We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool’s interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different “visual channel” of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in “knowledge-guided” data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive “Knowledge Network.” KnowEnG adheres to “FAIR” principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system’s potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

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

This file contains the analyzed proteomic, phosphoproteomic, and metabolomic data sets as separate sheets within the excel file. The left columns for each data set are intensity values that have been row Z-scored and have conditional formatting to create a heatmap within excel. Intensity values used for analysis can be found in the right-most columns, which have been normalized and filtered. (XLSX)

Data supporting for thesis titled “Large-scale data analysis and integration to advance precision prognosis, therapy stratification and understanding of human disease”

## GMarsGT: For rare cell identification from matched scRNA-seq (snRNA-seq) and scATAC-seq (snATAC-seq),includes genes, enhancers, and cells in a heterogeneous graph to simultaneously identify major cell clusters and rare cell clusters based on eRegulon.

## Data Collection The data was collected using GEO Database.

## Data Format The data is stored as TSV file and MTX file where each row represents a gene and each column represents a sample.

## Variables - Gene IDs: Gene Symbols (e.g., MALAT1) - Sample IDs: Sample identifiers (e.g., AAACATGCAAATTCGT-1) - Expression level: Row gene expression level.

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.

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.

The Multiomics Market offers a range of products, including instruments, consumables, software, and services. Instruments include sequencing systems, mass spectrometers, and flow cytometers. Consumables encompass reagents, kits, and microarrays. Software solutions provide data analysis and visualization capabilities. Services include sample preparation, data analysis, and interpretation. Recent developments include: September 2023: The chromium single-cell gene expression flex assay manufactured by 10x Genomics Inc. now offers high throughput multi-omic cellular profiling as a commercially available capability thanks to the introduction of a new kit. Researchers and their options may detect simultaneous gene and protein expression, which can be expanded at a greater scale thanks to the new kit, which makes the multi-omic characterization of cell populations simple and efficient. The company's product portfolio was able to grow due to this technique., February 2023: Becton, Dickinson, and Company introduced the Rhapsody HT Xpress System, a high-throughput single-cell multiomics platform, to broaden the field of scientific research. With up to eight times more cells per sample than previous BD single-cell analyzers, this innovative technology allows scientists to extract, label, and analyze individual cells at a high sample throughput. This plan should assist the business in expanding its product's uses and serving more clients.. Notable trends are: Rising integration of multi-omics data is driving the market growth.

Omics Data Integration AI Market Outlook

According to our latest research, the global Omics Data Integration AI market size reached USD 1.82 billion in 2024, reflecting robust growth dynamics driven by increasing adoption of AI technologies in life sciences and healthcare. The market is expected to grow at a compelling CAGR of 21.3% from 2025 to 2033, reaching a forecasted value of USD 12.17 billion by 2033. This significant expansion is fueled by the rising demand for multi-omics data analysis, advancements in AI-driven analytics, and the growing emphasis on precision medicine across the globe.

The primary growth factor for the Omics Data Integration AI market is the explosive increase in biological data generated from next-generation sequencing, mass spectrometry, and other high-throughput omics platforms. As researchers and clinicians seek to extract actionable insights from genomics, proteomics, metabolomics, and transcriptomics datasets, AI-powered integration platforms have become indispensable. These platforms enable the synthesis and interpretation of complex biological data, supporting breakthroughs in disease mechanism elucidation, biomarker discovery, and personalized treatment strategies. The integration of diverse omics data types using AI algorithms is thus revolutionizing biomedical research, driving the rapid expansion of this market.

Another crucial driver is the heightened focus on personalized medicine and targeted therapeutics. Pharmaceutical and biotechnology companies, as well as academic research institutions, are increasingly leveraging AI-enabled omics data integration to identify novel drug targets, optimize clinical trial designs, and stratify patient populations. The ability to combine genetic, proteomic, and metabolomic data through advanced machine learning models accelerates drug discovery and enhances clinical diagnostics, thereby reducing time-to-market and improving patient outcomes. This convergence of AI and omics sciences is fostering innovation and attracting substantial investments from both public and private sectors.

Technological advancements in artificial intelligence, particularly in deep learning, natural language processing, and cloud computing, are further propelling the market. The proliferation of cloud-based omics data integration solutions facilitates seamless data sharing, real-time analytics, and collaborative research across geographies. Additionally, the integration of AI with electronic health records (EHR) and laboratory information management systems (LIMS) is streamlining data workflows, reducing operational costs, and enabling scalable deployment. As a result, the Omics Data Integration AI market is witnessing strong adoption across diverse end-user segments, from hospitals and clinics to research laboratories and agricultural biotech firms.

From a regional perspective, North America currently dominates the Omics Data Integration AI market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, benefits from a robust ecosystem of AI startups, leading genomics research centers, and favorable regulatory frameworks. Europe is experiencing rapid growth due to increased funding for precision medicine initiatives and collaborative research networks. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by expanding healthcare infrastructure, growing investments in life sciences, and government support for digital health transformation. Latin America and the Middle East & Africa, though nascent, are expected to witness accelerated adoption as awareness and technological capabilities improve.

Component Analysis

The Omics Data Integration AI market is segmented by component into Software, Hardware, and Services. Software solutions represent the backbone of this market, encompassing AI-driven platforms for data integration, visualization, and analytics. These software tools are designed to handle the complexity and scale of multi-omics datasets, offering advanced functionalities such as pattern recognition, predictive modeling, and automated feature extraction. The rapid evolution of AI algorithms, particularly in unsupervised and supervised learning, is enabling software vendors to deliver increasingly sophisticated solutions tailored to the needs of researchers, clinicians, and pharmaceutical companies.

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Background: Detailed and comprehensive genome annotation can be considered a prerequisite for effective analysis and interpretation of omics data. As such, Gene Ontology (GO) annotation has become a well accepted framework for functional annotation. The genus Aspergillus comprises fungal species that are important model organisms, plant and human pathogens as well as industrial workhorses. However, GO annotation based on both computational predictions and extended manual curation has so far only been available for one of its species, namely A. nidulans. Results: Based on protein homology, we mapped 97% of the 3,498 GO annotated A. nidulans genes to at least one of seven other Aspergillus species: A. niger, A. fumigatus, A. flavus, A. clavatus, A. terreus, A. oryzae and Neosartorya fischeri. GO annotation files compatible with diverse publicly available tools have been generated and deposited online. To further improve their accessibility, we developed a web application for GO enrichment analysis named FetGOat and integrated GO annotations for all Aspergillus species with public genome sequences. Both the annotation files and the web application FetGOat are accessible via the Broad Institute's website (http://www.broadinstitute.org/fetgoat/index.html webcite). To demonstrate the value of those new resources for functional analysis of omics data for the genus Aspergillus, we performed two case studies analyzing microarray data recently published for A. nidulans, A. niger and A. oryzae. Conclusions: We mapped A. nidulans GO annotation to seven other Aspergilli. By depositing the newly mapped GO annotation online as well as integrating it into the web tool FetGOat, we provide new, valuable and easily accessible resources for omics data analysis and interpretation for the genus Aspergillus. Furthermore, we have given a general example of how a well annotated genome can help improving GO annotation of related species to subsequently facilitate the interpretation of omics data.

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</

The global multiomics market, valued at $3.11 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 15.26% from 2025 to 2033. This expansion is driven by several key factors. Advancements in sequencing technologies, particularly next-generation sequencing (NGS), are enabling researchers to analyze multiple omics datasets simultaneously, providing a more comprehensive understanding of complex biological systems. This holistic approach is proving invaluable in drug discovery and development, accelerating the identification of novel therapeutic targets and biomarkers. Furthermore, the increasing prevalence of chronic diseases, such as cancer and neurodegenerative disorders, is fueling demand for more precise diagnostic and therapeutic tools, bolstering the multiomics market. Growing investments in research and development across both academia and the pharmaceutical and biotechnology sectors further contribute to this market's rapid growth. The integration of artificial intelligence (AI) and machine learning (ML) in multiomics data analysis is also significantly impacting the field, enabling faster and more accurate interpretations of complex datasets. The market segmentation reveals significant opportunities across various product types, platforms, and applications. While instruments and reagents constitute major segments, the 'Other Products' category, encompassing software and data analysis tools, is experiencing rapid growth due to the increasing complexity of multiomics data. Single-cell multiomics, offering higher resolution and insights into cellular heterogeneity, is gaining traction over bulk multiomics. Within platforms, genomics maintains a dominant position, followed by transcriptomics and proteomics. However, integrated omics platforms, offering a more comprehensive analysis of multiple datasets simultaneously, are showing significant potential for future growth. Oncology and neurology are leading application areas, with substantial research focused on developing personalized medicine approaches leveraging multiomics data. The academic and research institutes segment remains a key end-user, while pharmaceutical and biotechnology companies are increasingly adopting multiomics for drug discovery and development, promising sustained long-term market growth. Competition among established players like Illumina, Thermo Fisher Scientific, and Agilent Technologies, alongside emerging innovative companies, drives further market dynamism and technological advancement. Recent developments include: February 2024: Vizzhy Inc. launched the world's inaugural Multiomics Lab in Bengaluru, India, heralding a major advancement in healthcare innovation. Equipped with cutting-edge tools and health AI technology, the lab enables physicians to pinpoint root causes and offer personalized recommendations for their patients.September 2023: MGI, a provider of technology and tools for life science, introduced the DCS Lab Initiative to stimulate crucial scientific research. This initiative encourages large-scale multiomics laboratories. Under the initiative, the organization offers products for numerous applications, including cell omics, DNA sequencing, and spatial omics based on DNBSEQ technologies, to specified research institutions globally.April 2023: Biomodal, formerly Cambridge Epigenetix, introduced a new duet multiomics solution that can enable simultaneous phased reading of epigenetic and genetic information in a single, low-volume sample.. Key drivers for this market are: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Potential restraints include: Rising Demand for Single-cell Multiomics and Advancements in Omics Technologies, Increasing Investment in Genomics R&D; Growing Demand for Personalized Medicine. Notable trends are: The Bulk Multiomics Segment is Expected to Hold the Largest Share of the Market.

Multi-Omics Data Visualization Platforms Market Outlook

According to our latest research, the global Multi-Omics Data Visualization Platforms market size in 2024 is estimated at USD 1.42 billion, demonstrating a robust foundation for this rapidly evolving sector. The market is expected to grow at a CAGR of 13.7% during the forecast period, reaching a projected value of USD 4.18 billion by 2033. This exceptional growth trajectory is primarily driven by the increasing integration of multi-omics technologies in biomedical research, the escalating demand for precision medicine, and the expanding applications of omics data analytics in drug discovery and clinical diagnostics. As per the latest research, industry stakeholders are investing heavily in advanced visualization tools to address the growing complexity of multi-dimensional biological datasets.

The surge in adoption of multi-omics data visualization platforms is underpinned by the exponential growth of biological data generated from high-throughput sequencing technologies. Researchers and clinicians now face the challenge of analyzing and interpreting vast, heterogeneous datasets encompassing genomics, proteomics, transcriptomics, metabolomics, and epigenomics. The need for intuitive, scalable, and interactive visualization platforms has become paramount to enable meaningful insights from these complex data layers. Furthermore, the integration of artificial intelligence and machine learning algorithms within these platforms is enhancing data interpretation, pattern recognition, and predictive analytics, thereby accelerating the pace of biomedical discoveries. The convergence of these technological advancements is fueling the widespread adoption of multi-omics data visualization platforms across the globe.

Another significant growth factor is the rapid advancement of personalized medicine and precision healthcare initiatives. Multi-omics data visualization platforms play a crucial role in translating multi-layered biological information into actionable clinical insights, supporting the development of targeted therapies and individualized treatment strategies. Pharmaceutical and biotechnology companies are leveraging these platforms to streamline drug discovery processes, identify novel biomarkers, and optimize clinical trial designs. The growing focus on patient-centric care, coupled with the increasing prevalence of chronic diseases and cancer, is amplifying the demand for comprehensive multi-omics analysis and visualization solutions. As a result, the market is witnessing increased collaborations between technology providers, research institutes, and healthcare organizations to develop next-generation visualization tools tailored for clinical and translational research.

The expansion of multi-omics data visualization platforms is also being propelled by government initiatives and funding for omics research, particularly in developed regions such as North America and Europe. Strategic investments in life sciences infrastructure, coupled with the establishment of national genomics and precision medicine programs, are fostering a conducive environment for market growth. Additionally, the rising adoption of cloud-based solutions and the proliferation of open-source visualization tools are democratizing access to advanced analytics, enabling smaller research labs and academic institutions to participate in cutting-edge multi-omics research. The global market landscape is further shaped by ongoing efforts to standardize data formats, enhance interoperability, and ensure data security and privacy, which are critical for large-scale multi-omics data integration and visualization.

From a regional perspective, North America is expected to maintain its dominant position in the multi-omics data visualization platforms market, driven by the presence of leading technology vendors, well-established research infrastructure, and favorable regulatory frameworks. Europe is anticipated to witness substantial growth, supported by collaborative research initiatives and increasing investments in precision medicine. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by expanding healthcare infrastructure, rising R&D expenditures, and growing awareness of omics technologies. Latin America and the Middle East & Africa are also poised for steady growth, albeit at a slower pace, as these regions gradually adopt advanced omics research methodologies and visualization solutions.

Macrophages are central players in immune response, manifesting divergent phenotypes to control inflammation and innate immunity through release of cytokines and other signaling factors. Recently, the focus on metabolism has been reemphasized as critical signaling and regulatory pathways of human pathophysiology, ranging from cancer to aging, often converge on metabolic responses. Here, we used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. A genome-scale metabolic network for the RAW 264.7 cell line was constructed to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation were identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. This study demonstrates that the role of metabolism in regulating activation may be greater than previously anticipated and elucidates underlying connections between activation and metabolic effectors. This submission corresponds to the metabolomics data from this study.

Additional file 8. tRigon session report in html-format for statistical testing including all inputs, setting options and outputs.

Multi-Omics Data Visualization Platforms Market Outlook

According to our latest research, the multi-omics data visualization platforms market size reached USD 1.28 billion in 2024, reflecting robust momentum driven by advancements in bioinformatics and computational biology. The market is projected to grow at a compelling CAGR of 13.4% from 2025 to 2033, leading to a forecasted market size of USD 4.06 billion by 2033. This significant growth is primarily attributed to the increasing integration of multi-omics approaches in life sciences research, enabling comprehensive analysis and visualization of complex biological datasets. As per our latest research, the accelerating demand for high-throughput data analysis tools and the widespread adoption of precision medicine are key growth drivers fueling this dynamic market.

The rapid expansion of the multi-omics data visualization platforms market is fundamentally underpinned by technological advancements in sequencing and analytical tools. The evolution of next-generation sequencing (NGS), mass spectrometry, and other high-throughput omics platforms has resulted in the generation of massive and complex datasets. This, in turn, has created an urgent need for advanced visualization solutions capable of integrating, analyzing, and rendering diverse data types in a user-friendly manner. The increasing demand for holistic biological insights—spanning genomics, proteomics, transcriptomics, metabolomics, and epigenomics—necessitates platforms that can seamlessly aggregate and visually interpret multi-layered data, facilitating novel discoveries in areas such as disease mechanisms, biomarker identification, and therapeutic target validation. The convergence of artificial intelligence and machine learning with data visualization is further enhancing the analytical power and predictive capabilities of these platforms, making them indispensable for researchers and clinicians alike.

Another significant growth factor for the multi-omics data visualization platforms market is the surge in personalized medicine initiatives worldwide. Healthcare providers and life sciences organizations are increasingly leveraging multi-omics data to tailor treatments to individual patient profiles, thereby improving clinical outcomes and reducing adverse effects. This paradigm shift towards personalized healthcare is driving investments in data integration and visualization technologies that can handle the complexity and scale of multi-omics datasets. Pharmaceutical and biotechnology companies are particularly active in adopting these platforms to accelerate drug discovery and development, optimize clinical trial design, and identify patient subgroups with greater precision. As regulatory agencies emphasize data transparency and reproducibility, robust visualization tools are becoming critical for ensuring compliance and facilitating communication of research findings.

Furthermore, the growing collaboration between academic institutions, research organizations, and industry players is catalyzing the adoption of multi-omics data visualization platforms. Government funding initiatives and public-private partnerships are supporting the development of integrated bioinformatics infrastructures, fostering innovation in data analysis and visualization. The increasing prevalence of chronic diseases, such as cancer and cardiovascular disorders, is also fueling demand for comprehensive multi-omics approaches to unravel complex disease etiologies and identify novel therapeutic strategies. As the multi-omics ecosystem expands, the need for scalable, interoperable, and user-centric visualization platforms is expected to intensify, driving sustained market growth over the forecast period.

Regionally, North America continues to dominate the multi-omics data visualization platforms market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading biotechnology and pharmaceutical companies, coupled with advanced healthcare infrastructure and substantial investments in omics research, positions North America as a key growth engine. Europe is witnessing rapid adoption due to supportive government policies and a vibrant research community, while Asia Pacific is emerging as a high-growth region, propelled by increasing R&D activities and expanding healthcare expenditure. The market landscape in Latin America and the Middle East & Africa remains nascent but is expected to gain traction as awareness and access to advanced omics technologies improve.<

Additional file 1. tRigon session report in html-format for a k-means clustering analysis including all inputs, setting options and outputs.

The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

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.