Metabolomics Bioinformatics Analysis

gutfloraomics

Metabolomics Bioinformatics Analysis

Metabolomics studies have been increasingly used in gut microbiota to perform the large-scale metabolic profiling and characterize small molecules produced by gut microbes, which is often considered the most direct angle to study the relationship between gut microbiota and the physiological state, fundamental understanding of diseases, and the search for potential biomarkers. The advances of technologies such as nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and Fourier transform infrared (FTIR) spectroscopy applied in gut metabolomics provide researchers with a large amount of spectral data. Bioinformatics and statistical methods are thus introduced to interpret the data and to excavate deeper and more comprehensive information to gain more understanding of gut microbial activities, to discover metabolites to suggest a biological pathway or disease condition, and hopefully, to find new applications in clinical use and routine healthcare.

Creative Proteomics, as a preeminent corporation that specializes in multi-omics and equipped with state-of-the-art techniques and bioinformatics platforms, as well as well-experienced scientists, provides comprehensive and delicate bioinformatics services for your gut microbiota research.

Chosen in accordance with chemical and physical properties of metabolites, separation techniques are coupled with detection techniques to address different analytes, each technique with different resolution, sensitivity and technological limitations in identifying metabolites. Once acquired by the above techniques, raw data are going through preprocessing involves noise reduction, retention time correction, peak detection and integration and chromatogram alignment, and for untargeted metabolomic studies, different databases are used to identify the metabolites from spectra. In the last data interpretation and integration step, the selected metabolites are linked to the biological context under study, through enrichment and pathway analyses, and provide an even broader view coupled with other ‘omics’ data.

We offer diversified data analysis services to meet the needs of your metabolomics programs, which include but not limited to:

Statistical analysis
- Univariate analysisThe comparison of microbial metabolomes associated with a single variable between samples and sample sets.
- Multivariate analysisThe comparison of microbial metabolomes associated with multiple variables between samples and sample sets, mostly performed with methods such as PCA, PLS-DA, and OPLS-DA.
- High-dimensional feature selectionHigh-dimensional datasets can consist of hundreds and thousands of features. The feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures in an easy to verify, easy to comprehend, and visualized manner.
- ClusteringDividing observed datasets into a few subclasses or clusters on the basis of a selected statistical distance function.
- Supervised classificationCan be used to select the important features that might elucidate biomarkers. Algorithms include PLS, ANOVA, PLS-DA, etc.
Metatolite set enrichment analysis (MSEA)To identify and interpret patterns of metabolite concentration changes in a biologically meaningful way.
Pathway analysisPathway analysis identifies metabolites related to key cellular signaling and metabolic networks, which reveals underlying mechanisms of differentially expressed metabolites.
Time series and Two-factor data analysisVarious analyses include but not limited to interactive 3D PCA visualization, two-way heatmaps with hierarchical clustering, two-way ANOVA for univariate two-factor analysis, multivariate empirical Bayes time-series analysis (MEBA), ANOVA-simultaneous component analysis (ASCA), etc.
Metabolite mapping
Biomarker analysisMetabolomics offers potential advantages in sensitivity and specificity. Metabolites biomarker analysis provides keen insight into potential application in clinical and health areas.
Sample size and power analysisTo use bioinformatics algorithms for sample size and power estimation procedures, which are key aspects of experimental design, for metabolic phenotyping studies.

Coupled state-of-the-art techniques with powerful analysis platforms that carry abundant analytical tools and sophisticated pipelines, we have full capability of exploring the taxonomic and functional compositions of gut microbial communities that play crucial roles in human health. Correspondingly, we provide comprehensive and delicate bioinformatics services for your gut microbiota research in order to bring more blessings for our future. To find out more about our bioinformatic services to meet your specific needs, please feel free to contact us!     

For Research Use Only. Not for use in diagnostic procedures.

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