赛德尔:高效计算强大的整体基因网络
Bastian Schiffthaler1, Elena van Zalen1, Alonso R Serrano2
1Department of Plant Physiology, Umea Plant Science Center, Umea University, Umea, Sweden.
Heliyon
|June 14, 2023
概括
赛德软件通过结合多种方法来推断基因调节网络,以减少偏差并提高准确性. 这种方法增强了复杂的基因表达数据中的生物信号检测.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 系统生物学 系统生物学
背景情况:
- 基因调节和共同表达网络对于分析高维基因表达数据至关重要.
- 现有的方法经常受到低信号噪声比率,非线性相互作用和数据集偏差的影响.
- 从多种方法聚合网络可以获得更好的,更强大的结果.
研究的目的:
- 介绍Seidr,这是一个可扩展的软件工具包,用于基因调节和共同表达网络推断.
- 解决现有的网络推断方法的局限性,包括算法偏差和噪音边缘.
- 为科学家提供一个实用的工具来进行最佳实践网络分析.
主要方法:
- 赛德尔建立社区网络以减轻算法偏见.
- 它采用噪声校正的网络骨干来改进网络边缘.
- 该工具包在模型生物中进行了基准测试:Saccharomyces cerevisiae,Drosophila melanogaster和Arabidopsis thaliana.
主要成果:
- 个别网络推断算法对特定的基因-基因相互作用有偏见.
- 赛德尔的社区网络方法表明,在各种基准上,偏见减少,表现强.
- 该软件成功识别了挪威杉干旱压力网络中的关键组件和建议基因功能.
结论:
- 与个人算法相比,Seidr为基因网络推断提供了一种强大的,不那么偏见的方法.
- 社区网络战略有效地减少了算法偏差,提高了可靠性.
- 赛德尔是分析基因表达数据的宝贵工具,即使在非模型物种中也是如此,有助于功能性基因发现.
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