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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...

Gene Evolution - Fast or Slow?

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.
In contrast, regions which code...

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

Genome Size and the Evolution of New Genes

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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相关实验视频

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

基因:评估基因模型与进化和机器学习.

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

概括

机器学习工具reelGene准确评估玉米中的基因模型,识别了28%的转录模型是不正确的或非功能性的,改善了基因组注释质量.

关键词:

进化演化演化演化演化演化演化演化基因注释基因注释基因模型基因模型基因组生物学基因组生物学机器学习是机器学习.玉米玉米玉米是一种

更多相关视频

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

相关实验视频

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

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

基因组组装和注释对于理解基因功能至关重要.
从每个注释组合中产生新的基因模型,导致不一致.
标注错误源于伪基因错误分类,转位子活动和内置保留.

研究的目的:

开发reelGene,一个用于评估基因模型预测的机器学习管道.
为了评估Zea mays ssp.中的基因模型的准确性. 玉米 (maize) 是一种玉米.
为研究基因组生物学和基因功能提供资源.

主要方法:

reelGene采用了一系列机器学习模型,重点关注转录边界,mRNA完整性和蛋白质结构.
模型利用序列特征和跨相关种群的进化保护.
机器学习模型根据保存的进化语法预测基因功能.

主要成果:

reelGene评估了玉米中的180万个转录模型,将28%的转录模型归类为错误注释或非功能.
该工具证实92.2%的玉米蛋白质组基因和99.2%的古典玉米基因是功能性的.
分析显示,10.3%的可用基因是功能性的,并且在重复基因中对M1亚基因组保留有30%的偏差.

结论:

reelGene是一种有效的工具,用于评估玉米和相关物种 (如和miscanthus) 的基因模型准确性.
该管道有助于研究基因组生物学,识别功能性可用基因和基因保留中的偏差.
reelGene可以通过MaizeGDB作为浏览器轨道和研究人员的Shiny App访问.