OMICS is an extensive collection of knowledge for indoor service robots gathered from internet users. Currently, it contains 48 tables capturing different sorts of knowledge. Each tuple of the Help table maps a user desire to a task that may meet the desire (e.g., ⟨ “feel thirsty”, “by offering drink” ⟩). Each tuple of the Tasks/Steps table decomposes a task into several steps (e.g., ⟨ “serve a drink”, 0. “get a glass”, 1. “get a bottle”, 2. “fill class from bottle”, 3. “give class to person” ⟩). Given this, OMICS offers useful knowledge about hierarchism of naturalistic instructions, where a high-level user request (e.g., “serve a drink”) can be reduced to lower-level tasks (e.g., “get a glass”, ⋯). Another feature of OMICS is that elements of any tuple in an OMICS table are semantically related according to a predefined template. This facilitates the semantic interpretation of the OMICS tuples.

Pathway Multi-Omics Simulated Data

These are synthetic variations of the TCGA COADREAD data set (original data available at http://linkedomics.org/data_download/TCGA-COADREAD/). This data set is used as a comprehensive benchmark data set to compare multi-omics tools in the manuscript "pathwayMultiomics: An R package for efficient integrative analysis of multi-omics datasets with matched or un-matched samples".

There are 100 sets (stored as 100 sub-folders, the first 50 in "pt1" and the second 50 in "pt2") of random modifications to centred and scaled copy number, gene expression, and proteomics data saved as compressed data files for the R programming language. These data sets are stored in subfolders labelled "sim001", "sim002", ..., "sim100". Each folder contains the following contents: 1) "indicatorMatricesXXX_ls.RDS" is a list of simple triplet matrices showing which genes (in which pathways) and which samples received the synthetic treatment (where XXX is the simulation run label: 001, 002, ...), (2) "CNV_partitionA_deltaB.RDS" is the synthetically modified copy number variation data (where A represents the proportion of genes in each gene set to receive the synthetic treatment [partition 1 is 20%, 2 is 40%, 3 is 60% and 4 is 80%] and B is the signal strength in units of standard deviations), (3) "RNAseq_partitionA_deltaB.RDS" is the synthetically modified gene expression data (same parameter legend as CNV), and (4) "Prot_partitionA_deltaB.RDS" is the synthetically modified protein expression data (same parameter legend as CNV).

Supplemental Files

The file "cluster_pathway_collection_20201117.gmt" is the collection of gene sets used for the simulation study in Gene Matrix Transpose format. Scripts to create and analyze these data sets available at: https://github.com/TransBioInfoLab/pathwayMultiomics_manuscript_supplement

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.

This dataset provides 61191 individual level omics data (WGS, RNA Seq, ChIP Seq, and ATAC Seq) and genome annotation information from 21 animal species, with an effective data size of 2.8 TB. In addition, this dataset also includes gene and phenotype entity recognition data obtained based on deep learning algorithms. Overall, this multi omics dataset can be used for gene discovery and functional validation of important agricultural traits, providing valuable resources for cross species comparative research and better serving the construction of animal economic trait key gene identification models and algorithm research.

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.

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. we propose MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis.

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics
profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:

• Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)
• Lipidomics (negative and positive ionization modes)
• Proteomics
• mRNA-seq
• miRNA-seq
• miRNA qRT-PCR
• Enzymation Methylation sequencing

The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (Ramos et al. 2017). Instructions on how to read the object into R can be found here: Read_MultiAssayExperiment.

A similar object for Python (MuData) including the same data will be added later.

DATA AVAILABILITY STATEMENT:

Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (https://clindata.imtm.cz) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact data.access@imtm.cz.

Single Cell Multi-Omics Market Outlook

As of 2023, the global single cell multi-omics market size is valued at approximately USD 2.5 billion, with a robust projected CAGR of 20.1% forecasted to propel the market to USD 9.8 billion by 2032. This remarkable growth is driven by several key factors, including technological advancements in single-cell analysis techniques, increased funding for omics research, and a growing emphasis on personalized medicine. The market is experiencing a surge in demand as researchers and healthcare providers seek more precise and comprehensive insights into cellular behavior, disease mechanisms, and therapeutic responses. The integration of multi-omics data at a single-cell level offers unparalleled resolution and depth, enabling a transformative understanding of complex biological systems.

One of the primary growth drivers of the single cell multi-omics market is the rapid advancement of technology, particularly in sequencing and analytical tools. Innovations in microfluidics, next-generation sequencing, and enhanced bioinformatics capabilities have significantly lowered the cost and increased the efficiency of single-cell analysis. These technological advancements allow researchers to dissect the heterogeneity of cellular populations with unprecedented precision, facilitating breakthroughs in understanding disease pathology and developing targeted therapeutics. Moreover, the continuous evolution of these technologies fosters their adoption across various fields, further expanding the market's scope and application.

Another significant factor contributing to market growth is the escalating demand for personalized medicine. As the healthcare industry shifts towards more individualized treatment approaches, the need for comprehensive insights at a cellular level becomes paramount. Single cell multi-omics provides a holistic view of cellular function by integrating genomic, transcriptomic, proteomic, and metabolomic data. This integrated approach not only enhances the understanding of disease mechanisms but also aids in the development of personalized therapeutic strategies, thereby driving the adoption of single cell multi-omics in clinical settings. The ability to tailor treatments based on unique cellular profiles is expected to significantly boost market demand over the forecast period.

Additionally, increasing funding and investments in life sciences research is acting as a catalyst for the growth of the single cell multi-omics market. Governments, academic institutions, and private entities are investing heavily in omics research to unlock new scientific insights and address pressing healthcare challenges. This influx of funding is facilitating the establishment of state-of-the-art research facilities and fostering collaborations between academic institutions and industry players. The enhanced research infrastructure and collaborative efforts are expected to accelerate scientific discoveries and propel the market's expansion, as researchers strive to unravel the complexities of biological systems at a single-cell level.

From a regional perspective, North America currently dominates the single cell multi-omics market, owing to its robust research infrastructure, presence of leading biotechnology firms, and substantial government funding for genomics and precision medicine initiatives. However, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period, driven by increasing investments in healthcare research, the rising prevalence of chronic diseases, and the burgeoning biotechnology sector. European countries are also witnessing a growing adoption of single cell multi-omics technologies, supported by collaborative research initiatives and favorable regulatory frameworks. These regional dynamics underscore the diverse growth opportunities within the global market, as stakeholders capitalize on regional strengths and address specific healthcare needs.

Technology Analysis

The technology segment within the single cell multi-omics market is predominantly categorized into single cell genomics, single cell transcriptomics, single cell proteomics, and single cell metabolomics. Each of these sub-segments plays a crucial role in providing comprehensive insights into cellular functions and interactions. Single cell genomics, which involves the analysis of DNA at a single-cell level, has become a cornerstone technology in this market. It enables researchers to investigate genetic variations, mutations, and chromosomal aberrations with unprecedented accuracy. This technology is pivotal in advancing our understanding of genetic predisposit

The Global Multi-omics Market comprising primarily Genomics, Transcriptomics, Proteomics, & Metabonomics is set to witness a growth rate of 16%. The next growth phase is likely to be driven by growing demand for personalized medicine, technological advancements, a favourable funding environment, a growing number of cancer & genetic disorders, and a rising number of R&D investments. […]

Multi-Omics Data Integration SaaS Market Outlook

According to the latest research, the global Multi-Omics Data Integration SaaS market size reached USD 1.42 billion in 2024, reflecting a robust momentum driven by technological advancements and increasing adoption across life sciences. The market is expected to expand at a CAGR of 17.6% during the forecast period, with projections indicating a value of USD 6.13 billion by 2033. This remarkable growth is primarily fueled by the rising demand for integrated omics solutions in drug discovery, precision medicine, and clinical diagnostics, as organizations seek to leverage data-driven insights for improved outcomes and operational efficiencies.

A key driver behind the expansion of the Multi-Omics Data Integration SaaS market is the surging volume and complexity of biological data generated through next-generation sequencing (NGS) and high-throughput omics technologies. Researchers and clinical practitioners are increasingly reliant on advanced SaaS platforms to unify genomics, proteomics, transcriptomics, and metabolomics data for comprehensive analysis. The integration of these diverse datasets enables a holistic understanding of biological systems, facilitating breakthroughs in disease characterization, biomarker discovery, and therapeutic target identification. As the need for cross-omics data analysis intensifies, SaaS-based solutions offer scalable, flexible, and cost-effective approaches, eliminating the constraints of traditional on-premises infrastructures.

Another significant growth factor is the ongoing digital transformation in healthcare and life sciences, which has accelerated the adoption of cloud-based platforms for data management and analytics. SaaS solutions for multi-omics data integration provide seamless collaboration, secure data sharing, and real-time analytics, empowering interdisciplinary teams to drive innovation at scale. The COVID-19 pandemic further underscored the importance of rapid data integration and remote accessibility, catalyzing investments in digital infrastructure and cloud-native applications. As regulatory frameworks evolve to support data privacy and interoperability, organizations are increasingly confident in leveraging SaaS platforms for sensitive multi-omics research and clinical workflows.

The emergence of artificial intelligence (AI) and machine learning (ML) technologies is also transforming the Multi-Omics Data Integration SaaS market. By harnessing advanced algorithms, SaaS platforms can automate complex data integration, normalization, and interpretation tasks, uncovering hidden patterns and actionable insights from vast multi-omics datasets. This capability is particularly valuable in precision medicine, where individualized patient profiles require sophisticated analytics to inform diagnosis, prognosis, and treatment selection. As AI-powered multi-omics platforms become more accessible and user-friendly, their adoption is expected to proliferate across academic, clinical, and commercial settings, further propelling market growth.

From a regional perspective, North America currently dominates the global Multi-Omics Data Integration SaaS market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s advanced healthcare infrastructure, significant R&D investments, and a strong presence of leading SaaS providers. Europe and Asia Pacific are also experiencing rapid growth, driven by expanding genomics research initiatives, government funding, and increasing collaborations between academic institutions and industry stakeholders. As emerging markets in Latin America and the Middle East & Africa invest in digital health infrastructure, the global footprint of multi-omics SaaS solutions is expected to broaden, fostering greater accessibility and innovation worldwide.

Component Analysis

The Component segment of the Multi-Omics Data Integration SaaS market is bifurcated into software and services, each playing a pivotal role in enabling seamless integration and analysis of multi-omics datasets. Software solutions form the backbone of this segment, offering robust platforms for data ingestion, harmonization, visualization, and advanced analytics. These solutions are designed to handle the complexity and heterogeneity of omics data, providing researchers with intuitive interfaces and customizable workflows. The increasing sophistication of analytical tools, including AI-

The Spatial Omics Market report segments the industry into By Technology (Spatial Transcriptomics, Spatial Genomics, and more), By Product (Instruments, Consumables, and more), By Sample (Formalin-Fixed Paraffin-Embedded (FFPE), and more), By Application (Diagnostics, and more), By End User (Academic & Translational Research Institutes, and more), and Geography (North America, and more).

Global Single-cell Omics Market size is growing with a CAGR of 16.2% in the prediction period and it crosses US$ 6.64 Bn by 2032 from US$ 2.32 Bn in 2025.

In recent years, the development of high-throughput omics technology has greatly promoted the development of biomedicine. However, the poor reproducibility of omics techniques limits its application. It is necessary to use standard reference materials of complex RNAs or proteins to test and calibrate the accuracy and reproducibility of omics workflows. Our study justified a omics standard reference material and reference datasets for transcriptomic and proteomics research. This helps to further standardize the workflow and data quality of omics techniques and thus promotes the application of omics technology in precision medicine.

Cloud-Based Multi-Omics Data Warehouse Market Outlook

According to our latest research, the global market size for the Cloud-Based Multi-Omics Data Warehouse Market reached USD 2.47 billion in 2024. The market is witnessing a robust expansion, registering a CAGR of 18.2% from 2025 to 2033, and is forecasted to achieve a value of USD 12.55 billion by 2033. This remarkable growth is primarily driven by the escalating adoption of cloud technologies in life sciences and healthcare, combined with the surging demand for integrated omics data analysis to accelerate drug discovery and personalized medicine initiatives.

The rapid proliferation of high-throughput sequencing technologies and the exponential rise in multi-omics data generation are pivotal growth factors for the Cloud-Based Multi-Omics Data Warehouse Market. As research organizations and healthcare providers increasingly focus on precision medicine, the need for scalable, secure, and interoperable platforms to store, manage, and analyze diverse datasets is more critical than ever. Cloud-based solutions offer unparalleled scalability and computational power, enabling seamless integration and real-time analysis of genomics, proteomics, transcriptomics, metabolomics, and epigenomics data. This capability is essential for uncovering novel biomarkers, understanding disease mechanisms, and tailoring therapeutic interventions, thereby fueling market expansion.

Another significant driver is the growing collaboration between pharmaceutical companies, academic institutions, and technology providers to develop advanced analytics platforms. These partnerships are fostering the development of comprehensive multi-omics data warehouses that support artificial intelligence (AI) and machine learning (ML) algorithms for predictive analytics and hypothesis generation. The increasing emphasis on reducing time-to-market for new drugs and improving clinical outcomes is compelling stakeholders to invest in cloud-based multi-omics solutions. Additionally, the adoption of regulatory-compliant cloud infrastructures is mitigating concerns related to data privacy and security, further accelerating market adoption across regulated sectors such as healthcare and pharmaceuticals.

The market is also benefiting from the rising prevalence of chronic diseases and the subsequent demand for personalized healthcare solutions. Multi-omics data integration enables clinicians to make informed decisions regarding disease diagnosis, prognosis, and treatment selection. Cloud-based platforms facilitate the aggregation and harmonization of large-scale omics datasets from diverse sources, supporting translational research and clinical applications. Furthermore, advancements in data interoperability standards and API-driven architectures are enhancing the accessibility and usability of multi-omics data warehouses, making them indispensable tools for researchers and clinicians alike.

Regionally, North America continues to dominate the Cloud-Based Multi-Omics Data Warehouse Market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of leading biotechnology firms, robust healthcare infrastructure, and significant investments in genomics research are key contributors to North America's leadership. Europe is witnessing steady growth owing to supportive regulatory frameworks and increasing funding for omics research. Meanwhile, Asia Pacific is emerging as a lucrative market, driven by expanding healthcare digitization, government initiatives to promote precision medicine, and rising adoption of cloud computing in research and clinical settings.

Component Analysis

The Cloud-Based Multi-Omics Data Warehouse Market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses platforms and tools designed for data integration, management, analysis, and visualization of multi-omics datasets. These solutions are engineered to

Spatial OMICS market was valued at USD 226.32 million in 2020 and is expected to grow at a CAGR of 10.5% 2021 - 2028

All clinical variables listed were significant at a false discovery rate of 10% over the variables tested and are ordered by significance. Only the top 10 associations are displayed. For more details see Data Dictionary in S3 Table and complete results in S5 Table.

The global single-cell omics market is estimated to grow from USD 3.89 billion in 2025 to USD 17.01 billion by 2035, representing a CAGR of 15.90%.

Portal for dataset discovery across a heterogeneous, distributed group of transcriptomics, genomics, proteomics and metabolomics data resources. These resources span eight repositories in three continents and six organisations, including both open and controlled access data resources.

This dataset includes tumor multi-omics profiles used in TMO-Net research. TCGA pan-cancer multi-omics were preprocessed and pre-trained. The metabric multi-omics dataset, metastatistic tumor multi-omics dataset, PDX cell line multi-omics dataset, GDSC cell line multi-omice dataset and CPTAC cancer multi-omics dataset were used for downstream tasks and analysis.