预测Arabidopsis thaliana的基因表达对环境的反应,使用DNA序列的自然变异
Margarita Takou1, Emily S Bellis1,2, Jesse R Lasky1
1Pennsylvania State University, University Park, 16802, USA.
bioRxiv : the preprint server for biology
|May 7, 2024
概括
机器学习模型在预测Arabidopsis thaliana寒冷反应中的基因表达变化方面取得了适度的成功. 需要进一步开发,以充分捕捉环境适应的 cis-regulatory 基因型-表型映射的复杂性.
科学领域:
- * 进化生物学 进化生物学
- * 基因组学 是一个学科.
- * 生物信息学是一门学科.
背景情况:
- * 了解基因表达进化是适应不断变化的环境的关键.
- * 由于复杂的 cis 调节元素和表观遗传因素,很难预测 DNA 序列对基因表达的影响.
- * 对调控元素的基因型到表型的映射仍然不太了解.
研究的目的:
- * 为了研究机器学习模型是否可以学习cis-regulatory的基因型到表型地图.
- *使用DNA序列预测基因表达中的环境反应 (冷).
- * 评估Cis监管在环境适应中的作用.
主要方法:
- *分析了来自五种不同的Arabidopsis thaliana连接的冷响应转录组数据.
- * 鉴定了差异表达基因 (DEGs) 的cis调节区域中的显著丰富的DNA动机.
- *使用卷积神经网络 (CNN) 来预测DNA序列的基因表达反应.
主要成果:
- * 在对冷反应的DEG的上游和下游地区分别发现了14个和15个丰富的图案.
- *CNN在预测差异性基因表达方面取得了中等的准确性.
- *生物复杂性和庞大的监管代码限制了预测准确性.
结论:
- *Cis调节序列在环境反应中发挥作用,丰富的图案证明了这一点.
- * 机器学习,特别是CNN,可以根据DNA序列部分预测基因表达.
- *仅从DNA序列预测环境反应需要进一步细化和潜在的额外数据.
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