Genome and Metabolome Integration Analysis



Metabolomics is a science that quantitatively describes the response of endogenous metabolites to internal and external factors, and can directly reflect the terminal and phenotypic information of living organisms. In recent years, it has played an important role in the fields of disease diagnosis and classification, biomarker discovery, drug development, gene function analysis, metabolic pathways and regulatory mechanisms. Genome-wide association study (GWAS) is often used to study disease phenotypes and related mutation sites. GWAS uses whole-genome resequencing or exome sequencing technology to obtain millions of SNP molecular markers, and perform joint analysis with phenotypic data to screen out disease-related SNPs and discover gene mutations that affect complex traits. Although a large number of related genomic mutation sites have been identified through GWAS technology, and many related genes have been discovered in practice, it can only explain a small part of the genetic traits, because the data available for phenotypes are limited and most of them cannot be quantified, which greatly limits the application of GWAS in gene mapping and functional research.

The integrated analysis of metabolomics and genomics data can obtain more quantifiable data, obtain more comprehensive molecular characteristics, information on changes in pathophysiological state, drug action or stress disturbance, and reveal the molecular mechanism of disease. The integrated research of metabolomics and genomic data can be widely used in various aspects of basic medicine, clinical diagnosis, and drug development. At present, the effective integration of multiple omics information is still a difficult point, requiring the joint advancement of systems biology and computer technology. With many years of data analysis experience, CD Genomic provides you with a one-stop genome and metabolome integration analysis service.

Heritability of lipidomic profiles and genetic correlations among the lipid species.Figure1. Heritability of lipidomic profiles and genetic correlations among the lipid species. (Tabassum R, et al. 2019)

Application Fields

Genome and metabolome integration analysis is used in many fields of biomedical research:

  • Basic disease research: biomarker discovery, susceptibility gene or loci identification, disease mechanism research.
  • Clinical diagnosis: disease classification, personalized treatment.

What We Offer

As one of the experienced biomedical data analysis service providers, we offer established, cost-efficient and rapid turnaround analysis services for genome and metabolome integration analysis for researchers. The raw input genome data and metabolome data can be produced from different sequencing platforms or mass spectrometers. In addition, we can use various formats of data for analysis such as raw data files, or other intermediate data formats. We provide our clients with the following services:

  • Data analysts evaluate and filter raw data.
  • Develop suitable analysis strategies according to the data situation.
  • Genome-wide association study analysis.
  • Perform genome and metabolome integration analysis.
  • Provide a comprehensive report and report interpretation services.
  • Experienced bioinformatics engineers complete all analysis content in a short period of time.

Data Analysis Technical Route

Flow chart showing genome and metabolome integration analysis. - CD Genomics.Fig 2. Flow chart showing genome and metabolome integration analysis.

An Example Analysis Content Includes:

Based on different types of genome data (such as genome resequencing, SNP microarray data) and metabolome data (such as amino acids, lipids, etc.), we first perform genome-wide association analysis of phenotype and metabolite, and then conduct integration analysis.

  • Perform genome-wide association analysis to find association sites.
  • Heritability and genetic correlations
  • Genome and metabolome integration analysis, and find candidate association sites.
  • Screen candidate genes and perform annotation analysis on gene function.
  • Analyze metabolic pathways and find candidate targets.

Data Ready

Before data analysis, the first thing is to get your data ready. The upload data can be obtained from the following channels:

Channels of genome and metabolome input data. - CD Genomics.

In order to process data more efficiently, we prefer to receive data files in the raw format, but we can also accept pre-normalized files. In addition, if you have any questions about the data analysis cycle, analysis content and price, please click online inquiry.

What's More

For genome and metabolome integration analysis, if you don't have the raw data, CD Genomics can help by arranging for the generation of your data. Alternatively, most of the data related to GWAS comes from the database, we are able to provide services for obtaining and mining data from available databases. For more detailed information, please feel free to contact us.


  1. Tabassum R, et al. Genetic architecture of human plasma lipidome and its link to cardiovascular disease[J]. Nature Communications, 2019, 10(1):1-14.
  2. Constanze P, et al. New natural products identified by combined genomics-metabolomics profiling of marine Streptomyces sp. MP131-18[J]. Scientific Reports, 2017, 7:42382.

* For research use only. Not for use in clinical diagnosis or treatment of humans or animals.

Online Inquiry

Please submit a detailed description of your project. Our industry-leading scientists will review the information provided as soon as possible. You can also send emails directly to for inquiries.