A Unified and Interpretable Framework for Evaluating Fluorescence Trace Quality in Transcription Kinetics

Quantifying transcriptional dynamics from fluorescence traces is a powerful approach to understanding gene regulation, but such analysis critically depends on the quality of the fluorescence signal. Experimental researchers often lack an objective and computationally simple way to assess trace quality before kinetic modeling. In this study, we fill in this gap via systematically investigating two key factors (i.e., signal-to-noise ratio (SNR) and trace length) using synthetic data generated from a composite-state Hidden Markov Model (cpHMM) simulator. By analyzing thousands of simulated traces, we identified quantitative thresholds (SNR ≥ 30 dB and length ≥ 360) beyond which transcriptional dynamics can be reliably captured for kinetic inference. Building on these findings, we further discovered a unified and easily computable quality indicator based on the difference between the first two autocorrelation lags. A threshold value of approximately 0.07 effectively separates reliable from low quality traces, providing a simple yet robust criterion for data selection. Together, these results establish a practical framework for assessing fluorescence trace reliability, offering experimental researchers an interpretable and computationally efficient tool to ensure data quality prior to transcription kinetics modeling.