The leading Protein Sequencing Company™, Quantum-Si, recently announced that it signed a partnership and license agreement with Biovista to offer customers more significant proteomic insights beyond their protein sequence output.
Biovista utilizes artificial intelligence in multiple formats to analyze enormous data repositories and visualize non-obvious networks and connections between proteins, diseases, and drug mechanisms of action. The partnership enhances Quantium-Si's services significantly by making proteomic-to-drug workflows more efficient for researchers.
Biovista's VIZIT™ exploration tool will be incorporated into Quantum-Si's cloud-based suite of analytic tools, allowing researchers to visualize the connections between the protein sequences identified during their experiment to diseases, other proteins, and post-translational modifications. Thus, researchers can identify potential disease-specific mechanisms and biomarkers more efficiently and effectively.
"Quantum-Si's protein sequencing technology in concert with Biovista's database and visualization technology can aid in the discovery of new proteins for future therapeutic targets associated with disease," said Jeff Hawkins, CEO of Quantum-Si. He further stated, "The potential can also extend to discovering new biomarkers for clinical research and diagnostics."
(Source- Business Wire)
"Quantum-Si is leading the next generation of real-world meaningful sequencing," said Dr. Aris Persidis, Biovista's Co-Founder and President. "We are excited to see deep sequencing and insight generation now available in one integrated platform," he concluded.
(Source- Business Wire)
About
Quantum-Si
Quantum-Si, The Protein Sequencing CompanyTM, is dedicated to revolutionizing the rapidly expanding field of proteomics. It provides the first-of-its-kind, end-to-end, universal single-molecule detection solutions, allowing to build, experiment, and discover on its platform regardless of their existing products. In addition, its proprietary Time Domain Sequencing technology eliminates the reliance on color as an identification method, a barrier for any application outside of genomics, where only four colors are required.