Metabolomics Bioinformatics Service
At CD ComputaBio, we specialize in Metabolomics Bioinformatics, offering cutting-edge solutions to facilitate groundbreaking discoveries in various domains, including drug development, disease prevention, and agricultural enhancement. Our team of experts utilizes advanced computational techniques to analyze complex datasets, helping researchers turn raw data into actionable insights.
Introduction to Metabolomics Bioinformatics
Metabolomics encompasses the comprehensive analysis of metabolic profiles and compounds within biological systems. With the advent of high-throughput technologies, researchers can now capture an extensive array of metabolites simultaneously. These techniques produce vast amounts of data that require sophisticated bioinformatics tools and approaches for meaningful interpretation.
Figure 1. Metabolomics Bioinformatics. (Chen et al., 2022)
Our Service
At CD ComputaBio, we understand the intricacies involved in metabolomics analysis. Our bioinformatics services include:
Metabolite Quantification
CD ComputaBio offers both relative and absolute metabolite quantification services. For relative quantification, we use statistical methods to compare metabolite levels between different samples, such as control and treatment groups. In absolute quantification, we combine experimental data with calibration curves and internal standards to determine the exact concentration of metabolites in a sample.
Pathway Analysis
We analyze metabolite data in the context of metabolic pathways. Our bioinformatics tools map identified metabolites onto known metabolic pathways, such as glycolysis, the citric acid cycle, and lipid metabolism pathways. We can identify disrupted pathways in disease states or responses to experimental interventions, providing insights into the underlying biological mechanisms.
Integration with Other Omics
Metabolomics does not exist in isolation, it often interlinks with genomics, transcriptomics, and proteomics. Our integrative analysis service combines metabolomic data with other omics data to provide a holistic view of biological systems. This enables the exploration of correlations and causative relationships among different biological molecules.
Biomarker Discovery
Our team uses computational algorithms to search for metabolite biomarkers. We analyze large datasets from multiple samples, including those from healthy and diseased individuals, to find metabolites that are significantly different in abundance. We validate potential biomarkers using independent datasets and statistical methods to ensure their reliability for applications.
Software We Use
XCMS
CD ComputaBio uses XCMS for data pre-processing, including peak detection, alignment, and retention time correction.
MetaboAnalyst
MetaboAnalyst is a comprehensive software tool for metabolomics data analysis. It offers a wide range of functions, from data upload and pre-processing.
Approaches to Metabolomics Bioinformatics
Database - Searching Approaches
We rely on comprehensive metabolite databases, such as HMDB (Human Metabolome Database) and METLIN. These databases contain information about metabolite structures, spectra, and associated biological functions.
Statistical Approaches
CD ComputaBio employs a variety of statistical methods in metabolomics bioinformatics. For data pre-processing, we use techniques such as normalization and scaling to reduce data variability.
Comparative Metabolomics
Comparative metabolomics involves analyzing metabolite profiles between different sample groups. This approach helps in uncovering alterations in metabolic pathways associated with specific conditions.
Advantages of Our Services
Metabolomics Bioinformatics is a rapidly evolving field that is crucial for understanding biological systems at the metabolite level. CD ComputaBio offers a comprehensive suite of services, incorporating different approaches, reliable software tools, and a team of experts. Our focus on customized solutions, high-quality data handling, and timely service makes us an ideal partner for metabolomics research. Contact us to learn more about our services.
Reference:
- Chen, Yang, En-Min Li, and Li-Yan Xu. Guide to metabolomics analysis: a bioinformatics workflow. Metabolites. 2022,12.4: 357.
* For Research Use Only.
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