In order to achieve success in metabolomics studies, the study design is of paramount importance. Parameters like sample size, randomization, and storage need to be taken into account so that reproducible results with minimized erroneous variability can be delivered.
Most metabolites are not only present in small quantities in biological fluids but are also fragile. Further, any one chromatography protocol cannot cover all metabolites. A well-designed experimental pipeline can yield significantly better results and a wider metabolome coverage.
The foundation of a good metabolomics study is the quality of the sample. This is ensured by developing a proper and time-tested protocol for sample collection, handling, and storage.
Our proprietary software performs all steps of data analysis, including pre-processing, peak picking, noise removal, identification and removal of degenerate features such as in-source fragments, adducts and isotopes, data cleaning/filtering, quantification of peak area, metabolite identification, univariate and multivariate statistical analysis, and interactive plotting. Our AI-powered software incorporates the latest algorithms and statistical tools for biomarker discovery.
Our team of data analysts and advanced software can save you months in data analysis time, resulting in faster publication or submission to regulatory agencies. We can also help you quantify 13C isotopic enrichment if your work involves 13C labeling studies.
Based on your needs and requirements, our team of analysts are trained to use popular open-source tools. Therefore, we can help you if you need to obtain results from validated and widely accepted open-source software.