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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Evaluating ortholog prediction algorithms in a yeast model clade.

Leonidas Salichos1, Antonis Rokas

  • 1Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.

Plos One
|May 3, 2011
PubMed
Summary

Simple ortholog prediction algorithms like cRBH show high accuracy for evolutionary studies. Complex algorithms struggle with gene duplication events, highlighting the need for robust ortholog delineation methods.

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Accurate ortholog identification is essential for evolutionary studies and functional annotation.
  • A lack of manually curated, genome-scale databases hinders the evaluation of ortholog prediction algorithms.
  • This study evaluates four popular ortholog prediction algorithms against a high-quality curated dataset.

Purpose of the Study:

  • To evaluate the performance of four popular ortholog prediction algorithms: MultiParanoid, OrthoMCL, Reciprocal Best Hit (RBH), and Reciprocal Smallest Distance (RSD).
  • To assess algorithm accuracy, sensitivity, and specificity using a manually curated dataset of orthologous genes.
  • To investigate the impact of whole genome duplication and subsequent gene loss on ortholog prediction accuracy.

Main Methods:

  • Evaluation of four ortholog prediction algorithms (MultiParanoid, OrthoMCL, cRBH, cRSD) against 2,723 curated orthologous gene groups from six Saccharomycete yeasts.
  • Analysis of algorithm performance across a broad parameter range, varying number of species, and in the presence of gene duplication and loss events.
  • Calculation of sensitivity, specificity, accuracy, and false discovery rate for each algorithm.

Main Results:

  • The clustered Reciprocal Best Hit (cRBH) algorithm demonstrated the highest accuracy and specificity, while OrthoMCL showed the highest sensitivity.
  • cRBH and clustered Reciprocal Smallest Distance (cRSD) exhibited superior accuracy and lower false discovery rates across varying species numbers.
  • All evaluated algorithms experienced a dramatic increase in false discovery rates when encountering 'traps' caused by differential gene loss after whole genome duplication.

Conclusions:

  • Simple algorithms like cRBH may be preferable for inferring single-copy orthologs in evolutionary and functional genomics, particularly in phylogenetics.
  • Complex algorithms like OrthoMCL and MultiParanoid may not outperform simpler methods when accurate inference of single-copy orthologs is the primary goal.
  • The accuracy of all ortholog prediction algorithms is significantly compromised in the presence of rampant gene duplication and differential loss.