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SECIM and UF Research Computing Host Galaxy Training Sessions

Published: Jun 11th, 2015

SECIM, in conjunction with UF Research Computing, will be holding monthly training sessions on data interpretation in the Galaxy web interface. The first session for the 2017 Spring semester will be Thursday, February 9.

Training and Courses

Published: Jan 29th, 2015

Training Research Computing Training: For Research Computing training resources, including the training schedule, recordings of past sessions, presentation slides and worksheets, visit the UF Research Computing Wiki Training Page. SECIM […]

SECIM Seminar, Oct. 16: Dr. Timothy Ebbels, Imperial College London

Published: Oct 6th, 2014

SECIM welcomes research collaborator, Dr. Timothy Ebbels for his presentation, “Bayesian Deconvolution and quantitation of NMR Metabolic Profiles with BATMAN.”

Explore magazine: Trail blazers – Metabolites map biological processes in all living things

Explore magazine: Trail blazers – Metabolites map biological processes in all living things

Published: Aug 5th, 2014

UF’s Explore magazine features the UF CTSI’s Southeast Center for Integrated Metabolomics and its role in the National Institutes of Health Common Fund’s metabolomics consortium.

SECIM’s Lauren McIntyre, Ph.D., awarded UFRF Professorship

Published: Jun 4th, 2014

Dr. McIntyre joins thirty-two additional UF faculty members as recipients of the 2014 University of Florida Research Foundation Professorships.

UF receives grant to join national metabolomics consortium

Published: Sep 18th, 2013

The University of Florida today launched the Southeast Center for Integrated Metabolomics with a five-year, $9 million grant from the National Institutes of Health to help chart the course of biomedical discovery in the emerging field of metabolomics.

Core 4: Bioinformatics

Core 4: Bioinformatics

Published: Sep 13th, 2013

Core 4: Bioinformatics will provide the infrastructure and automation that enables SECIM to process thousands of samples from independent investigators, resulting in high quality, de-identified data sets and analyses.