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Estimating transcriptome complexities across eukaryotes.

James E Titus-McQuillan1, Adalena V Nanni2,3, Lauren M McIntyre2,3

  • 1Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA. jmcquil2@uncc.edu.

BMC Genomics
|May 11, 2023
PubMed
Summary
This summary is machine-generated.

Genomic complexity can now be quantified across species using new metrics that analyze transcriptomes. These metrics reveal how complexity changes over evolutionary time, especially in novel genes.

Keywords:
Effective exon numberEvolutionary ratesNovel genesOrthoDBOrthologsTranDTranscript modelTranscriptome complexity

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Area of Science:

  • Evolutionary genomics
  • Comparative genomics
  • Bioinformatics

Background:

  • Genomic complexity is a critical area in evolutionary biology, yet quantifying it across species remains challenging.
  • Existing methods struggle with the lack of correlation between genome size and complexity.
  • New approaches are needed to accurately measure genomic complexity and its evolutionary trajectory.

Purpose of the Study:

  • To develop and apply novel metrics for quantifying genomic complexity across diverse lineages.
  • To investigate the dynamics of complexity changes at the transcriptomic level.
  • To compare complexity across whole genomes, orthologs, and novel genes.

Main Methods:

  • Development of three new complexity metrics: TpG, EpT, and EpG, to capture alternative splicing dynamics.
  • Utilizing the Effective Exon Number (EEN) metric to analyze exon size distribution.
  • Comparison of complexity metrics across whole genome annotations, ortholog subsets, and novel genes.

Main Results:

  • The study introduces quantifiable metrics for transcriptome complexity, including TpG, EpT, and EpG.
  • Effective Exon Number (EEN) was used to compare exon size distributions against random expectations.
  • Analyses revealed biases in complexity metrics when focusing solely on orthologs, particularly for novel genes.

Conclusions:

  • The developed metrics enable precise quantification of complexity changes across lineages, improving on previous cross-species comparisons.
  • These findings advance whole-transcriptome analysis in evolutionary genomics, offering insights into deep evolutionary timescales.
  • The study provides methods to quantify ortholog calling biases and correct for lineage-specific effects, particularly for newly evolved genes.