CoSIA:一个R生物导体包用于CROss物种调查和分析
Anisha Haldar1, Vishal H Oza1, Nathaniel S DeVoss1
1The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, United States.
Bioinformatics (Oxford, England)
|December 18, 2023
概括
我们创建了CoSIA (跨物种调查和分析),一个R包和应用程序,以简化跨物种基因表达的比较. 它解决了高通量测序数据分析的挑战,以获得更好的生物见解.
科学领域:
- 进行比较的转录学.
- 生物信息学是一种生物信息学.
- 基因表达分析 基因表达分析
背景情况:
- 高通量测序使跨物种的转录组学研究成为可能.
- 生物和技术因素在比较转录学学中提出了挑战.
- 现有的框架可能无法充分解决这些复杂性.
研究的目的:
- 开发一种用于跨物种转录基因比较的新型框架.
- 为分析各种物种的基因表达数据提供可访问的工具.
- 为了促进可视化变异性,多样性和特异性的转录数据.
主要方法:
- 开发 CoSIA (跨物种调查和分析) 作为生物导体 R 包和 Shiny 应用程序.
- 利用了Bgee数据库中的基因表达数据,用于未患病的野生类型生物.
- 包括人类,老鼠,老鼠,斑马鱼,和线虫等物种.
- 专注于不同组织的比较分析.
主要成果:
- CoSIA提供了一个用户友好的界面,用于跨物种的转录组数据探索.
- 该包可视化关键指标,如变化,多样性和特异性.
- 能够对跨多个物种和组织的RNA测序数据进行可靠的比较.
结论:
- CoSIA为跨物种转录基因分析提供了一个有价值的替代框架.
- 该工具有助于研究人员克服比较转录组学方面的挑战.
- 有助于更深入地了解跨物种的基因表达模式.
相关概念视频
Multi-species Conserved Sequences
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Biostatistics: Overview
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...


