Bioinformatics teams are asked to do everything.
Ingest messy, multimodal datasets. Build pipelines across imaging, genomics, phenotypic, and clinical variables. Align with collaborators and regulators. Make it reproducible. Make it fast.
And too often, they’re doing it all on brittle infrastructure held together with one-off scripts, spreadsheets, and goodwill.
“In the same lab, you’ll have a researcher cutting genes with CRISPR, and then turn around and use spreadsheets in Excel to manipulate the data,” says Manifold Co-Founder Vivek Mohta. That disconnect between high-skill science and low-fidelity infrastructure doesn’t just slow teams down—it risks the quality and integrity of the science itself.
The reality is: most platforms weren’t built for bioinformatics. They were built for administration, or one-off analysis, or IT compliance. What bioinformatics teams actually need is different. It’s scientific infrastructure that supports depth, diversity, and change.
That means:
At Manifold, this is the work. Not retrofitting generic software to science, but building the infrastructure research actually needs.
And this vision is already in motion. With the Broad Institute, we’re helping transform Terra into an AI-native research platform—one that supports semantic search, governed collaboration, and real-time analytics across clinical, genomic, and consent data. It’s built for how modern teams actually work, not how systems expect them to.
The same principles power our partnerships with the American Cancer Society, the Simon Cancer Center, and Morehouse School of Medicine—where Manifold supports secure multimodal ingestion, audit-ready workflows, and analytic sharing across complex research ecosystems.
This isn’t just about pipelines. It’s about scaling "scientific capability."
As Vivek says:
We’re not just trying to reduce the cost of working with data—we’re trying to unlock its full value by making it usable by many more people. When data is accessible, trustworthy, and well-structured, it stops being a bottleneck and starts becoming a force multiplier. That’s the shift we’re building for.
Because at the pace of modern science, infrastructure isn’t just technical. It’s strategic.
And bioinformatics teams deserve better.
.png)
As the research community continues to rely on data shared by the National Institutes of Health (NIH), ensuring the security of that data continues to be an important requirement. Last year, the NIH issued an implementation update to the NIH Genomic Data Sharing Policy (GDS Policy), which outlines requirements for researchers accessing, storing, or processing large-scale human and non-human genomic data generated from NIH-funded research. The update was designed to continue to promote responsible data management and access under the GDS Policy and ensure the broad and responsible sharing of NIH genomic data for research that advances human health.
As the research community continues to rely on data shared by the National Institutes of Health (NIH), ensuring the security of that data continues to be an important requirement. Last year, the NIH issued an implementation update to the NIH Genomic Data Sharing Policy (GDS Policy), which outlines requirements for researchers accessing, storing, or processing large-scale human and non-human genomic data generated from NIH-funded research. The update was designed to continue to promote responsible data management and access under the GDS Policy and ensure the broad and responsible sharing of NIH genomic data for research that advances human health.
%20(1540%20x%20513%20px).png)
The landscape of cancer research is rapidly evolving with the integration of multimodal data, including tissue analysis, clinical records, genomic sequencing, and imaging. By harnessing this rich array of cancer-specific data, researchers gain a comprehensive understanding of disease, leading to more accurate diagnoses and personalized treatments.
The landscape of cancer research is rapidly evolving with the integration of multimodal data, including tissue analysis, clinical records, genomic sequencing, and imaging. By harnessing this rich array of cancer-specific data, researchers gain a comprehensive understanding of disease, leading to more accurate diagnoses and personalized treatments.
Observational studies are pivotal in driving cancer breakthroughs, providing insights into the environmental, lifestyle, and genetic factors that influence cancer development and prevention. This real-world data is the cornerstone of targeted prevention strategies, leading to life-saving discoveries.
Observational studies are pivotal in driving cancer breakthroughs, providing insights into the environmental, lifestyle, and genetic factors that influence cancer development and prevention. This real-world data is the cornerstone of targeted prevention strategies, leading to life-saving discoveries.
.svg)
Platform walkthrough tailored to your data environment
Current workflow review and fit assessment
Implementation timeline and resource requirements
