TOOLS

This advanced tool analyzes two distinct DNA sequences simultaneously to discover optimal dual-function RNA candidates capable of targeting both genes (e.g., acting as saRNA for activation and siRNA for silencing). It utilizes Nearest-Neighbor thermodynamic calculations (Δ G) and Rational Design rules (Reynolds/Ui-Tei) to rank candidates based on stability and efficacy. Additionally, it applies strict homopolymer filtering (e.g., GGGG exclusion) and provides detailed duplex visualizations (Sense/Antisense) with comprehensive export options (PDF, FASTA, Excel) for synthesis. 

This tool scans the provided DNA promoter sequence to design optimal saRNA (small activating RNA) candidates for gene activation. It uses thermodynamic calculations (Δ G ) and Reynolds rules to rank candidates based on their stability and asymmetry using a scientific scoring system. Additionally, it visualizes the duplex structure formed by the Sense (passenger) and Antisense (guide) strands, allowing you to copy them for synthesis. 

This tool generates saRNA candidates in bulk from your DNA sequence and provides an advanced selection and comparison system to manage the best candidates. You can select specific candidates to add to the bottom "Dock" panel, then view and copy them as a consolidated table for export. Additionally, each result card features a duplex visualization showing the hybrid structure of the Sense and Guide strands. 

This tool is a practical converter that instantly transforms a single input DNA promoter sequence into Sense (Passenger) and Antisense (Guide) RNA strands. It visualizes the duplex structure showing how the strands align and automatically reverses the Guide strand into the correct 5' → 3' orientation required for synthesis ordering. It allows you to copy all generated data in a formatted manner with a single click. 

This web‑based tool is designed to examine—at high resolution—the 3′‑UTR region of the TP53 gene, which plays a critical role in aggressive tumors such as anaplastic thyroid cancer (ATC). TP53 serves as the “central command” for cellular stress responses and tumor‑suppression mechanisms; therefore, even small alterations within its 3′‑UTR can dramatically affect TP53 protein levels.

This tool is an advanced artificial intelligence system designed to predict the on-target cleavage efficiency of single guide RNA (sgRNA) sequences for CRISPR-Cas9 gene editing experiments..

Malaria is a life-threatening infectious disease caused by Plasmodium parasites. The traditional diagnostic method, thin blood smear microscopy, relies on manual examination by expert pathologists, a process that is both time-consuming and prone to human error. This project has developed an automated diagnostic tool using Convolutional Neural Networks (CNN) to detect malaria parasites from microscopic images. Beyond simple classification, the model employs Grad-CAM (Gradient-weighted Class Activation Mapping) technology to visualize the cellular regions the AI focuses on, thereby meeting "Explainable AI" (XAI) standards.