OmicsResonance: An LLM-assisted cloud ecosystem for interactive and reproducible single-cell transcriptomic analysis
Year: 2026
Type: preprint
Abstract: While single-cell RNA sequencing (scRNA-seq) is indispensable, existing analytical tools impose high computational barriers, requiring complex environment configurations and programming proficiency. To democratize single-cell analysis, we developed OmicsResonance, a code-free, web-based platform that eliminates local installations and empowers researchers to execute end-to-end analyses directly within a browser. The platform integrates standard processing pipelines and Large Language Model (LLM)-assisted cell annotation with a suite of advanced modules, including pseudo-time inference, CNV profiling, and virtual knockout simulations. Built on a scalable architecture, OmicsResonance champions “justified analysis” by avoiding black-box defaults. Its interactive interface allows users to dynamically adjust parameters and instantly visualize biological impacts, ensuring transparent and biologically sound analytical decisions. Validation across three public datasets successfully reproduced established findings and facilitated further interpretation of the data. Ultimately, OmicsResonance bridges the gap between complex computational pipelines and bench research, providing an accessible yet powerful environment for scRNA-seq data exploration. The platform is available at https://cloud.rnastar.com/ . Abstract Graph
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214347367