Work

Protocol to predict gene expression from transcriptomic data using PREDICT

Year: 2026

Type: article

Abstract: Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis -regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k -mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k -mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al. 1 , 2

Source: STAR Protocols

DOI: 10.1016/j.xpro.2026.104482

Authors: Ming-Ren Yen, Kai-Cheng Pan, Ming-Jung Liu, Chia-Yi Cheng, Ting-Ying Wu

Institutions: Institute of Plant and Microbial Biology, Academia Sinica, Academia Sinica, National Taiwan University

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $1,550

Funders: National Science and Technology Council, Trường Đại học Nha Trang, Academia Sinica, National Science and Technology Council