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Related Experiment Videos

Identifying uniformly mutated segments within repeats.

S Cenk Sahinalp1, Evan Eichler, Paul Goldberg

  • 1School of Computing Science, Simon Fraser University, Canada. cenk@cs.sfu.ca

Journal of Bioinformatics and Computational Biology
|December 24, 2004
PubMed
Summary
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This study introduces an algorithm to detect if a data string originates from a single random source. It efficiently identifies variations in mutation rates, aiding in genomic sequence analysis and distinguishing functional regions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing long genomic sequences requires methods to detect variations in their generation processes.
  • Identifying functional genomic regions and understanding evolutionary copy events are critical in bioinformatics.

Purpose of the Study:

  • To develop an algorithm for determining if a character string originates from a single independent and identically distributed (i.i.d.) random source.
  • To assess the likelihood of n-coin models for generating binary strings and compare models with varying numbers of sources.

Main Methods:

  • An algorithm employing dynamic programming is presented, running in O(l^4 log l) time, where l is the string length.
  • The method evaluates the likelihood of n-coin models, considering mutation rates and copying event probabilities with a uniform prior.

Related Experiment Videos

  • It determines if a single source (n=1) is more probable than multiple sources (n>1) based on a posteriori probabilities.

    • Main Results:

    • The algorithm efficiently determines the most probable number of random sources generating a given string.
    • It successfully exploits the convexity of the a posteriori probability for n in its dynamic programming approach.

    Conclusions:

    • The developed algorithm can distinguish between single and multiple i.i.d. random sources for character string generation.
    • This method has significant applications in identifying functional genomic regions and analyzing complex evolutionary copy events in genome sequences.