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Related Experiment Video

Updated: May 23, 2026

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina
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Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

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Statistical significance of optical map alignments.

Deepayan Sarkar1, Steve Goldstein, David C Schwartz

  • 1Theoretical Statistics and Mathematics Unit, Indian Statistical Institute, New Delhi, India.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 18, 2012
PubMed
Summary

The Optical Mapping System aids in analyzing large genomic datasets to build restriction maps. This study introduces methods to filter inaccurate maps, improving genome assembly and identifying structural variations.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Optical mapping generates genome-wide restriction maps from individual DNA molecules.
  • Mammalian genome analysis presents unique computational and statistical challenges compared to microbial genomes.
  • Accurate genome structure and variation analysis is crucial for understanding complex biological systems.

Purpose of the Study:

  • To develop methods for filtering poorly aligned restriction maps.
  • To enhance the accuracy and efficiency of iterative genome assembly.
  • To identify structural genomic abnormalities using alignment scores.

Main Methods:

  • Development of map-specific thresholds for filtering low-quality alignments.
  • Utilizing optimal self-alignment scores as a proxy for alignment probability.

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High-Throughput Analysis of Optical Mapping Data Using ElectroMap
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High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

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Last Updated: May 23, 2026

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina
07:02

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

Published on: June 30, 2023

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

  • Iterative assembly algorithms incorporating improved map filtering.

    • Main Results:

    • Map-specific thresholds effectively control errors and enhance iterative genome assembly.
    • Optimal self-alignment scores accurately approximate alignment probabilities.
    • The developed methods improve the identification of structural genomic variations.

    Conclusions:

    • The study provides robust computational and statistical solutions for optical mapping data analysis.
    • Improved map filtering and alignment scoring facilitate more accurate mammalian genome analysis.
    • These advancements are critical for applications in structural variation detection and comparative genomics.