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PacBio, Mt. Sinai Partner on Clinical Research

By a GenomeWeb staff reporter

NEW YORK (GenomeWeb News) – Pacific Biosciences and the Mount Sinai School of Medicine today announced a collaboration "to advance research" through the Mount Sinai Institute of Genomics and Multiscale Biology.

The institute will be led by Eric Schadt, who will also continue as chief scientific officer at PacBio.

As part of the collaboration, a Single Molecule Real Time Biology User Facility will be established within the institute, which is the hub of genomics research at Mount Sinai and collaborates with 13 other disease-oriented and core technology-based institutes at Mount Sinai.

PacBio developed the SMRT technology for the real-time analysis of biomolecules with single-molecule resolution. The SMRT Biology User Facility will be outfitted with R&D versions of the SMRT platform called Astros.

The Astros systems were developed for research into biological processes, including DNA sequencing, direct RNA sequencing, protein translation, and ligand binding. The platforms will be available for use by the institute and other collaborators in the eastern half of the US, the partners said. PacBio also will continue to perform collaborations from its headquarters in Palo Alto, Calif.

Financial and other terms of the deal were not disclosed.

"The large-scale generation and integration of multiple sources of biological data combined with clinical information will expand our ability to characterize disease, and ultimately help develop and improve the diagnosis and treatment of patients," Dennis Charney, the Dean of Mount Sinai School of Medicine, said in a statement.

"Multiscale data integration, including genomic, expression, metabolite, protein, and clinical information will ultimately define the future of patient care," said Schadt. "With our intent to collaborate in areas, such as newborn screening for rare genetic disorders, infectious diseases, and cancer we hope to accelerate this revolution, starting by integrating clinical data with previously untapped biological information to build new computational models for predicting human disease."

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