Jonathon O'Brien
CEO
Golgi
Golgi provides a computational platform designed to enhance the reliability and throughput of mass spectrometry proteomics analysis. Their algorithms improve data quality in high-throughput drug screening by substantially increasing true positives and reducing false positives in isobaric and label-free experiments. This framework enables researchers to achieve higher statistical power, even with smaller sample sizes, in complex proteomics studies.
Updated 7 months ago
Funding
Funding rounds are not available yet.
Founders
Product
Problem
High-throughput drug screening using mass spectrometry proteomics often struggles with insufficient sensitivity and specificity, leading to an increased rate of false positives and a reduction in reliable true positives. This impacts the efficiency and accuracy of identifying promising drug candidates.
Solution
Golgi offers a computational platform designed to enhance mass spectrometry proteomics workflows, particularly for high-throughput drug screening. The platform leverages advanced algorithms to simultaneously improve the sensitivity and specificity of isobaric proteomics experiments. This results in a substantial increase in true positive identifications while significantly reducing false positives, providing more robust and actionable data for drug discovery. The system's analytical framework is capable of achieving superior results, even with single-replicate (N=1) experimental designs, compared to traditional multi-replicate (N=3) approaches.
Target Audience
The primary users are researchers and scientists involved in high-throughput drug screening and proteomics analysis within pharmaceutical and biotechnology companies.
Features
- Computational platform for mass spectrometry proteomics data analysis
- Algorithms designed to increase true positive identification rates by up to 149%
- Features that reduce false positive rates by up to 75% in label-free experiments
- Data analysis framework for combining multiple batches to increase experimental power
- Optimized for isobaric proteomics experiments
- Enables higher reliability in high-throughput drug screening
- Capable of achieving superior results with N=1 designs compared to standard N=3 approaches