種を超えた遺伝子調節進化を研究するための機械学習と機能ゲノミクスの統合
Erin N Gilbertson1, Steven K Reilly2
1Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
Current opinion in genetics & development
|December 20, 2025
まとめ
進化生物学は、種の差異の遺伝的根拠を求めている。新しい方法は、比較ゲノミクスと機械学習を組み合わせて、シス調節因子の変化が遺伝子発現の進化をどのように引き起こすかを解読している。
科学分野:
- 進化生物学
- ゲノミクス
- 遺伝子調節
背景:
- 種の表現型の違いの理解は、遺伝的基盤の特定を必要とする。
- 哺乳類のゲノムは、主に非コード領域に多くの配列の違いを明らかにする。
- シス調節因子(CRE)は遺伝子発現を制御するが、複雑で研究が難しい。
研究 の 目的:
- 遺伝子調節進化を研究する上での最近の進歩をレビューすること。
- 種を超えたCRE機能の理解のための実験的および計算的戦略を強調すること。
主な方法:
- 配列の違いを特定するための比較ゲノミクス。
- 遺伝子発現とCRE活性を評価するための種を超えた機能プロファイリング。
- CRE機能をテストするためのハイスループット摂動アッセイ。
- DNA配列からCRE活性を予測するための機械学習。
主要な成果:
- 遺伝子発現とCRE機能における種を超えた違いのカタログ化を可能にする進歩。
- 配列データのみからCRE活性を予測するための機械学習モデルが登場している。
結論:
- 実験的および計算的アプローチの統合は、遺伝子調節進化を解読するための鍵である。
- 新しい戦略は、CREにおける遺伝的変化を種を超えた機能的結果に結びつける能力を向上させている。
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