使用可解释机器学习模型对牛,猪和绵羊肌肉进行比较转录组分析
Yaqiang Guo1,2, Shuai Li1, Rigela Na1
1College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010010, China.
Animals : an open access journal from MDPI
|October 26, 2024
概括
这项研究使用机器学习确定了影响牛,猪和羊肌肉生长的关键基因. 它突出了同源基因和特定物种的候选基因,揭示了脂质代谢和信号传递等关键肌肉发育的途径.
科学领域:
- 基因组学和生物信息学
- 动物科学动物科学
- 分子生物学分子生物学
背景情况:
- 肌肉生长对于肉质和牲畜的经济价值至关重要.
- 了解肌肉发育基因对于改善畜牧养殖和肉类生产至关重要.
- 关于跨物种肌肉发育基因的现有研究需要进一步的系统性调查.
研究的目的:
- 确定调节不同牲畜物种肌肉生长和发育的关键基因.
- 使用机器学习进行跨物种转录组分析,用于基因发现.
- 发现影响牛,猪和绵羊肌肉发育的同源性和特定物种基因.
主要方法:
- 利用来自猪,牛和绵羊肌肉组织的275个转录组的数据集.
- 采用了九个机器学习模型,包括支持矢量分类器 (SVC) 和支持矢量机器 (SVM).
- 应用了夏普利添加剂扩展 (SHAP) 来分析转录组数据并识别显著基因 (SHAP基因).
主要成果:
- 确定了41个同源基因 (例如,NANOG,TLR9) 在牛,猪和绵羊中一致表达.
- 发现了牛 (例如SLC47A1),猪 (例如DRP2) 和绵羊 (例如COL10A1) 的特定物种候选基因.
- 凯格路径分析揭示了以太脂代谢,皮质醇合成和信号在肌肉发育中的参与.
结论:
- 该研究成功地确定了主要牲畜物种肌肉生长的关键遗传调节者.
- 同源和特定物种的基因为改善肌肉发育和肉质提供了目标.
- 已识别的途径提供了对农业动物肌肉生长和发育背后的分子机制的见解.
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