Submitted to: Cell Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2026
Publication Date: 5/16/2026
Citation: Orosco, C., Jain, P., Rivers, A.R. 2026. TAMIPAMI: Software and methods for PAM/TAM identification for CRISPR and OMEGA gene editing systems. Cell Systems. https://doi.org/10.64898/2026.05.15.725432.
DOI: https://doi.org/10.64898/2026.05.15.725432

Interpretive Summary: This project develops TAMIPAMI, a simple and accessible method for identifying the short DNA sequences—called PAMs and TAMs—that gene-editing enzymes need in order to recognize and cut specific genetic targets. These sequences are essential for CRISPR-Cas and OMEGA/TnpB systems to function, but existing approaches to find them are often complicated, expensive, and slow. TAMIPAMI streamlines this process by requiring only one treated sample and one control, reducing cost and time while making advanced gene-editing characterization easier for scientists in agriculture and biotechnology. The software applies robust statistical analysis to identify which DNA motifs an enzyme targets and uses a novel algorithm to summarize these motifs into a clear, minimal set of patterns. TAMIPAMI successfully identified known motifs for multiple CRISPR enzymes as well as newer TnpB nucleases, demonstrating both accuracy and broad usefulness. By enabling faster development of gene-editing tools, this work supports efforts to protect U.S. agriculture from invasive pests and diseases by helping researchers design crops, livestock, and beneficial microbes with improved resistance and resilience. It also advances national goals to improve human health through better nutrition by providing tools that can be used to precisely modify and enhance the nutritional quality of food crops. Because TAMIPAMI is available through both a web interface and command-line software, it expands access to powerful biotechnology methods and accelerates innovation that benefits farmers, consumers, and the wider agricultural system.

Technical Abstract: Protospacer adjacent motifs (PAMs) and target-adjacent motifs (TAMs) are essential for target recognition by CRISPR–Cas and TnpB nucleases. Here we present TAMIPAMI, an efficient experimental and computational framework for rapid PAM/TAM identification from high-throughput sequencing data. TAMIPAMI requires only a single control and Cas or TnpB-treated library, simplifying experimental design, reducing cost, and providing greater accessibility for users. The platform interprets sequencing data analysis using centered log-ratio transformation to improve separation of cleaved and uncleaved motifs and introduces a novel algorithm that determines the minimal exact set of degenerate IUPAC sequences describing the observed PAM/TAM patterns. Using this approach, we accurately recovered canonical motifs for several nucleases, including SpCas9, LbCas12a, AsCas12a, BrCas12b, Cas12i1, and AmaTnpB. TAMIPAMI is available as both a web application and command-line tool, ultimately providing an accessible and efficient platform for PAM/TAM discovery and characterization across CRISPR and OMEGA systems.