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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

Genome Size and the Evolution of New Genes

Genome Size and the Evolution of New Genes

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Related Experiment Video

Updated: Jan 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Fishing for a reelGene: evaluating gene models with evolution and machine learning.

Aimee J Schulz¹, Jingjing Zhai², Taylor AuBuchon-Elder³

¹Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York, 14853, USA.

The Plant Journal : for Cell and Molecular Biology

|September 22, 2025

Summary

reelGene, a machine learning tool, accurately evaluates gene models in maize, identifying 28% of transcript models as incorrect or non-functional, improving genome annotation quality.

Keywords:

evolution gene annotation gene models genome biology machine learning maize

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Last Updated: Jan 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome assembly and annotation are crucial for understanding gene function.
New gene models arise from each annotated assembly, leading to inconsistencies.
Annotation errors stem from pseudogene misclassification, transposon activity, and intron retention.

Purpose of the Study:

To develop reelGene, a machine learning pipeline for evaluating gene model predictions.
To assess the accuracy of gene models in Zea mays ssp. mays (maize).
To provide a resource for investigating genome biology and gene function.

Main Methods:

reelGene employs a pipeline of machine learning models focusing on transcription boundaries, mRNA integrity, and protein structure.
Models utilize sequence characteristics and evolutionary conservation across related taxa.
Machine learning models predict gene function based on conserved evolutionary grammar.

Main Results:

reelGene evaluated 1.8 million transcript models in maize, classifying 28% as incorrectly annotated or non-functional.
The tool confirmed 92.2% of maize proteome genes and 99.2% of classical maize genes as functional.
Analysis revealed 10.3% of dispensable genes are functional and a 30% bias towards M1 subgenome retention in duplicate genes.

Conclusions:

reelGene is an effective tool for evaluating gene model accuracy in maize and related species like sorghum and miscanthus.
The pipeline aids in investigating genome biology, identifying functional dispensable genes and biases in gene retention.
reelGene is accessible via MaizeGDB as a browser track and Shiny App for researchers.