBSs) crucial for gene regulation.
  • DNA structured motifs represent ordered sets of TFBSs coordinating gene regulation in higher eukaryotes.
  • Discovering structured motifs is computationally challenging, with existing methods using direct or combined approaches.

Purpose of the Study:

  • To develop and present SISMA, a de-novo computational tool for discovering DNA structured motifs.
  • To evaluate the advantages of a two-stage approach for structured motif discovery compared to direct methods.
  • To provide a software solution for the general problem of structured motif discovery, addressing a gap in available tools.

Main Methods:

  • SISMA employs an exact, enumerative two-stage algorithm.
  • Stage 1: Discovery of all potential component simple motifs.
  • Stage 2: Combination of simple motifs respecting defined constraints.

Main Results:

  • SISMA successfully discovers DNA structured motifs de-novo.
  • Evaluated against existing tools on synthetic and real datasets, SISMA demonstrated superior performance in many cases.
  • The tool exhibits good performance even when not outperforming competitors.

Conclusions:

  • The two-stage approach offers significant advantages over direct methods, including modularity and parallelization.
  • SISMA's performance suggests the viability of two-stage algorithms for structured motif discovery.
  • For difficult instances, a hybrid approach combining direct and two-stage methods may be optimal.