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从数百万个随机序列中学习替代拼接的序列决定因素
Alexander B Rosenberg1, Rupali P Patwardhan2, Jay Shendure2
1Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA.
Cell
|October 27, 2015
概括
研究人员开发了先进的模型来了解RNA拼接. 他们的发现揭示了普遍控制替代拼接的序列模式, 改善了引起疾病的突变的预测.
科学领域:
- 分子生物学
- 遗传学
- 生物信息学
背景情况:
- 替代拼接是基因表达的一个关键过程,从单个基因生成多种蛋白质异型.
- 替代拼接的失调与众多人类疾病有关,强调需要更好的预测模型.
研究的目的:
- 开发改进的计算模型来预测替代拼接模式.
- 确定调节替代拼接决策的序列决定因素.
- 获得对替代拼接过程的机械洞察力.
主要方法:
- 产生和高吞吐量测序超过200万个合成微基因与退化的次序.
- 在广泛的拼接模式数据上训练机器学习模型.
- 对连接点选择的序列图案效应的分析.
主要成果:
- 发现绝大多数六合体序列图案对结合部位的选择有显著影响.
- 证明了多个动机的附加效应,而不是合作相互作用.
- 发现拼接图案在不同的拼接模式中表现出保留的调节作用 (5',3',磁带外子).
结论:
- 开发了基于序列决定因素的替代拼接的高度预测模型.
- 确定序列动机在替代异构体识别中起到普遍作用.
- 经验验证的模型准确地预测了自然遗传变异对体内替代拼接的影响.
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