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解读基因组学大规模深度神经网络的Cis-调控相互作用

Shushan Toneyan¹, Peter K Koo¹

¹Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA.

bioRxiv : the preprint server for biology

|July 18, 2023

概括

CREME是一种用于基因表达预测的深度神经网络 (DNN) 解释的新工具包. 它揭示了这些模型如何识别cis-regulatory元素及其复杂的相互作用,推进基因调节研究.

科学领域:

基因组学和生物信息学
计算生物学计算生物学
分子生物学分子生物学

背景情况:

大规模的基于序列的深度神经网络 (DNN) 越来越多地用于基因表达预测.
评估和解释这些DNN带来了挑战,目前的方法对预测驱动器的洞察力有限.
现有的解释性工具,如动机分析,与更长的序列的复杂性作斗争.

研究的目的:

介绍CREME,一个设计用于对大规模DNN进行基因调节规则的询问的in silico扰动工具包.
为了研究突出的基因表达预测DNN,Enformer,使用CREME.
为了发现更高层次的 cis-regulatory element (CRE) 相互作用以及增强剂和减噪剂的学习生物化学特征.

主要方法:

开发CREME,这是一个用于查询DNN的in silico扰动工具包.
将CREME应用于Enformer,这是一个用于基因表达预测的大规模DNN.
对CREME输出进行分析,以确定CREs,高阶相互作用和序列距离关系.

主要成果:

在分析Enformer时,CREME成功地确定了增强或沉默目标基因的cis-regulatory元素 (CREs).
这项研究探讨了复杂的高级CRE相互作用以及CRE距离转录开始地点的影响.
CREME揭示了增强剂和减噪剂的学习生物化学特征,并提供了功能序列元素的更高分辨率视图.

结论:

CREME提供了一个强大的工具包,用于发现大规模DNN学到的基因调节规则.
该工具包允许详细调查CREs,它们的相互作用和监管机制.
克雷姆 (CREME) 便于将DNN预测转化为洞察力,用于研究基因调节的基本问题.

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