Metabolon implemented solutions to overcome some of the limitations of MS-based metabolomics while maximizing longitudinal reproducibility. At Metabolon, we implemented solutions based on data normalization and quality control (QC) as critical data processing components to improve data quality and reduce the non-biological variation in the samples. These solutions allow us to overcome some of the limitations of MS-based metabolomics while maximizing longitudinal reproducibility.
Rare diseases represent a unique challenge in clinical medicine. These diseases signify that few people have these diseases and even fewer have a successful diagnosis. Rare diseases are classified in many categories, including metabolic diseases, rare cancers, autoimmune diseases, blood disorders, digestive diseases, endocrine system disorders, nervous system diseases, reproductive disorders, and musculoskeletal diseases.
Metabolon’s extensive work establishes criteria for evaluating metabolite stability in clinical metabolomic applications and provides guidelines for disqualifying unstable metabolites deriving from sample handling and processing in clinical metabolomic studies to ensure pre-analytical compliance.
Metabolomics and Shotgun Metagenomics Reveal Evidence That Glyphosate Exposure Affects the Gut Microbiome
A multi-omics approach provides valuable insights for investigating complex areas of science to enhance our understanding while Metabolon’s platform delivers interpretive depth not possible elsewhere. Additionally, the profiling of multiple matrices allowed for analysis of both microbiome derived compounds as well as how those changes may be reflected systemically, giving the work a much broader impact.
DBS is a valuable and convenient sampling option to drive metabolomics research forward. In this Q&A section, we asked Kelli Goodman, Staff Scientist at Metabolon, to address some of the common questions regarding the use of DBS samples for metabolomics analysis.
Metabolon’s newly updated Client Data Table format helps clients review, analyze and understand their project data while making it easier to import into statistical tools like R and Python.
When evaluating large quantities of information, it is paramount to make data quality a top priority. Metabolon’s Precision Metabolomics™ Data quality is extremely important to maintaining exceptionally high standards. From start to finish, Metabolon emphasizes quality control measures and checks and balances.
At Metabolon, we have spent the last 20 years building the best metabolomic competency in the world. Today, we enable, accelerate, and support drug development through biomarker discovery, understanding mechanism of action, patient stratification and more. One tool we use to power our work is machine learning, which automates routine tasks and teachable processes so our experts can focus their efforts on the challenges that most require their expertise.
Metabolomic data can play a pivotal role in understanding critical questions across the life sciences—the launch of DNA Genotek’s OMNImet.GUT validated by Metabolon dramatically expanded the accessibility of metabolomics for gut microbiome studies. This milestone shows how Metabolon can deliver innovative metabolomic insights through other collaborations.
A successful metabolomics study requires a comprehensive approach focused on quality and accuracy. That's why choosing the right partner can yield many more insights than trying to go it on your own, all while avoiding expensive mistakes. Consider these four risks that come with trying to do a metabolomics analysis without support.