Gene Expression Analysis

CD Genomics provides scientific researchers with comprehensive solutions for gene expression profiling, screening, validation, and biomarker analysis based on different types of gene microarray data and next-generation sequencing data.

Gene expression analysis refers to directly or indirectly measuring the expression of all or part of the genes in a sample, generally measuring the transcription product mRNA. And the RNA expression levels of different genes and/or different samples can be compared at the same time. This analysis can help scientists identify the molecular basis that causes phenotypic differences and select target genes for in-depth research. Commonly used detection methods for gene expression include: real-time fluorescent quantitative PCR (qRT-PCR), Microarray, expressed sequence tag (EST), Serial Analysis of Gene Expression, (SAGE) and RNA-seq. At present, high-throughput gene expression measurement methods are mainly gene chips and transcriptome sequencing. Using bioinformatics to analyze the differences in gene expression of the data generated by the wet experiment can mine the potential biomarkers of specific disease phenotypes and make further biomarker verification possible. Gene expression analysis provides valuable insights for the identification and verification of differentially expressed genes and biomarkers in normal organism samples and disease samples, and provides a basis for subsequent wet experiments.

What Can Gene Expression Analysis Do?

Gene expression data analysis is to answer some biological questions by analyzing the gene expression matrix. There are two types of analysis ideas: The first category is time series analysis, the main idea is to find common regulatory genes through analysis methods such as clustering and principal component analysis based on the expression values of genes at multiple time points, and then to study their deep mechanisms. The second category is the significance analysis of the difference in gene expression, which analyzes the difference in expression of the same gene in different samples to study the influence of the differential expression of genes on the phenotype. In gene expression analysis, the significance analysis of gene expression differences is the top priority in current gene analysis. Its purpose is to compare gene expression differences under two conditions or sources, and through statistical methods, identify specific genes related to conditions, and then further analyze the biological significance of these specific genes.

CD Genomics Data Analysis Process

Fig 1. The general process of differentially expressed gene data analysis.

Explore CD Genomics Gene Expression Analysis Solutions

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CD Genomics provides customers with different types of gene expression data analysis services. We provide gene expression analysis solutions for different types of data such as NGS Data & Microarray data, and present gene expression in the form of heat maps and volcano plots. In addition, for differential expression analysis, we also provide supporting downstream analysis solutions, such as gene function annotation, cluster analysis, etc.

Our Service Process

Biomedical-Bioinformatics, a division of CD Genomics, provides a one-stop gene expression analysis service according to customers' requirements. In addition, we will customize the analysis strategy for you according to your analysis needs, including the software, algorithms, and models used in the analysis. If you have any questions about the data analysis content, turnaround time and price, please feel free to contact us. We have a professional technical support team to provide you with the best services, and we look forward to working with you!

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