Orosco, C. | Search bioRxiv

bioRxiv2026

Guide RNA binding induces reverse transcription bias during RT-qPCR analysis of RNA-targeting CRISPR/Cas systems

RNA-targeting CRISPR systems are commonly evaluated by RT-qPCR, but guide RNA binding can confound these measurements. We show that crRNA alone produces apparent knockdown without reducing target RNA abundance, whereas RNA sequencing remains unbiased and reveals guide-associated transcriptomic perturbations. A simple RNA denaturation step before reverse transcription restores accurate RT-qPCR quantification, providing practical guidance for RNA-targeting CRISPR analysis and guide design.

genomics↗

bioRxiv2026

TAMIPAMI: Software and methods for PAM/TAM identification for CRISPR and OMEGA gene editing systems

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. TAMIPAMI requires only a single control library and Cas or TnpB-treated library, simplifying experimental design, reducing cost, and providing greater accessibility for users. The platform interprets sequencing data with interactive visualizations 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.

bioinformatics↗

bioRxiv2026

Architecture of a DNA-guided Cas12a

CRISPR/Cas systems have largely been restricted to RNA-guided nucleases. Here, we present the cryo-EM structure of Acidaminococcus sp. Cas12a (AsCas12a) bound to a pseudo-DNA ({Psi}DNA) guide and RNA target, revealing how Cas12a accomplishes DNA-guided RNA recognition. The {Psi}DNA hairpin bridges the recognition and nuclease lobes, mimicking a PAM-proximal duplex and positioning the spacer to allow formation of a canonical RNA-DNA heteroduplex along the REC lobe. This provides a structural framework for its activity and provides a blueprint for future engineering.

biochemistry↗

bioRxiv2021

SCOUR: A stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

BackgroundThe topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms - two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: Stepwise Classification Of Unknown Regulation, or SCOUR. ResultsWe evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32-88% for noiseless data, 9.2-49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6-27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. ConclusionsSCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

bioinformatics↗