
RNA SequencingRevolutionary RNA Sequencing Technology
Converting biological complexity into actionable digital insights
Significant cost reduction compared to traditional methods
Plugins Integrated
Advanced Biology with AI
Transforming raw data into disease predictions and treatment optimization
Plugins Integrated
Why is Biostate AI different?Not Just Another Bio-AI Company
While many companies apply AI to medical images or electronic health records, we focus on analyzing the complete transcriptome—the dynamic expression of all genes—which provides a much more comprehensive view of a patient’s biological state than static DNA analysis or limited protein panels.
Our N of 1 approach prioritizes individual patient outcomes while minimizing toxicity and side effects before focusing on developing entirely new treatments for broader populations. By integrating our proprietary low-cost RNA sequencing technology with advanced AI models, we’ve created a vertically integrated system that generates data and insights at unprecedented scale. This allows us to build more robust models with less bias than companies relying solely on public datasets or expensive third-party sequencing services.
BIRT (Barcode-Integrated Reverse Transcription) is our proprietary technology that integrates sample barcoding directly during the reverse transcription step of RNA sequencing. This allows us to pool up to 96 samples before expensive library preparation steps, dramatically reducing costs.
PERD (Probes for Excess RNA Depletion) is our enzyme-free method for removing ribosomal RNA, which typically makes up around 90% of cellular RNA but provides little useful information. Traditional methods use expensive enzymes like RNase H, but our approach uses specially designed probes that selectively deplete rRNA without additional enzymatic steps, further reducing costs while maintaining high data quality.
Together, these technologies reduce per-sample costs significantly compared to traditional methods, making large-scale RNA sequencing economically feasible.
Our cost advantages come from multiple innovations. Sample pooling with BIRT allows us to process up to 96 samples simultaneously through expensive library preparation steps. Our enzyme-free rRNA depletion with PERD eliminates costly enzymes while maintaining effective rRNA removal. We’ve also developed optimized reagent formulations that reduce waste and increase efficiency, implemented automation to minimize labor costs, and vertically integrated our process to eliminate markup costs from multiple vendors.
Biostate’s unique BIRT technology uses random hexamers to prime the reverse transcription of RNA into complementary cDNA, allowing it to handle challenging samples effectively. This approach works with partially degraded samples, including FFPE (formalin-fixed paraffin-embedded) tissues with RNA integrity numbers (RIN) as low as 2.2-2.3.
Our technology captures all RNA types, including messenger RNA, non-coding RNAs, long non-coding RNAs, and microRNAs. The way we integrate barcoding with random hexamer priming is a unique capability developed from our leadership’s decades of experience with novel DNA nanotechnology, making our approach particularly effective for archival and degraded samples that other methods struggle with.
We implement multiple quality control checkpoints throughout our workflow. RNA integrity scores and quantity measurements are taken for each sample before sequencing. We perform library QC with size distribution and concentration checks to ensure optimal sequencing performance. During sequencing, coverage depth metrics and quality scores are monitored in real-time, while our bioinformatic QC uses automated pipelines to detect batch effects, sample swaps, or other anomalies. We also regularly process known control samples to ensure consistency.
Our standardized protocols and centralized processing minimize batch effects—a major problem in multi-site studies. By processing samples uniformly in our facilities, we eliminate the technical variation that often confounds biological signals in traditional approaches.
We’ve taken a foundation model approach similar to what revolutionized NLP with GPT models, rather than building narrow, single-purpose AI models. We pre-train on massive unlabeled RNA datasets (hundreds of thousands to millions of samples) so our models learn the “grammar” of biology—patterns of the reality. We then fine-tune these pre-trained models for specific applications using much smaller labeled datasets.
This approach dramatically reduces the amount of labeled data needed for specific applications while also enabling generalization of the models and other unique emergent properties. In internal studies, our pre-trained models achieved 89% accuracy after fine-tuning on as little as 60 samples, compared to a baseline of 65%, in certain applications.
Our foundation models learn biological relationships in several key ways. They develop gene pathway awareness by pre-training on diverse biological contexts, learning which genes function together in pathways and networks. We incorporate structured knowledge from biological databases like KEGG and Gene Ontology for biological context encoding. Our models capture patterns at multiple biological scales—from individual cellular systems to whole organisms.
This comprehensive approach allows our models to capture meaningful biological relationships rather than spurious correlations, making them more robust for real-world clinical applications. Taking a foundational model approach ensures our capabilities are more generalizable than simple statistical correlations.
Traditional biomarker approaches focus on a small number of pre-selected genes or proteins (typically 1-50), rely on linear statistical relationships between markers and outcomes, are limited to known biology and existing hypotheses, often have poor generalization to diverse patient populations, and typically require large validation cohorts.
Our approach examines the entire transcriptome (all ~30,000 human genes), captures complex non-linear relationships between genes and outcomes, discovers novel patterns beyond current biological understanding, adapts to different patient populations through transfer learning, and requires smaller validation cohorts due to pre-training advantages.
For example, while traditional approaches might measure levels of a handful of known cancer biomarkers, our models can identify subtle patterns across thousands of genes that more accurately predict treatment response or disease progression. This comprehensive approach is particularly powerful for complex diseases where traditional biomarkers have shown limited success.
Similar to how large language models like ChatGPT learn patterns in text, our foundation models learn patterns in RNA expression data. Both approaches use massive datasets for pre-training followed by fine-tuning for specific applications. However, while language models work with text tokens, we’ve developed specialized methods to tokenize biological data, accounting for the unique characteristics of RNA expression.
A key difference is that our models are specifically designed to understand biology and predict health outcomes. We incorporate structured biological knowledge and focus on causal relationships rather than just correlations. Additionally, we’ve built custom architectures optimized for processing RNA data and capturing complex gene interactions, unlike general-purpose language models.
While AlphaFold focuses on predicting protein structures from amino acid sequences, our technology addresses a fundamentally different challenge: understanding and predicting how the expression of thousands of genes changes in response to disease or treatment.
Structural prediction models like AlphaFold provide valuable insights about what proteins can do based on their shape, but our approach reveals what genes and proteins are actually doing in a living system under specific conditions. This dynamic, system-level understanding is essential for predicting disease progression and treatment responses. Additionally, our models incorporate temporal data to predict future biological states, going beyond the static structural predictions of AlphaFold.
We use standard GPU-accelerated high-performance computing clusters with distributed training architectures that allow our models to scale effectively across different collaborators without requiring them to share all their data into a single system. This federated learning system enables model training across geographically distributed data while maintaining compliance with regional data regulations.
Our infrastructure is built on OmicsWeb, our data lake and storage solution, which orchestrates the entire process and is available as a standalone service for interested parties. This integrated approach optimizes both computation and data management for complex biological modeling.
We implement a multi-layered approach to data privacy. Our federated learning architecture allows models to learn from data without raw data ever leaving secure environments. We implement differential privacy with mathematical guarantees that limit how much information about any individual can be extracted from models.
Our regional data processing centers ensure compliance with local regulations (HIPAA, GDPR, India PDP), while rigorous de-identification protocols remove personal identifiers while preserving scientific value. We maintain comprehensive tracking of patient consent across all uses and undergo regular independent audits to verify privacy safeguards. This approach allows us to build powerful models while maintaining the highest standards of patient privacy.
The traditional approach requires multiple vendors and tools: in-house sample extraction with variable protocols, RNA sequencing contracted to service providers, data processing by specialized bioinformaticians, analysis performed with R or Python, visualization created with additional tools, manual literature review for interpretation, and manual report writing.
Our integrated stack eliminates these fragmented workflows by providing standardized extraction and sequencing with our BIRT/PERD technology, secure data storage and organization in OmicsWeb, conversational analysis without specialized programming knowledge via Copilot, automated draft generation for papers and reports through QuantaQuill, and contextual interpretation leveraging our pre-trained foundation models.
This integration eliminates time-consuming handoffs between steps, reduces cost markups from multiple vendors, and creates a seamless experience from sample to insight that accelerates research by months or even years.
OmicsWeb Copilot is our AI research assistant that allows scientists to analyze complex RNA data through natural language conversations rather than code. Unlike general-purpose LLMs that might fabricate analysis code, Copilot is built on trusted bioinformatics libraries (DESeq2, limma, edgeR, etc.). It interprets your question about the data, determines the appropriate statistical methods needed, creates statistically sound analysis code using validated libraries, runs the analysis on your data in our secure cloud environment, explains the results in plain language with appropriate visualizations, and allows follow-up questions to refine or expand the analysis.
This approach democratizes sophisticated bioinformatics, allowing researchers without computational expertise to perform complex analyses immediately rather than waiting weeks for specialist availability, while ensuring methodological rigor by using established statistical approaches.
QuantaQuill is our AI-powered scientific writing assistant that transforms RNA analysis results into publication-ready manuscripts. It analyzes statistical outputs and data visualizations, incorporates relevant published research automatically, generates detailed technical methods sections, produces publication-quality figures with appropriate statistical notation, suggests implications and limitations based on results, and formats citations according to journal requirements.
While traditional manuscript preparation might take weeks or months, QuantaQuill produces high-quality first drafts in hours. Scientists maintain full control over the final content while benefiting from automated formatting and integration of technical details, dramatically accelerating the translation of findings into shareable knowledge.
This tool is applicable for any general scientific topic, but when coupled with Copilot, it allows you to rapidly write papers dealing with omics data.