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Related Concept Videos

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Related Experiment Video

Updated: Jun 26, 2026

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications
05:41

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Published on: July 10, 2020

Learning to count: robust estimates for labeled distances between molecular sequences.

John D O'Brien1, Vladimir N Minin, Marc A Suchard

  • 1Department of Biomathematics, University of California, Los Angeles, USA.

Molecular Biology and Evolution
|January 10, 2009
PubMed
Summary
This summary is machine-generated.

Robust counting offers a new method for estimating molecular sequence distances, reducing bias from inaccurate models. This approach improves accuracy for evolutionary studies, including phylogenetic analysis of HIV transmission networks.

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Area of Science:

  • Molecular evolution
  • Bioinformatics
  • Computational biology

Background:

  • Continuous-time Markov chain models are standard for estimating molecular sequence distances.
  • Model misspecification can introduce significant bias in these distance estimates.
  • Accurate distance estimation is crucial for understanding evolutionary dynamics and constructing phylogenetic trees.

Purpose of the Study:

  • To introduce a novel method, robust counting, for estimating molecular sequence distances.
  • To mitigate bias caused by model misspecification in distance calculations.
  • To enable accurate estimation of labeled codon distances, such as synonymous and nonsynonymous substitutions.

Main Methods:

  • Generalizing conventional distance estimation to include empirical site pattern distributions from sequence alignments.
  • Developing a flexible framework for computing distances using subsets of possible substitutions.
  • Comparing robust and conventional estimators through simulation studies.

Main Results:

  • Robust counting significantly reduces bias compared to conventional methods, especially under model misspecification.
  • Accurate synonymous and nonsynonymous distance estimates can be obtained using simpler nucleotide substitution models.
  • The method effectively bypasses the need for computationally intensive codon models.

Conclusions:

  • Robust counting provides a more reliable approach to molecular sequence distance estimation.
  • The method enhances the accuracy of evolutionary analyses, including phylogenetic reconstruction.
  • Empirical examples demonstrate its utility in studying influenza evolution and mitigating convergent evolution effects in HIV phylogenetic analysis.