Discover high-sensitivity, multiplex protein analysis with Olink— optimized for biomarker discovery and multi-omics integration.
Olink leverages Proximity Extension Assay (PEA) technology to deliver high-specificity, high-sensitivity protein quantification. With the ability to measure hundreds of proteins from a single sample, it’s a powerful platform for biomarker discovery, disease profiling, and precision medicine research.
Proteins are direct indicators of biological activity and disease states, yet have traditionally been harder to access than genomic data. Macrogen bridges this gap by combining Olink protein data with genomic insights, enabling multi-omics analysis for more personalized and predictive research outcomes.
SERVICE
Olink
Using Olink panels, Macrogen supports a full workflow from large-scale screening to target identification and validation — helping researchers accelerate protein biomarker discovery with greater accuracy and scalability.
Olink’s PEA technology combines immunoassay precision with DNA-based readout. When two DNA-tagged antibodies bind to a target protein, a unique DNA sequence is generated and quantified via qPCR or NGS. This allows for precise, multiplexed measurement of protein biomarkers — even at low concentrations.
Product | Contents | Features |
---|---|---|
Olink® Explore 384 Olink® Explore 1536 Olink® Explore 3072 |
~370 Proteins ~1,480 Proteins ~2,960 Proteins |
Mass screening and NGS of data from the entire Plink protein library can be offered |
Olink® Target 96 | 92 Proteins | Research on 15 major pathways (including one panel using mice) is possible |
Olink® Target 48 Cytokine | 45 Proteins | Appropriate for research on cyotokine and infection-related diseases; Quantitative values can be provided from the tests |
Olink® FLEX | 15~21 Proteins | From the 200 proteins carefully selected from the Olink data base, further customization can be carried out for the panel; Quantitative values can be provided from the tests |
※It's a kind of Olink® Target 96 panel
Areas of Analysis | Description |
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Data analysis of DE | Basic statistics(Fold change, group mean, sd etc.) |
Identifying differentially expressed protein(T-test, Mann-Whitney U test, etc.) | |
Multiple testing correction (FDR, Bonferroni etc.) | |
Clustering Analysis for DEP ( Hierarchical clustering, etc.) |