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Exon Recombination

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes.
Exon shuffling follows “splice frame rules.” Each exon...

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

Evolutionary Relationships through Genome Comparisons

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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...

Transduction

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Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome...

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Related Experiment Video

Updated: Mar 10, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Decoding the Ecological Function of Accessory Genome.

Michelle Qiu Carter¹

¹US Department of Agriculture, Agricultural Research Service, Western Regional Research Center, Produce Safety and Microbiology Research Unit, Albany, CA, USA.

Trends in Microbiology

|December 7, 2016

Summary

Shiga toxin-producing Escherichia coli O157:H7 (STEC O157:H7) asymptomatically infects cattle. Machine learning analysis of STEC O157:H7 pan-genome data predicts a small subset of bovine strains can cause human disease.

Keywords:

Shiga toxin-producing Escherichia coli accessory genome enterohemorrhagic Escherichia coli pan-genome support vector machine zoonotic potential

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Area of Science:

Microbiology
Genomics
Infectious Disease Epidemiology

Background:

Shiga toxin-producing Escherichia coli O157:H7 (STEC O157:H7) is a significant foodborne pathogen.
Cattle are a primary reservoir for STEC O157:H7, often asymptomatically carrying the bacteria.
Human infections typically result from consuming contaminated food products.

Purpose of the Study:

To identify specific genetic factors differentiating bovine STEC O157:H7 isolates with human pathogenic potential.
To develop a predictive model for assessing the risk of STEC O157:H7 transmission from cattle to humans.

Main Methods:

Application of a machine-learning approach to analyze complex pan-genome data from bovine STEC O157:H7 isolates.
Comparative genomic analysis to identify virulence-associated genes and genomic islands.

Main Results:

The study predicted that only a small subset of bovine STEC O157:H7 isolates possess the genetic makeup to cause disease in humans.
Identification of specific genomic features associated with enhanced virulence and host adaptation.

Conclusions:

The genetic diversity within bovine STEC O157:H7 populations is substantial.
Machine learning analysis of pan-genome data offers a promising strategy for predicting the zoonotic potential of bacterial pathogens.