Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Integration Platforms market size is valued at USD 1.62 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.1% expected during the forecast period. By 2033, the market is projected to reach approximately USD 4.38 billion, driven by the surging demand for comprehensive biological data analysis in healthcare and life sciences. Key growth factors include the increasing adoption of precision medicine, the rapid expansion of genomics research, and the need for integrated solutions that can manage, analyze, and interpret complex multi-omics datasets for actionable insights.

The primary growth driver for the Multi-Omics Data Integration Platforms market is the escalating demand for precision medicine and personalized therapies. As healthcare providers and pharmaceutical companies increasingly shift towards individualized treatment regimens, the integration of diverse omics data—such as genomics, transcriptomics, proteomics, and metabolomics—has become essential. These platforms enable researchers to uncover complex biological interactions, identify novel biomarkers, and accelerate drug discovery processes. The convergence of high-throughput sequencing technologies with advanced computational tools has further amplified the need for robust multi-omics integration, facilitating more accurate disease modeling and patient stratification.

Another significant factor fueling market expansion is the rising volume and complexity of biological data generated by next-generation sequencing (NGS), mass spectrometry, and other high-throughput omics technologies. Research institutions, academic centers, and pharmaceutical companies are increasingly investing in multi-omics data integration platforms to manage and analyze these vast datasets efficiently. The integration of artificial intelligence and machine learning algorithms into these platforms further enhances their analytical capabilities, enabling the extraction of meaningful patterns and insights from heterogeneous data sources. This technological advancement is not only accelerating research and development activities but also improving clinical decision-making and patient outcomes.

Additionally, the increasing prevalence of chronic diseases and the growing emphasis on translational research are propelling the adoption of multi-omics data integration platforms across various healthcare settings. Hospitals, clinics, and diagnostic laboratories are leveraging these platforms to support early disease detection, monitor disease progression, and tailor therapeutic interventions. The expanding applications of multi-omics platforms in agriculture, environmental science, and food safety are also contributing to market growth. Furthermore, strategic collaborations among academic institutions, industry players, and government agencies are fostering innovation and driving the development of next-generation data integration solutions.

From a regional perspective, North America currently leads the global multi-omics data integration platforms market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of leading biotechnology and pharmaceutical companies, advanced healthcare infrastructure, and substantial investments in omics research. Europe follows closely, driven by strong government support for genomics and precision medicine initiatives. Meanwhile, the Asia Pacific region is poised for the fastest growth over the forecast period, fueled by increasing healthcare expenditure, expanding research activities, and rising awareness of the benefits of integrated omics approaches. Latin America and the Middle East & Africa are also witnessing steady growth, supported by improving research capabilities and growing healthcare investments.

Component Analysis

The Component segment of the Multi-Omics Data Integration Platforms market is primaril

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Integration Platforms market size reached USD 1.47 billion in 2024, reflecting robust growth driven by the increasing adoption of precision medicine and advanced bioinformatics. The market is projected to expand at a CAGR of 14.2% during the forecast period, reaching a value of USD 4.19 billion by 2033. This remarkable growth is primarily fueled by the rising demand for comprehensive data analysis in genomics, proteomics, and other omics sciences, facilitating breakthroughs in drug discovery, diagnostics, and personalized healthcare.

One of the primary growth factors for the Multi-Omics Data Integration Platforms market is the escalating volume and complexity of biological data generated through next-generation sequencing, mass spectrometry, and other high-throughput technologies. As research institutions and healthcare providers increasingly rely on multi-omics approaches to gain a holistic view of biological systems, there is a pressing need for platforms that can seamlessly integrate, manage, and interpret diverse datasets. The convergence of genomics, transcriptomics, proteomics, metabolomics, and epigenomics data is enabling researchers to uncover novel biomarkers, understand disease mechanisms, and develop more targeted therapies, thereby driving the demand for sophisticated integration solutions.

Another significant driver is the rapid advancement in artificial intelligence and machine learning algorithms, which are being incorporated into multi-omics data integration platforms to enhance data analysis capabilities. These technologies empower platforms to deliver actionable insights from complex, multidimensional datasets, accelerating the pace of discovery in drug development and precision medicine. Pharmaceutical and biotechnology companies are increasingly investing in these platforms to streamline their R&D processes, reduce time-to-market for new drugs, and improve patient outcomes. Furthermore, the growing trend toward cloud-based deployment is making these platforms more accessible, cost-effective, and scalable, further propelling market growth.

The expanding application of multi-omics integration in clinical diagnostics and personalized healthcare is also contributing to market expansion. With the global healthcare sector shifting toward patient-centric models, there is a heightened emphasis on identifying individual molecular profiles to guide treatment decisions. Multi-omics platforms enable clinicians to integrate genetic, proteomic, and metabolomic data for comprehensive patient assessment, leading to more accurate diagnoses and the development of tailored therapeutic strategies. This paradigm shift is particularly evident in oncology, rare diseases, and complex chronic conditions, where multi-omics integration is proving invaluable for early detection, prognosis, and therapeutic monitoring.

From a regional perspective, North America continues to dominate the Multi-Omics Data Integration Platforms market, accounting for the largest share in 2024 due to its advanced healthcare infrastructure, strong presence of leading biotech companies, and substantial investments in genomics research. Europe follows closely, driven by supportive government initiatives and a thriving academic research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing healthcare expenditure, expanding genomics research capabilities, and rising awareness of precision medicine. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing adoption of multi-omics technologies in research and clinical settings.

Component Analysis

The component segment of the Multi-Omics Data Integration Platforms market is bifurcated into software and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of data integration, offering robust analytical tools, visualization modules, and interoperability features that facilitate the seamless amalgamation of diverse omics datasets. These platforms are designed to handle massive data volumes, manage data heterogeneity, and provide user-friendly interfaces for researchers and clinicians. The increasing sophistication of software, including AI-driven analytics and cloud-based functionalities, is enhancing their adoption across pharmaceutical, academic, and clinical

Global Multi-omics Data Integration AI Market is segmented by Application (Healthcare_Pharmaceuticals_IT_Biotechnology_Research & Development), Type (AI-Powered Multi-omics Analysis_Personalized Medicine_Data-Driven Biomarker Discovery_Gene Expression Modeling_Clinical Data Integration), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Multi-Omics Data Integration Platforms Market Outlook

According to our latest research, the Global Multi-Omics Data Integration Platforms market size was valued at $1.25 billion in 2024 and is projected to reach $5.67 billion by 2033, expanding at a robust CAGR of 18.7% during the forecast period of 2025–2033. The primary driver for this remarkable growth is the accelerating adoption of personalized and precision medicine, which relies heavily on the integration of diverse omics datasets—such as genomics, proteomics, transcriptomics, and metabolomics—to derive actionable insights for disease diagnosis, treatment planning, and drug development. As healthcare and life sciences organizations strive to harness the power of big data for advanced analytics, the demand for scalable, interoperable, and user-friendly multi-omics data integration platforms is expected to surge across the globe.

Regional Outlook

North America currently dominates the Multi-Omics Data Integration Platforms market, accounting for over 42% of the global revenue share in 2024. This leadership is attributed to the region’s mature healthcare infrastructure, substantial investments in life sciences research, and widespread adoption of advanced data analytics technologies. The presence of major pharmaceutical and biotechnology companies, coupled with robust collaborations between academic research institutes and industry, further fuels market growth. Additionally, favorable government policies, such as the Precision Medicine Initiative in the United States, have accelerated the integration of multi-omics data into clinical and research workflows. These factors, combined with a high concentration of skilled bioinformaticians and data scientists, have solidified North America’s position as the epicenter of innovation and commercialization in this market.

The Asia Pacific region is poised to be the fastest-growing market, with a projected CAGR of 22.4% from 2025 to 2033. This rapid expansion is driven by increasing government funding for genomics and biotechnology research, rising awareness of precision medicine, and the proliferation of next-generation sequencing technologies. Countries such as China, Japan, and South Korea are making significant investments in healthcare digitization and are establishing large-scale population genomics projects. Strategic partnerships between local academic institutions and global platform providers are also catalyzing adoption. Moreover, the growing burden of chronic diseases and an expanding base of clinical trials in the region are creating a fertile environment for the deployment of multi-omics data integration solutions.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing multi-omics data integration platforms, albeit at a slower pace due to infrastructural and regulatory challenges. The adoption rate is hampered by limited access to high-throughput sequencing technologies, a shortage of skilled professionals, and constrained healthcare budgets. However, localized demand is rising, particularly in urban centers and research hubs, where there is increasing recognition of the value of integrated omics data in improving clinical diagnostics and agricultural productivity. Policy reforms aimed at fostering innovation, coupled with international collaborations and capacity-building initiatives, are expected to gradually overcome these barriers and unlock new growth opportunities in these regions over the next decade.

Report Scope

Multi-Omics Data Integration SaaS Market Outlook

According to our latest research, the global Multi-Omics Data Integration SaaS market size in 2024 is valued at USD 1.98 billion, reflecting the rapidly growing adoption of integrated omics solutions worldwide. The market is registering a robust CAGR of 15.3% and is forecasted to reach USD 5.54 billion by 2033. This exceptional growth is primarily driven by the increasing demand for comprehensive biological data analysis in drug discovery, precision medicine, and clinical diagnostics. As per our latest research, the convergence of cloud computing, advanced analytics, and the exponential rise in omics data generation are key propellants fueling this market’s expansion.

One of the most significant growth factors underpinning the expansion of the Multi-Omics Data Integration SaaS market is the surge in next-generation sequencing (NGS) and high-throughput omics technologies. The cost of sequencing genomes and other omics layers has plummeted over the past decade, resulting in an unprecedented volume of data generation across genomics, proteomics, metabolomics, and transcriptomics. This data deluge necessitates advanced integration platforms, and SaaS-based solutions are uniquely positioned to provide scalable, secure, and collaborative environments for researchers and clinicians. The integration of AI and machine learning algorithms further enhances the value of these platforms by enabling sophisticated data mining, biomarker discovery, and predictive modeling, which are critical for advancing precision medicine and accelerating drug development pipelines.

Another pivotal growth driver is the increasing focus on personalized healthcare and precision medicine initiatives globally. Governments, research institutions, and healthcare providers are investing heavily in multi-omics approaches to unravel complex disease mechanisms, identify novel therapeutic targets, and tailor interventions to individual patient profiles. SaaS-based multi-omics platforms offer the flexibility and interoperability required to combine diverse datasets from genomics, proteomics, transcriptomics, and beyond, providing holistic insights into biological systems. This capability is particularly valuable in oncology, rare disease research, and chronic disease management, where integrated omics analyses are transforming clinical diagnostics and treatment paradigms. The seamless accessibility and collaborative features of SaaS platforms are further accelerating cross-institutional research and translational medicine efforts.

Regulatory support and increasing investments from both public and private sectors are also catalyzing the growth of the Multi-Omics Data Integration SaaS market. Governments in North America, Europe, and Asia Pacific are launching large-scale genomics and multi-omics projects, providing funding for infrastructure development, and fostering public-private partnerships. Additionally, the pharmaceutical and biotechnology industries are embracing SaaS-based multi-omics solutions to enhance R&D productivity, reduce time-to-market, and improve the success rates of clinical trials. The growing awareness of the benefits of integrated omics analysis among hospitals, clinics, and academic research institutes is further expanding the customer base for these platforms, paving the way for sustained market growth over the forecast period.

From a regional perspective, North America continues to dominate the Multi-Omics Data Integration SaaS market, driven by the presence of leading technology providers, advanced healthcare infrastructure, and significant R&D investments. However, Asia Pacific is emerging as the fastest-growing region, fueled by expanding genomics initiatives, increasing healthcare digitalization, and rising investments in precision medicine. Europe also holds a substantial market share, supported by robust government funding and a strong focus on collaborative research networks. The Middle East & Africa and Latin America, while currently smaller in market size, are witnessing growing adoption as awareness of multi-omics integration and its clinical applications spreads.

Spatial Multi-Omics Data Integration Software Market Outlook

According to our latest research, the global spatial multi-omics data integration software market size reached USD 392.5 million in 2024, demonstrating robust growth fueled by increasing adoption of multi-omics technologies in biomedical research and clinical practice. The market is projected to expand at a remarkable CAGR of 13.7% during the forecast period, with the value expected to reach approximately USD 1,162.8 million by 2033. This accelerated growth is primarily driven by the surging demand for integrated data solutions to unravel complex biological mechanisms, enhance drug discovery, and enable precision medicine initiatives. As per our latest research, the marketÂ’s momentum is underpinned by technological advancements, rising R&D investments, and the growing prevalence of chronic diseases necessitating advanced diagnostic and therapeutic strategies.

One of the primary growth factors propelling the spatial multi-omics data integration software market is the increasing need for comprehensive biological insights at the cellular and tissue levels. The convergence of genomics, transcriptomics, proteomics, metabolomics, and epigenomics data enables researchers and clinicians to capture a multidimensional view of biological systems. This holistic approach is essential for understanding disease heterogeneity, tumor microenvironments, and cellular interactions, particularly in oncology and immunology. The rapid evolution of spatial omics technologies, coupled with the availability of high-throughput sequencing platforms, has generated massive datasets that require sophisticated integration and analysis tools. Consequently, the demand for advanced software solutions capable of harmonizing and interpreting complex multi-omics data is experiencing a significant uptick across both academic and industrial settings.

Another critical driver for the market is the accelerating pace of drug discovery and development, which increasingly relies on spatial multi-omics data integration to identify novel therapeutic targets and biomarkers. Pharmaceutical and biotechnology companies are leveraging these software platforms to streamline the drug development pipeline, reduce attrition rates, and personalize treatment regimens based on patient-specific molecular profiles. The integration of spatial and multi-omics data enhances the ability to predict drug responses, monitor disease progression, and assess therapeutic efficacy in real time. Furthermore, collaborations between software providers, academic institutions, and life science companies are fostering the development of user-friendly, scalable, and interoperable solutions that cater to the evolving needs of end users. This collaborative ecosystem is expected to sustain market growth by facilitating knowledge transfer, standardization, and innovation.

The rising adoption of personalized medicine and precision diagnostics is further fueling the spatial multi-omics data integration software market. As healthcare systems worldwide shift toward individualized care paradigms, there is a growing emphasis on leveraging multi-layered molecular data to inform clinical decision-making. Spatial multi-omics integration software enables clinicians to correlate genetic, transcriptomic, proteomic, and metabolic alterations with spatial context, thereby improving the accuracy of disease classification, prognosis, and therapeutic selection. This paradigm shift is particularly evident in oncology, neurology, and rare disease management, where spatially resolved molecular insights can guide targeted interventions. The increasing prevalence of chronic diseases, aging populations, and the need for early disease detection are expected to drive sustained investments in multi-omics data integration capabilities across healthcare and research institutions.

Regionally, North America continues to dominate the spatial multi-omics data integration software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the presence of leading life science companies, advanced healthcare infrastructure, and substantial government funding for multi-omics research. Europe follows closely, benefiting from strong academic networks and growing investments in precision medicine initiatives. The Asia Pacific region is emerging as a high-growth market, driven by expanding genomics research, increasing healthcare expenditure, and rising awareness of the benefits of integrated omics analyse

Data and pre-trained MEFISTO model to run the vignettes and tutorials provided at https://biofam.github.io/MOFA2/MEFISTO.html.* Evodevo application: Input data is provided as evodevo.csv and evodevo.RData, the trained MEFISTO model is provided in evodevo_model.hdf5 * Longitudinal microbiome application: Input data is provided as microbiome_data.csv and microbiome_features_metadata.csv, the trained MEFISTO model is provided in microbiome_model.hdf5 * single cell multi-omics application: Input data is provided as scnmt_data.txt.gz and scnmt_sample_metadata.txt the trained MEFISTO model is provided in scnmt_mefisto_model.rds * spatial transcriptomics application: Input data is downloaded as described in the tutorial, the trained MEFISTO model is provided in ST_model.hdf5

Triple negative breast cancer is an aggressive type of breast cancer with very little treatment options. TNBC is very heterogeneous with large alterations in the genomic, transcriptomic, and proteomic landscapes leading to various subtypes with differing responses to therapeutic treatments. We applied a multi-omics data integration method to evaluate the correlation of important regulatory features in TNBC BRCA1 wild-type MDA-MB-231 and TNBC BRCA1 5382insC mutated HCC1937 cells compared with normal epithelial breast MCF10A cells. The data includes DNA methylation, RNAseq, protein, phosphoproteomics, and histone post-translational modification. Data integration methods identified regulatory features from each omics method had greater than 80% positive correlation within each TNBC subtype. Key regulatory features at each omics level were identified distinguishing the three cell lines and were involved in important cancer related pathways such as TGFbeta signaling, PI3K/AKT/mTOR, and Wnt/beta-catenin signaling.

High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multi-omics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.1

Additional file 1. Horizontal integration analysis script. R script for performing horizontal integration as presented in the paper.

Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different modalities share a common underlying distribution, aiming to project them into a single unified latent space. This assumption, however, can obscure the unique insights offered by each modality, thereby limiting the full potential of multi-omics analyses. To address this limitation, we present the Spatial Multi-View (SpaMV) representation learning algorithm which captures both the shared information across modalities and the distinct, modality-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and provides users with more interpretable dimension reduction for downstream analysis. Moreover, SpaMV uniquely annotates cell types within clusters of a mouse thymus dataset, highlighting its effectiveness in interpretable dimensionality reduction.

Additional file 2: Supplementary Table S1. Merged annotation and Arabidopsis homologs of all DEGs. Supplementary Table S2. Databases used for annotation of DEGs and assignment of Arabidopsis homologs.

Omics Data Integration AI Market Outlook

According to our latest research, the global Omics Data Integration AI market size reached USD 1.89 billion in 2024, with a robust compound annual growth rate (CAGR) of 23.7% observed. The market is projected to surge to USD 14.84 billion by 2033, driven by the increasing convergence of artificial intelligence (AI) with multi-omics data analytics. This remarkable expansion is fueled by the rising demand for precision medicine, accelerated drug discovery, and the need for advanced data integration tools in biological research, as per our 2025 industry analysis.

One of the primary growth factors for the Omics Data Integration AI market is the explosive increase in biological data generation across genomics, proteomics, transcriptomics, and metabolomics. The widespread adoption of next-generation sequencing and high-throughput screening technologies has resulted in vast, complex datasets that require sophisticated computational approaches for meaningful interpretation. AI-powered integration platforms are increasingly seen as indispensable for extracting actionable insights from these diverse data types. The ability of AI to automate pattern recognition, identify novel biomarkers, and predict disease trajectories is revolutionizing both academic research and clinical applications. As a result, investment in AI-driven omics platforms is accelerating, especially among pharmaceutical and biotechnology enterprises seeking to reduce time-to-market for new therapeutics and diagnostics.

Another significant driver is the growing emphasis on personalized medicine and targeted therapies. Healthcare systems worldwide are shifting from a one-size-fits-all approach to more individualized treatment regimens based on a patient’s unique molecular profile. This transition necessitates the integration of multi-omics data—spanning genomics, epigenomics, transcriptomics, and metabolomics—using advanced AI algorithms capable of deciphering complex biological interactions. AI-enabled platforms are enabling clinicians and researchers to identify patient subgroups, stratify disease risk, and optimize therapeutic interventions with unprecedented accuracy. These advancements are not only improving patient outcomes but are also driving the adoption of AI-based omics data integration solutions across hospitals, research institutions, and clinical laboratories.

The Omics Data Integration AI market is also benefitting from expanding applications beyond healthcare, particularly in agriculture and crop science. AI-powered omics platforms are being leveraged to enhance crop yield, disease resistance, and nutritional content by integrating genomic, proteomic, and metabolomic data from various plant species. This multidisciplinary approach is enabling agri-biotech companies and research institutes to accelerate breeding programs, develop climate-resilient crops, and address global food security challenges. The convergence of omics data integration and AI is thus creating new opportunities across multiple sectors, further propelling market growth and innovation.

Regionally, North America continues to dominate the Omics Data Integration AI market, accounting for the largest share due to its advanced healthcare infrastructure, substantial R&D investments, and the presence of leading AI and life sciences companies. Europe follows closely, supported by strong government funding for precision medicine initiatives and a robust academic research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing adoption of genomics and AI technologies in countries such as China, Japan, and India. These regional dynamics are shaping the competitive landscape and influencing global market trends.

Component Analysis

The Omics Data Integration AI market is segmented by component into Software, Hardware, and Services, each playing a pivotal role in the ecosystem. Software solutions</

R code for all analyses

Through the developments of Omics technologies and dissemination of large-scale datasets, such as those from The Cancer Genome Atlas, Alzheimer’s Disease Neuroimaging Initiative, and Genotype-Tissue Expression, it is becoming increasingly possible to study complex biological processes and disease mechanisms more holistically. However, to obtain a comprehensive view of these complex systems, it is crucial to integrate data across various Omics modalities, and also leverage external knowledge available in biological databases. This review aims to provide an overview of multi-Omics data integration methods with different statistical approaches, focusing on unsupervised learning tasks, including disease onset prediction, biomarker discovery, disease subtyping, module discovery, and network/pathway analysis. We also briefly review feature selection methods, multi-Omics data sets, and resources/tools that constitute critical components for carrying out the integration.

Multi-omics has the promise to provide a detailed molecular picture for biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimum structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to associate with a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30°C and 37°C, and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37°C.

Additional file 2: Table S1. General information of three real datasets downloaded from TCGA. Table S2. Top 20 rules identified from BRCA mRNA dataset. Table S3. Top 20 rules identified from BRCA DNA methylation. Table S4. Top 20 rules identified from ESCA mRNA dataset. Table S5. Top 20 rules identified from ESCA DNA methylation dataset. Table S6. Top 20 rules identified from LUAD mRNA dataset. Table S7. Top 20 rules identified from LUAD DNA methylation dataset. Table S8. Top 20 rules identified from the combined BRCA mRNA and DNA methylation datasets. Table S9. Top 20 rules identified from the combined ESCA mRNA and DNA methylation datasets. Table S10. Top 20 rules identified from the combined LUAD mRNA and DNA methylation datasets.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.