Need expert help integrating your multi-omics data? Our senior bioinformaticians can design custom pipelines, perform robust analysis, and deliver clear, publication-ready results. Request a free consultation →
We provide tailored multi-omics bioinformatics services trusted by researchers worldwide.
In recent years, biological and biomedical research increasingly moves beyond single data types. Instead of studying only gene expression (transcriptomics) or only protein abundance (proteomics), many projects now require expert multi-omics data analysis and integration services covering RNA, ATAC-seq, DNA methylation, proteomics, and metabolomics data - all supported by robust bioinformatics pipelines.
This multi-omics approach provides a much richer and more accurate view of biological systems. By capturing complexity of regulation across multiple molecular levels, multi-omics studies can reveal hidden mechanisms, better biomarkers, and deeper biological insights which single-omics studies often miss.
However, multi-omics data analysis brings its own set of challenges - and requires careful design, data processing, and integration strategies.
Multi-omics integration refers to combination and joint analysis of two or more types of omics data, such as:
- Transcriptomics (e.g., RNA-seq)
- Proteomics (e.g., mass spectrometry)
- Epigenomics (e.g., ATAC-seq, DNA methylation arrays, CUT&Tag)
- Metabolomics (e.g., LC-MS, GC-MS data)
- Genomics (e.g., whole-genome sequencing)
- Single-cell multi-omics (e.g., Multiome, CITE-seq)
The goal is not only to analyze each dataset separately, but also to integrate them to uncover relationships between different molecular layers.
For example:
- How chromatin accessibility (ATAC-seq) influences gene expression (RNA-seq)
- How RNA abundance correlates with protein levels
- How epigenetic marks relate to metabolic pathway activities
From our experience, these relationships are often non-linear, and special care must be taken when analyzing them.
While multi-omics can be incredibly powerful, it also comes with several difficulties:
- Different data types and scales: RNA-seq, proteomics, and metabolomics have very different distributions, units, and noise levels.
- Batch effects: Data from different omics layers are often generated on different platforms, times, or laboratories.
- Missing data: Some samples may be missing one or more data types.
- Data sparsity: Especially in single-cell multi-omics, matrices can be highly sparse.
- Biological complexity: Relationships across omics layers can be highly context-dependent.
Thus, proper data normalization, quality control, and choice of integration methods are critical for reliable conclusions.
| Strategy | Description | Example Use |
|---|---|---|
| Early integration (concatenation) | Merge feature matrices before analysis. | Simple clustering across omics. |
| Intermediate integration (model-based) | Model relationships during analysis. | Joint factor analysis, network construction. |
| Late integration (ensemble) | Analyze each omics separately, then combine results. | Meta-clustering, pathway integration. |
| Graph-based integration | Build multi-omics networks capturing cross-layer relationships. | Systems biology modeling. |
| Deep learning integration | Use neural networks for heterogeneous omics integration. | Feature extraction, prediction models. |
In practice, the choice depends heavily on study aims, sample size, and missing data conditions. Our team often designs customized pipelines for different scenarios.
- Cancer research: Integrating genomics, transcriptomics, proteomics, and epigenomics to identify driver mutations and therapeutic vulnerabilities.
- Developmental biology: Combining RNA-seq and ATAC-seq data to understand regulatory networks during cell differentiation.
- Microbiome studies: Linking host transcriptomic profiles with microbiome metagenomic activities.
- Neuroscience: Mapping transcriptomic, proteomic, and epigenetic changes in neurodegenerative diseases.
We have supported projects in all of these areas and observed that integration often enables insights not reachable by single-omics alone.
At AccuraScience, we have extensive experience in multi-omics data analysis and integration. We can assist you with:
- Designing multi-omics study and data generation plans.
- Processing and QC of RNA-seq, proteomics, epigenomics, and metabolomics data.
- Applying suitable integration strategies for your study design.
- Biological interpretation of integrated results.
- Generating figures and reports suitable for publication or grant submission.
We work with both bulk and single-cell omics datasets. Whether you are integrating two or multiple omics layers, our team can help you obtain reliable and biologically meaningful results.
If you are planning a multi-omics project or currently have multi-omics data to be integrated, send us an inquiry, chat with us online (during our business hours 9-5 Mon-Fri U.S. Central Time), or reach us in other ways!
We are looking forward to helping you turn complex datasets into new biological insights.
About the Author: Zack Tu holds a B.S. in Biochemistry, an M.S. in Software Systems, and a Ph.D. in Pharmacology. A computational genomics expert with over 20 years of experience, Zack has provided informatics support to numerous research groups across academia and industry. He led the development and operation of the core bioinformatics infrastructure at the University of Minnesota’s research sequencing center (2001–2012) and contributed to a clinical genome sequencing facility. Since joining AccuraScience in 2013, he has worked extensively with NGS data generated from Illumina, Roche/454, Ion Torrent, PacBio, and Oxford Nanopore platforms. His areas of expertise include genome assembly, bulk and single-cell RNA-seq, 16S rRNA profiling, shotgun metagenomics, variant detection and interpretation, and computational modeling of mutation calling. More recently, he has contributed to projects involving spatial transcriptomics, integrative multi-omics analysis, and clinical bioinformatics applications. More at https://www.accurascience.com/our_team.html.