mRNA-Proteome-Metabolome Integration Analysis

Introduction

Proteins are the ultimate carriers for performing functions in organisms. Proteomics research is mainly based on mass spectrometry proteomics. Metabolomics is an emerging discipline in the field of systems biology. It explores the metabolic mechanism of the entire organism by detecting changes in the metabolites in the organism after exogenous stimulation or genetic modification. A comprehensive analysis of a series of metabolites produced in the life process of an organism can help reveal the connection between genotype and phenotype. Integrated multi-omics analysis is currently the most effective method for comprehensive analysis of metabolites.

The transcriptome and proteome data are related to each other according to the translation relationship between mRNA and protein. Through this relationship, the mRNA and its translated protein are integrated, that is, the matching transcript data is found by protein ID. The most common idea of ​​proteomics and metabolomics integrated analysis is based on the data integration of the same KEGG pathway, by finding the differentially expressed proteins involved in an important metabolic pathway, and focusing on this pathway in the results of metabolomics analysis. Based on the change of metabolites in the KEGG pathway, we will further explore whether the changes of metabolites are caused by changes in proteins. Based on this, we can find the proteins and metabolites that have significant changes in the same biological process (KEGG Pathway), and quickly lock in key proteins and mining-related target molecules. Based on the above research ideas, CD Genomics provides medical researchers with integrated analysis services of transcriptome, proteomics and metabolomics.

Figure1. Metabolomics and Proteomics Reveal Cancer Cell Death Mechanisms Associated with Au-Nanorod Photo-thermal Therapy. (Ali M.R.K, et al. 2016)

Application Fields

In the field of biomedicine, the integrated analysis of mRNA, proteomics and metabolomics is mainly used in the occurrence and development of diseases and basic scientific research:

• mRNA and protein interaction analysis.
• Metabolic Mechanism Research.
• Study the link between genotype and phenotype.
• Research on the regulation mechanism of disease occurrence and development.
• Biomarker discovery.

What We Offer

As one of the experienced bioinformatics data analysis providers, we offer established, cost-efficient and rapid turnaround analysis services for mRNA-proteome-metabolome integration analysis. The raw input mRNA, proteomics data and metabolomics data can be produced from different sequencing platforms or technologies (including next-generation, third-generation, microarray data, HPLC, LC-MS and LC-MS/MS, etc.). 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 data analysis services:

• Raw data filtering and evaluation.
• Develop an analysis plan (including the selection of analysis strategies and software).
• mRNA-proteome-metabolome integration analysis and personalized analysis content.
• Generate complete results and reports.
• Repeatable analysis results and fast analysis process.

Data Analysis Technical Route

Fig 2. A flow chart showing mRNA, protein and metabolite integration analysi.

Analysis Content Includes:

Based on mRNA, metabolite, and protein sequencing data, CD Genomics provides you with a one-stop data analysis service. In addition to the following regular analysis content, we can also provide customized analysis services according to your requirements.

• mRNA, metabolite, and protein raw data quality control.
• Perform differential expression analysis on mRNA, proteome data, and metabolome data, respectively.
• mRNA and protein correlation analysis
• Target gene GO annotation, KEGG enrichment analysis.
• Related protein GO annotation, KEGG enrichment analysis.
• Interaction analysis of mRNA and protein correlation results.
• Diagram of protein interaction network.
• Differential expression metabolite analysis and Pearson correlation coefficient analysis.
• Combined analysis of mRNA, protein and metabolome.
• Pathway integration analysis, screening of differentially expressed metabolites based on KEGG annotation.
• Classification of differential metabolites.
• Statistics and cluster analysis of mRNA-protein-metabolite pathways.
• Regulation network.

Data Ready

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

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.

Reference

1. Ali M.R.K, et al. Simultaneous Time-Dependent Surface-Enhanced Raman Spectroscopy, Metabolomics, and Proteomics Reveal Cancer Cell Death Mechanisms Associated with Gold Nanorod Photothermal Therapy[J]. J. Am. Chem. Soc. 2016. DOI: 10.1021/jacs.6b08787.

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