Overview
BioTeam provided embedded scientific and technical support to researchers at a leading NIH institution, bridging the gap between complex biological data and actionable scientific insight. Over the course of a multi-year engagement, BioTeam delivered ad hoc troubleshooting and sustained project collaborations, enabling researchers to successfully complete more than 20 high-impact research projects.
Our support ranged from rapid troubleshooting engagements lasting hours or days to longer project collaborations spanning weeks or months. These efforts helped researchers overcome technical bottlenecks, enhance analysis pipelines, and accelerate scientific discovery across a diverse range of scientific focus areas, including image segmentation using machine learning, flow cytometry analysis, and RNA-Seq analysis.
Impact Summary
- Supported 20+ research projects, accelerating scientific discovery across multiple laboratories
- Enabled researchers to analyze complex imaging and sequencing datasets
- Modernized pipelines to ensure reproducibility, portability, and scalability
- Eliminated critical bioinformatics bottlenecks that were slowing research progress
Services Provided
Ad hoc and project-based scientific support
BioTeam provided flexible, on-demand support tailored to each lab’s needs. Ad hoc engagements addressed immediate technical challenges and were often resolved within hours or days, while larger projects involved multi-week or multi-month collaborations.
Project support included:
- OCT image segmentation using machine learning
- Pipeline modernization and workflow optimization
- Flow cytometry analysis
- Variant call detection
- Spatial transcriptomics analysis
- Epitope mapping
- H&E image analysis
- Nanopore sequencing workflow development
- De-identification of imaging data
- RNA-Seq analysis
- 3D image registration enhancements
- Hi-C data analysis
Workflow modernization and reproducibility
BioTeam ensured long-term sustainability by implementing enhanced software engineering practices, including:
- Version-controlled code repositories using GitHub
- Containerized workflows for reproducibility and portability
- Standardized pipeline development and documentation
- Improved data management and sharing practices
Challenge
Researchers at this NIH institution were generating increasingly complex datasets, including advanced imaging, spatial transcriptomics, and long-read sequencing. While these researchers were leaders in their biological domains, many labs lacked in-house expertise in bioinformatics, high-performance computing, and modern analysis workflows.
As a result, researchers were unable to fully analyze their own datasets and had no dedicated computational support within their labs. This created significant bottlenecks between data generation and biological insight, slowing research progress and limiting the ability to extract meaningful results.
Approach
BioTeam implemented an integrated scientific support model that integrated directly with researchers and provided both immediate and sustained technical expertise.
Direct scientific and technical execution
BioTeam worked alongside researchers to perform complex analyses and modernize workflows using advanced computational methods. This included machine learning-based image segmentation, RNA-Seq analysis, pipeline enhancements, and workflow optimization.
By combining deep scientific understanding with technical expertise, BioTeam enabled researchers to analyze datasets that were previously inaccessible due to technical limitations.
Reproducible and scalable workflow development
BioTeam implemented standardized, version-controlled workflows using GitHub and containerization, enabling analyses to be reproduced, shared, and extended by researchers and collaborators.
Outcomes
The engagement resulted in a measurable increase in research velocity and technical capability across the institution.
BioTeam successfully supported over 20 research projects, enabling researchers to overcome computational barriers and complete analyses that were previously out of their reach. By enhancing pipelines, improving workflows, and providing integrated scientific expertise, BioTeam helped transform the institution’s ability to analyze and operationalize complex biological data.
These efforts established a sustainable framework for reproducible scientific computing and positioned the institution to scale its research capabilities in a data-intensive environment.
