Multi-Omics Integrated Analysis

Technical Services

NEORABIO

Technical Services

Multi-Omics Integrated Analysis
Multi-Omics Integrated Analysis
Neorabio provides multi-omics integrated analysis services designed to support systems-level investigation of complex biological processes. While individual omics layers capture specific molecular aspects, biological phenotypes often emerge from coordinated regulation across genomic, transcriptional, epigenetic, proteomic, and metabolic levels. Systems biology studies have shown that integrating multiple omics modalities enables reconstruction of regulatory cascades and functional relationships that are not observable within single-layer analyses, a perspective comprehensively articulated in disease-focused multi-omics reviews by Hasin, Seldin, and Lusis (2017). Within this framework, multi-omics integration functions not as data aggregation, but as a structured analytical approach for linking molecular layers into coherent biological mechanisms.

About Service

Neorabio's multi-omics integrated analysis services are built on a structured analytical framework that combines standardized preprocessing with integration strategies tailored to cross-layer interpretation. Input datasets may originate from Neorabio's experimental platforms or external sources and are first harmonized through modality-specific normalization and batch-effect assessment. Integration strategies are selected based on data characteristics and study objectives and may include correlation-based association analysis, pathway-anchored integration, and network-level modeling.

Our Scope

● Cross-omics Data Harmonization
● Standardized preprocessing and normalization across multiple molecular layers
● Integration Strategy Selection
● Application of pathway-, network-, or phenotype-driven integration frameworks
● Systems-level Interpretation
● Organization of results into biologically interpretable gene, pathway, and process-level outputs

Applications

● Mechanistic studies linking regulatory and functional molecular layers
● Biomarker and molecular signature discovery across omics modalities
● Network-level modeling of complex biological systems
● Cross-layer target and pathway prioritization
● Exploratory, hypothesis-generating analysis in data-rich studies

Workflow

Exploratory Consultation → Data Landscape Review & Integration Objective Definition → Cross-Omics Harmonization & Feature Mapping Strategy → Multi-Layer Data Integration & Statistical Modeling → Systems-Level Biological Interpretation & Hypothesis Generation → Final Integrated Report Delivery & Scientific Review Discussion

References

Hasin, Y., Seldin, M., Lusis, A. Multi-omics approaches to disease. Genome Biology, 2017, 18: 83. DOI: 10.1186/s13059-017-1215-1
Karczewski, K. J., Snyder, M. P. Integrative omics for health and disease. Nature Reviews Genetics, 2018, 19(5): 299–310. DOI: 10.1038/nrg.2018.4
Subramanian, I., et al. Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 2020, 14: 1177932219899051. DOI: 10.1177/1177932219899051
Misra, B. B., Langefeld, C., Olivier, M., Cox, L. A. Integrated omics: tools, advances and future approaches. Journal of Molecular Endocrinology, 2019, 62(1): R21–R45. DOI: 10.1530/JME-18-0055
Argelaguet, R., et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of mul

Inquiry Center

Project execution at Neorabio emphasizes interpretability, reproducibility, and transparent reporting throughout the integration process. To address challenges inherent to heterogeneous data integration—such as scale differences, feature imbalance, and result interpretability—analytical outputs are organized at gene, pathway, and biological process levels using structured integration schemas commonly applied in multi-omics studies. In addition, network-based and pathway-centric interpretation strategies are informed by systems-level modeling concepts developed in integrative biology research, including multi-layer network approaches and phenotype-driven integration frameworks.
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