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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Non-parametric and semi-parametric support estimation using SEquential RESampling random walks on biomolecular

Wei Wang1, Jack Smith1, Hussein A Hejase2

  • 11Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 USA.

Algorithms for Molecular Biology : AMB
|April 24, 2020
PubMed
Summary
This summary is machine-generated.

We introduce Sequential Resampling (SERES), a novel method for biomolecular sequence analysis. SERES overcomes limitations of standard bootstrap methods by accounting for sequence dependence, improving support estimation in bioinformatics.

Keywords:
BootstrapMultiple sequence alignmentNon-parametricRandom walkResamplingSemi-parametricStatistical support

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Resampling methods like bootstrap are crucial for support estimation in computational biology.
  • The standard bootstrap method assumes independent and identically distributed (i.i.d.) data, which is often violated in biomolecular sequences.
  • Sequential dependence in biological sequences is biologically relevant and impacts analysis.

Purpose of the Study:

  • To develop a new non-parametric/semi-parametric sequential resampling technique.
  • To relax the restrictive i.i.d. assumption in biomolecular sequence analysis.
  • To introduce the Sequential Resampling (SERES) method.

Main Methods:

  • Proposed a novel sequential resampling technique generalizing mirrored inputs.
  • Developed a method using random walks along biomolecular sequences (aligned or unaligned).
  • Applied the SERES method for support estimation in multiple sequence alignment.

Main Results:

  • SERES was applied to estimate support for multiple sequence alignment.
  • Performance was evaluated using both simulated and empirical data.
  • SERES demonstrated comparable or superior performance to existing state-of-the-art methods.

Conclusions:

  • The proposed SERES method effectively addresses the limitations of i.i.d. assumptions in sequence analysis.
  • SERES provides a robust approach for support estimation in computational biology and bioinformatics.
  • This novel technique offers improved accuracy for analyzing biomolecular sequences.