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KATMAP infers splicing factor activity and regulatory targets from knockdown data.

Michael P McGurk1, David C McWatters2, Christopher B Burge3

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|November 4, 2025
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Summary
This summary is machine-generated.

KATMAP is a new regression model that explains how splicing factor activity alters RNA sequencing (RNA-seq) data. It identifies splicing factors and their direct targets, aiding in understanding transcriptomic changes.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • RNA sequencing (RNA-seq) reveals numerous splicing alterations linked to splicing factor (SF) activity.
  • Elucidating the precise impact of each SF on splicing is crucial for understanding transcriptomic variation.

Purpose of the Study:

  • To introduce KATMAP, an interpretable regression model for analyzing transcriptomic splicing changes.
  • To model splicing alterations by integrating SF binding data and RNA processing changes.

Main Methods:

  • KATMAP utilizes SF perturbation RNA-seq data and SF binding motifs as inputs.
  • It employs a regression approach to learn SF position-specific regulatory activity and predict target genes.
  • Pretrained models on ENCODE SF knockdown data are available within the KATMAP software.

Main Results:

  • KATMAP provides detailed descriptions of SF regulatory activity and identifies predicted SF targets.
  • The model can predict SF regulation and cis-elements at individual exons.
  • It effectively distinguishes direct SF targets from indirect effects in RNA-seq data.

Conclusions:

  • KATMAP offers a powerful tool for interpreting RNA-seq data and understanding SF roles in splicing.
  • The model can infer causative SFs from clinical RNA-seq data.
  • KATMAP findings can guide the design of splice-switching antisense oligonucleotides.