针对 lncRNA 函数和目标组的计算资源
Anamika Thakur1,2, Manoj Kumar3,4
1Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh, India.
Methods in molecular biology (Clifton, N.J.)
|December 20, 2024
概括
本综述总结了用于研究长非编码RNA (lncRNAs) 的计算工具和数据库. 这些资源有助于研究人员了解ncRNA功能及其在各种疾病中的作用.
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.
- 分子生物学分子生物学
背景情况:
- 长非编码RNAs (lncRNAs) 是生物过程中的关键调节者.
- 失调的lncRNA表达与各种疾病有关,表明治疗潜力.
- 为 lncRNA 研究而出现了众多的计算资源.
研究的目的:
- 提供现有的 lncRNA 数据库和预测工具的全面审查.
- 突出 lncRNA 资源在不同生物体 (包括人类和模型生物体) 中的重要性.
- 引导生物学家选择适合他们的研究计算工具.
主要方法:
- 审查 lncRNA 数据库和注册表.
- 分析机器学习算法 (深度学习,SVM,RF) 用于lncRNA识别.
- 汇编了关于lncRNA表达,疾病关联和目标调节的资源.
主要成果:
- 详细概述关键的 lncRNA 数据库和最新的资源.
- 讨论使用各种技术识别 lncRNA 的计算工具.
- 基于数据类型的资源分类,如微分表达式和目标交互.
结论:
- 在 silico 资源对于推进 lncRNA 研究至关重要.
- 这一综述赋予了生物学家掌握可用的计算工具的知识.
- 通过计算方法了解lncRNA对于生物学见解和疾病研究至关重要.
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