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Related Experiment Videos

GRAM and genfragII: solving and testing the single-digest, partially ordered restriction map problem

C Soderlund1, C Burks

  • 1Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM 87545.

Computer Applications in the Biosciences : CABIOS
|June 1, 1994
PubMed
Summary

GRAM (Genomic Restriction map AsseMbly) aids genome mapping by assembling restriction maps from clone fragments. It uses clustering and stochastic assembly for efficient, man-machine collaborative problem-solving in genomic research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome mapping relies on analyzing restriction fragments from overlapping clones.
  • Experimental data in restriction mapping often contains errors and uncertainties, complicating computational analysis.
  • Existing methods face challenges in accurately assembling restriction maps from complex datasets.

Purpose of the Study:

  • To develop an efficient computational tool, GRAM, for assembling partially ordered restriction maps.
  • To facilitate collaborative problem-solving between humans and machines in genomic restriction mapping.
  • To provide a user-interactive system for querying and editing generated restriction maps.

Main Methods:

  • GRAM employs a clustering algorithm to identify probable sets of restriction fragments.

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  • A stochastic assembly algorithm is used to order fragments, maximizing contiguity.
  • Interactive graphics are integrated to support user-guided map refinement and editing.

    • Main Results:

    • GRAM generates plausible, partially ordered restriction maps with corresponding clone fragment alignments.
    • The system effectively approximates solutions for computationally challenging restriction mapping problems.
    • Performance was validated using genfragII, a simulation program for clone digestion.

    Conclusions:

    • GRAM provides a robust framework for genomic restriction map assembly.
    • The man-machine collaborative approach enhances the accuracy and efficiency of genome mapping.
    • GRAM is actively utilized in ongoing genome mapping projects, demonstrating its practical utility.