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Evolutionary Relationships through Genome Comparisons02:54

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis.

Jens Keilwagen1, Jan Grau, Stefan Posch

  • 1Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany. Jens.Keilwagen@ipk-gatersleben.de

BMC Bioinformatics
|March 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized prior distribution for Markov random field models in bioinformatics. This new prior enables direct comparisons of learning principles, revealing that neglecting prior distributions can lead to inaccurate conclusions in DNA sequence analysis.

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07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Sequence Analysis

Background:

  • Accurate recognition of short DNA signal sequences is crucial for understanding cellular processes.
  • Existing algorithms often use Markov random field models but overlook the impact of prior distributions.
  • This oversight can lead to questionable conclusions in comparative studies.

Purpose of the Study:

  • To develop a generalized prior distribution for Markov random field models.
  • To enable direct comparisons of different learning principles using consistent prior information.
  • To investigate the influence of prior distributions on model performance in DNA sequence recognition.

Main Methods:

  • Derived a generalization of the product-Dirichlet prior distribution.
  • Analyzed the behavior of the derived prior (Gaussian-like near maximum, Laplace-like in tails).
  • Applied the prior in case studies for transcription factor binding site and splice site recognition.

Main Results:

  • The generalized prior facilitates direct comparison of learning principles.
  • Demonstrated utility in recognizing Sp1 transcription factor binding sites.
  • Showcased effectiveness in identifying human donor splice sites.

Conclusions:

  • Comparisons of learning principles are sensitive to the choice of prior distributions.
  • Neglecting prior effects can yield different results than previously reported.
  • The new prior is implemented in the Jstacs library for broader application in sequence analysis.