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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Published on: August 3, 2018

Periodic pattern detection in sparse boolean sequences.

Ivan Junier1, Joan Hérisson, François Képès

  • 1Epigenomics Project, Genopole, CNRS UPS3201, UniverSud Paris, University of Evry, Genopole Campus 1 - Genavenir 6, 5 rue Henri Desbruères - F-91030 EVRY cedex, France. Francois.Kepes@epigenomique.genopole.fr.

Algorithms for Molecular Biology : AMB
|September 14, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm to detect periodic patterns in sparse gene sequences, even with noisy or incomplete data. The method enhances the analysis of gene regulation and chromosome structure by identifying positional gene scores.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Genetics

Background:

  • Gene positions along DNA reflect chromosome structure and genetic regulation.
  • Previous studies identified periodic trends in gene distances but were limited by small gene groups and data contamination.
  • Gene sequences are often sparse and lack perfect ordering, posing challenges for statistical analysis.

Purpose of the Study:

  • To develop an algorithm for highlighting periodic patterns in sparse boolean sequences.
  • To address limitations of existing methods in analyzing gene positions and regulatory relationships.
  • To create a robust tool for analyzing gene transcription start sites.

Main Methods:

  • An algorithm designed to detect periodic patterns in sparse boolean sequences (0s and 1s).
  • Exploitation of alignment properties of periodic points in solenoidal coordinates.
  • Development of a method robust to data contamination, missing data, and noise.

Main Results:

  • The algorithm demonstrates robustness against signal distortions like added/deleted data and positional noise.
  • It effectively identifies periodic trends in sparse sequences where Fourier-based methods are less suitable.
  • The framework assigns positional scores to genes, indicating their participation in periodic trends.

Conclusions:

  • The developed algorithm offers an efficient alternative for analyzing periodic patterns in gene sequences.
  • It provides a method to identify specific genes contributing to observed periodic trends.
  • Associated software is publicly available for research applications.