Biostate AI Launches Total RNA Sequencing & OmicsWeb Copilot for RNAseq Data Analysis

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Biostate AI, the scalable biodata foundry startup, emerged from stealth today with the launch of two service products: Total RNA Sequencing and OmicsWeb Copilot for RNAseq Data Analysis. Biostate AI aims to partner and collaborate with academic researchers, hospital biorepositories, and pharma/biotech companies, leveraging its new technologies for scalable multiomic data collection, scientific discovery, and AI training.

Total RNA Sequencing uses its patent-pending Barcode-Integrated Reverse Transcription technology to affordably, scalably, and comprehensively analyze all types of RNA. In contrast, typical gene expression profiling using RNA sequencing typically only captures information from the roughly 30,000 messenger RNA species, ignoring the 300,000 species of non-coding RNA that includes long non-coding RNAs , microRNAs (miRNAs), circular RNAs , and others. Biostate has filed 9 pending patents on its technologies and collaborates with a number of industry partners on technology development, including Twist Biosciences. Biostate AI also recently in-licensed further intellectual property (IP) from the California Institute of Technology to expand the range of biomolecules analyzed.

OmicsWeb Copilot helps biologists analyze and visualize data. OmicsWeb leverages state of the art large-language models (LLMs) to understand user requests and intent to build customized software and scripts for data analysis. In addition to analyzing the user’s own uploaded data, Copilot also allows the user to use and analyze over 1000 unique RNAseq datasets collected by the Biostate team. Copilot is being fine-tuned on 5000 proprietary RNAseq datasets, so that it can perform analyses and perform anomaly detection only possible after being trained on massive amounts of high-quality RNAseq data. The Copilot tool is being offered completely for free to academic and nonprofit users and researchers.

“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.” says David Zhang, Co-Founder & CEO of Biostate AI. 

“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.” said Fred Farina, Chief Innovation & Corporate Partnerships Officer at Caltech. 

“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, and potentially unlock a new trillion-dollar market.” said Haomiao Huang, general partner at Matter Venture Partners. 

“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.” said Ashwin Gopinath, Co-Founder & CTO of Biostate AI.

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

The ultimate goal of Biostate AI is to build AI that can predict human and animal health changes, including toxicity and efficacy responses to drugs.  The team has recently demonstrated RNA expression in blood taken from rats before drug dosing can predict survival with a Hazard Ratio of 8.  To scale this proof-of-concept demonstration to prediction of toxicity in humans for novel drugs, far more data must be collected, analyzed, and fed into AI models for training.  In this course of this data collection, petabytes of RNAseq and other omics data must be collected, interpreted, and tokenized.