Biostate AI unveils revolutionary RNA Sequencing and OmicsWeb Copilot

New Delhi: Biostate AI has officially emerged from stealth mode with the launch of two cutting-edge service products: Total RNA Sequencing and OmicsWeb Copilot for RNAseq Data Analysis.

The company aims to revolutionize RNA analysis and data interpretation by partnering with academic researchers, hospital biorepositories, and pharma/biotech companies, leveraging its proprietary technologies for scalable multiomic data collection, scientific discovery, and AI training.

Total RNA Sequencing utilizes Biostate AI’s patent-pending Barcode-Integrated Reverse Transcription (BIRT) technology to offer an affordable, scalable, and comprehensive analysis of all types of RNA. Unlike traditional RNA sequencing methods that focus only on the approximately 30,000 messenger RNA species, Biostate AI’s approach includes the analysis of over 300,000 species of non-coding RNA, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs). With nine pending patents on its technologies, Biostate AI collaborates with industry leaders like Twist Biosciences and has in-licensed additional intellectual property from the California Institute of Technology to expand its biomolecule analysis capabilities.

OmicsWeb Copilot is designed to help biologists analyze and visualize RNAseq data. Leveraging state-of-the-art large language models (LLMs), OmicsWeb understands user requests and intents, building customized software and scripts for data analysis. In addition to processing user-uploaded data, Copilot provides access to over 1,000 unique RNAseq datasets collected by Biostate AI. The tool is being fine-tuned on 5,000 proprietary RNAseq datasets to enhance its analysis capabilities and anomaly detection. Remarkably, OmicsWeb Copilot is offered for free to academic and nonprofit users.

“Training any AI well requires large quantities of relevant and high-quality data. Biostate AI has developed instrumental technologies to help collect more biological data at lower costs and is pleased to offer these capabilities to academic and industry partners and collaborators,” said David Zhang, Co-Founder & CEO of Biostate AI.

Fred Farina, Chief Innovation & Corporate Partnerships Officer at Caltech, emphasized, “Biostate AI’s approach will dramatically reduce the amount of animal testing performed by pharma and biotech companies in preclinical studies. We are proud to support Caltech alumni David and Ashwin in the form of both financial investment and intellectual property licensing.”

Haomiao Huang, General Partner at Matter Venture Partners, highlighted the broader implications, noting, “As a US company, Biostate’s affordable AI-embedded CRO services are much needed today as the supply of preclinical research services shrinks due to geopolitical tensions. Simultaneously, Biostate’s ultimate vision of individualized AI to predict drug effects would revolutionize medicine and health, potentially unlocking a new trillion-dollar market.”

Ashwin Gopinath, Co-Founder & CTO of Biostate AI, shared, “Bioinformatic analysis of RNAseq and other omics data is a highly complex, multi-step process that currently takes many hours of dedicated specialized programming. As we scaled up our RNAseq data collection in the past year, we started building OmicsWeb Copilot as an internal tool to help our scientists make sense of the data. And then we realized other people may also find this tool useful, so we’re opening it up to the general public for free.”

To date, Biostate AI has raised over $4 million in venture funding. The funding round was led by Matter Venture Partners, with participation from Vision Plus Capital, Catapult VC, the Caltech Fund, and individual investors, including Dario Amodei (CEO of Anthropic), Joris Poort (CEO of Rescale), Michael Schnall-Levin (CTO of 10X Genomics), and Emily Leproust (CEO of Twist Biosciences).

Biostate AI’s ultimate goal is to build AI that can predict human and animal health changes, including toxicity and efficacy responses to drugs. The team has recently demonstrated that RNA expression in blood taken from rats before drug dosing can predict survival with a Hazard Ratio of 8. Scaling this proof-of-concept to predict toxicity in humans for novel drugs will require extensive data collection, analysis, and AI model training. This endeavor will involve collecting, interpreting, and tokenizing petabytes of RNAseq and other omics data.