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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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A phylogenetic approach for weighting genetic sequences.

Nicola De Maio1, Alexander V Alekseyenko2,3, William J Coleman-Smith2

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK. demaio@ebi.ac.uk.

BMC Bioinformatics
|May 29, 2021
PubMed
Summary

We introduce phylogenetic novelty scores to weight sequences in bioinformatics. These scores improve accuracy in tasks like protein family profiling by accounting for evolutionary novelty.

Keywords:
AlignmentConservation scoresPhylogeneticsProtein profileSequence weights

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Sequence weighting schemes are crucial in bioinformatics for applications like sequence alignment and protein family profiling.
  • Existing methods aim to reduce bias from non-independent homologous sequences and uneven taxon representation.
  • These schemes assign weights based on sequence 'novelty' relative to the dataset.

Purpose of the Study:

  • To rigorously define and introduce 'phylogenetic novelty scores' for sequence weighting.
  • To develop computationally efficient algorithms for calculating these scores.
  • To demonstrate the utility of phylogenetic novelty scores in improving downstream bioinformatics analyses.

Main Methods:

  • Formalized the principle of evolutionary novelty for sequences within an alignment.
  • Developed algorithms for efficient computation of phylogenetic novelty scores.
  • Utilized simulations to evaluate the performance of the new scoring system.

Main Results:

  • Introduced novel 'phylogenetic novelty scores' with desirable properties.
  • Demonstrated application in inferring character frequencies at alignment columns.
  • Showcased improved accuracy in character frequency estimation compared to existing schemes via simulations.

Conclusions:

  • Phylogenetic novelty scores offer an evolutionarily meaningful approach to adjust for uneven taxon sampling.
  • Potential applications include estimating evolutionary conservation, generating sequence logos, and enhancing sequence alignment accuracy.
  • These scores are valuable for various bioinformatics tasks requiring robust handling of sequence data diversity.