麻省理工学院BChristopher B. Burge小组近日取得一项新成果。经过不懈努力，他们发现了KATMAP从敲除数据推断剪接因子活性和调控靶点。相关论文于2025年11月4日发表于国际顶尖学术期刊《自然—生物技术》杂志上。

研究团队提出了一个可解释的回归模型KATMAP，该模型通过分析SF结合的变化和RNA加工的变化来模拟整个转录组的剪接变化。为了学习调控模型，KATMAP需要SF扰动RNA-seq数据和SF的结合基序作为输入，返回SF的位置特异性调控活性和预测靶标的描述。KATMAP软件包括对ENCODE SF击倒数据进行预训练的模型。学习后的KATMAP模型可以用于预测SF调控和单个外显子的顺式元件，这可以指导剪接开关反义寡核苷酸的设计。KATMAP还可以通过揭示导致转录组变化的因素、区分直接SF靶点和间接效应以及从临床RNA-seq数据推断相关SF来解释RNA-seq数据。

据介绍，典型的RNA测序（RNA-seq）实验揭示了数百种剪接变化，反映了剪接因子（SF）活性的潜在变化。了解SF活性如何影响转录组变异需要阐明每个SF如何影响剪接。

附：英文原文

Title: KATMAP infers splicing factor activity and regulatory targets from knockdown data

Author: McGurk, Michael P., McWatters, David C., Burge, Christopher B.

Issue&Volume: 2025-11-04

Abstract: Typical RNA sequencing (RNA-seq) experiments uncover hundreds of splicing changes, reflecting underlying changes in splicing factor (SF) activity. Understanding how SF activity influences transcriptomic variation requires elucidating how each SF impacts splicing. Here, we present an interpretable regression model, KATMAP, which models splicing changes throughout the transcriptome by analyzing changes in SF binding and the resulting alterations in RNA processing. To learn a regulatory model, KATMAP requires SF perturbation RNA-seq data and the SF’s binding motif as inputs, returning a description of the SF’s position-specific regulatory activity and predicted targets. The KATMAP software includes models pretrained on ENCODE SF knockdown data. Learned KATMAP models can be applied to predict SF regulation and cis-elements at individual exons, which can guide the design of splice-switching antisense oligonucleotides. KATMAP can also interpret RNA-seq data by uncovering the factors responsible for transcriptomic changes, distinguishing direct SF targets from indirect effects and inferring relevant SFs from clinical RNA-seq data.

DOI: 10.1038/s41587-025-02881-9

Source: https://www.nature.com/articles/s41587-025-02881-9