Additional file 2. Example of interactive plot generated using chromLinks: Normalized expression of the top 50 highly expressed orthologous genes in mouse (Mus musculus) and rat (Rattus norvegicus) during pluripotent cell determination (NCBI Gene Expression Omnibus id: GSE42081) [16]. Orthologous gene pairs are connected with colored links. Each orthologous pair is indicated by a different link color.

Additional file 1. Example of chromoMap interactive plot constructed using various features of chromoMap including polyploidy (used as multi-track), feature-associated data visualization (scatter and bar plots), chromosome heatmaps, data filters (color-coded scatter and bars). Differential gene expression in a cohort of patients positive for COVID19 and healthy individuals (NCBI Gene Expression Omnibus id: GSE162835) [12]. Each set of five tracks labeled with the same chromosome ID (e.g. 1-22, X & Y) contains the following information: From top to bottom: (1) number of differentially expressed genes (DEGs) (FDR < 0.05) (bars over the chromosome depictions) per genomic window (green boxes within the chromosome). Windows containing ≥ 5 DEGs are shown in yellow. (2) DEGs (FDR < 0.05) between healthy individuals and patients positive for COVID19 visualized as a scatterplot above the chromosome depiction (genes with logFC ≥ 2 or logFC ≤ −2 are highlighted in orange). Dots above the grey dashed line represent upregulated genes in COVID19 positive patients. Heatmap within chromosome depictions indicates the average LogFC value per window. (3–4) Normalized expression of differentially expressed genes (scatterplot) and of each genomic window containing DEG (green scale heatmap) in (3) patients with severe/critical outcomes and (4) asymptomatic/mild outcome patients. (5) logFC of DEGs between healthy individuals and patients positive for COVID19 visualized as scatter plot color-coded based on the metabolic pathway each DEG belongs to.

Exponential growth of high-throughput data and the increasing complexity of omics information have been making processing and interpreting biological data an extremely difficult and daunting task. Here we developed FuncTree (http://bioviz.tokyo/functree), a web-based application for analyzing and visualizing large-scale omics data, including but not limited to genomic, metagenomic, and transcriptomic data. FuncTree allows user to map their omics data onto the “Functional Tree map”, a predefined circular dendrogram, which represents the hierarchical relationship of all known biological functions defined in the KEGG database. This novel visualization method allows user to overview the broad functionality of their data, thus allowing a more accurate and comprehensive understanding of the omics information. FuncTree provides extensive customization and calculation methods to not only allow user to directly map their omics data to identify the functionality of their data, but also to compute statistically enriched functions by comparing it to other predefined omics data. We have validated FuncTree’s analysis and visualization capability by mapping pan-genomic data of three different types of bacterial genera, metagenomic data of the human gut, and transcriptomic data of two different types of human cell expression. All three mapping strongly confirms FuncTree’s capability to analyze and visually represent key functional feature of the omics data. We believe that FuncTree’s capability to conduct various functional calculations and visualizing the result into a holistic overview of biological function, would make it an integral analysis/visualization tool for extensive omics base research.

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.

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

Datasets for protein, mRNA, and microRNAThe file contains four datasets. Three with differentially expressed protein, mRNA, and microRNA, respectively. The last dataset contain data on all proteins detected in all TMT sets. Information about the sample names is included in the readme file.Datasets.xlsx

All datasets that are created or used in the publication introducing Bonsai tree-representations: "Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data".

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-

Supplementary text, figures, and data files. All text and materials were formated as a small self-contained website (1 HTML file with necessary figures and data files). Data files include input and result files of the case study including the fold change of expression values between tumor vs. normal conditions (in log2FC), average expression value of each gene in 4 GBM subtypes, GBM-altered subnetworks (nodes and edges) weighted by expression correlations between each pair of genes, and gene sets in 2 critical modules and their functional annotations. (ZIP 4315kb)

Biological Data Visualization Market Outlook

According to our latest research, the biological data visualization market size reached USD 1.87 billion globally in 2024, fueled by the increasing complexity and scale of biological datasets in genomics, proteomics, and other life sciences domains. The market is projected to grow at a CAGR of 12.6% from 2025 to 2033, with the forecasted market size expected to reach USD 5.45 billion by 2033. This robust growth is primarily driven by the expanding need for advanced visualization tools to interpret multidimensional biological data, coupled with the surge in research and development activities across the pharmaceutical, biotechnology, and healthcare sectors.

One of the primary growth factors propelling the biological data visualization market is the exponential increase in biological data generated from next-generation sequencing, single-cell analysis, and high-throughput screening technologies. These advances have enabled researchers to generate vast and complex datasets that require sophisticated visualization platforms for meaningful interpretation. As the volume of genomics, proteomics, and metabolomics data continues to surge, the demand for intuitive and interactive visualization solutions is becoming indispensable. This trend is further amplified by the adoption of multi-omics approaches in biomedical research, where integrating and visualizing data from multiple sources is crucial for uncovering novel biological insights.

Another significant driver is the growing emphasis on personalized medicine and precision healthcare, which relies heavily on the effective analysis and visualization of biological data. Healthcare providers, pharmaceutical companies, and research institutions are increasingly leveraging biological data visualization tools to identify biomarkers, understand disease mechanisms, and tailor therapeutic interventions. The integration of artificial intelligence and machine learning algorithms with visualization platforms is further enhancing the ability to detect patterns, predict outcomes, and accelerate drug discovery. This technological synergy is expected to play a pivotal role in shaping the future trajectory of the biological data visualization market.

Additionally, the increasing collaboration between academia, industry, and government agencies is fostering innovation in biological data visualization. Funding initiatives and public-private partnerships are supporting the development of next-generation visualization tools that cater to the evolving needs of the life sciences community. The rise of cloud-based platforms and the democratization of computational resources are making advanced visualization technologies accessible to a broader audience, including small and medium-sized enterprises and research organizations with limited IT infrastructure. These collaborative efforts are creating a fertile environment for the continuous evolution of the biological data visualization market.

From a regional perspective, North America continues to dominate the global biological data visualization market, driven by the presence of leading biotechnology firms, well-established research infrastructure, and significant investments in genomics and precision medicine. Europe follows closely, benefiting from strong government support and a vibrant academic ecosystem. The Asia Pacific region is emerging as a lucrative market, propelled by rising healthcare expenditure, increasing adoption of advanced technologies, and growing investments in life sciences research. Latin America and the Middle East & Africa, while smaller in market size, are witnessing steady growth due to improving healthcare infrastructure and a growing focus on biomedical research.

Product Type Analysis

The biological data visualization market is segmented by product type into software, services, and platforms, each playing a pivotal role in the overall ecosystem. Software solutions constitute the largest share of th

• 13 parameter files (*.YML) used by the 3DGB workflow to produce models of 3D genomes.
• 13 3D genomes structures (*.PDB).
• 4 animated GIF of representative structures.
• 1 XLSX file that lists raw (Hi-C and ChIP-seq) data used in this study and the associated analysis.

Configuration files (*.yml) used by the 3DGB workflow to produce models of 3D genomes and resulting structures (*.pdb) presented in the paper "Visual integration of omics data : 3D modeling of Hi-C contacts to see the spatial organization of fungal chromosomes". Animated GIF of 4 structures are also available.

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.

Snapshot img

Spatial Genomics And Transcriptomics Market Size 2025-2029

The spatial genomics and transcriptomics market size is forecast to increase by USD 732.3 million, at a CAGR of 12% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of spatial genomics in drug discovery. This innovative approach allows for a more precise understanding of the spatial organization of cells, enabling the identification of new targets and biomarkers for disease diagnosis and treatment. Furthermore, the use of spatial omics is gaining traction in biomarker identification, offering potential for personalized medicine and improved patient outcomes and in therapeutic areas like neurological disorders, infectious diseases, neuroscience, immunology, genomics, and proteomics. However, the market faces challenges, including the lack of workforce expertise in spatial genomics. As this field continues to evolve, there is a pressing need for skilled professionals to drive research and development efforts.
Companies seeking to capitalize on the opportunities in this market must invest in workforce development and collaborate with academic institutions and industry partners to build a strong foundation for future success. The ability to navigate these challenges and harness the power of spatial genomics will be crucial for companies looking to gain a competitive edge in the life sciences industry.

What will be the Size of the Spatial Genomics And Transcriptomics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, driven by advancements in technologies and applications across various sectors. Cell signaling, confocal microscopy, RNA extraction, and sample preparation are integral components of this dynamic landscape. Ethical considerations are increasingly becoming a focus, as the use of high-throughput sequencing and data visualization tools uncovers new insights into genomic data. In situ sequencing and software solutions facilitate pathway analysis and data integration, enabling a more comprehensive understanding of biological processes. RNA extraction and sample preparation techniques play a crucial role in the market, ensuring accurate and reliable data. High-throughput sequencing technologies, such as next-generation sequencing (NGS), have revolutionized genome editing and disease modeling by providing vast amounts of genomic data.

Data repositories and machine learning algorithms facilitate data interpretation and gene regulatory network analysis. The continuous unfolding of market activities includes the development of spatial transcriptomics platforms, which offer three-dimensional genome organization insights. Microfluidic devices and protein-DNA interactions are also gaining attention, as they enable precise manipulation of biological samples. Quantitative PCR (qPCR) and chromatin conformation capture techniques complement these advancements, providing additional layers of information. The integration of various technologies, such as microarray technology, fluorescence microscopy, and data visualization tools, offers a more holistic approach to understanding complex biological systems. Spatial genomics and transcriptomics applications extend to drug discovery and gene expression analysis, providing valuable insights into cellular processes and biological pathways.

In conclusion, the market is characterized by continuous innovation and evolving patterns. The integration of various technologies, including cell signaling, confocal microscopy, RNA extraction, sample preparation, ethical considerations, high-throughput sequencing, data visualization, in situ sequencing, software solutions, pathway analysis, data integration, microfluidic devices, protein-DNA interactions, next-generation sequencing, gene regulatory networks, and more, offers a more comprehensive understanding of biological systems. This knowledge drives progress in personalized medicine, biomarker discovery, genome editing, disease modeling, and other sectors.

How is this Spatial Genomics And Transcriptomics Industry segmented?

The spatial genomics and transcriptomics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.ProductConsumablesInstrumentsEnd-userTranslational researchAcademic customersDiagnostic customersPharmaceutical manufacturerApplicationDrug Discovery & DevelopmentDisease Research (Oncology, Neuroscience)Biomarker IdentificationTechniqueSpatial Transcriptomics (e.g., Visium, MERFISH)Spatial GenomicsProteomics (Spatial Proteomics)GeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and Afr

The size of the Bioinformatics Platforms Market market was valued at USD 16.36 Million in 2023 and is projected to reach USD 27.93 Million by 2032, with an expected CAGR of 7.94% during the forecast period. The Bioinformatics Platforms Market includes the software and tools required to understand biological data that contain genomic, proteomic, or metabolic data. These platforms include support for various applications like drug discovery, individualized medicine, and clinically related diagnostics through helps of data integration, statistical analysis and visualization. Some of the emerging trends that are driving the bioinformatics market are cloud-based bioinformatics solutions to support scalability and collaboration, advanced machine learning and artificial intelligence (AI) technologies to accurately analyze raised significance of multi-omics data integration for profound tumor bioinformatics analysis. Such factors pulling the market ahead include increasing volume of biological data in facets like research and clinical trials, evolving sequencing technologies, along with the increasing requirement for enhanced data management and analysis in genomics and proteomics. Further, the rising usage of bioinformatics for customized treatment and the growing number of research studies in genomics complement the market’s growth. Recent developments include: In June 2022, California's biotechnology research startup LatchBio launched an end-to-end bioinformatics platform for handling big biotech data to accelerate scientific discovery., In March 2022, ARUP launched Rio, a bioinformatics pipeline and analytics platform for better, faster next-generation sequencing test results.. Key drivers for this market are: Increasing Demand for Nucleic Acid and Protein Sequencing, Increasing Initiatives from Governments and Private Organizations; Accelerating Growth of Proteomics and Genomics; Increasing Research on Molecular Biology and Drug Discovery. Potential restraints include: Lack of Well-defined Standards and Common Data Formats for Integration of Data, Data Complexity Concerns and Lack of User-friendly Tools. Notable trends are: Sequence Analysis Platform Segment is Expected Hold a Significant Share Over the Forecast Period.

Here, we summarise available data and source code regarding the publication "Spatially Resolved Transcriptomics Mining in 3D and Virtual Reality Environments with VR-Omics". Abstract Spatially resolved transcriptomics (SRT) technologies produce complex, multi-dimensional data sets of gene expression information that can be obtained at subcellular spatial resolution. While several computational tools are available to process and analyse SRT data, no platforms facilitate the visualisation and interaction with SRT data in an immersive manner. Here we present VR-Omics, a computational platform that supports the analysis, visualisation, exploration, and interpretation SRT data compatible with any SRT technology. VR-Omics is the first tool capable of analysing and visualising data generated by multiple SRT platforms in both 2D desktop and virtual reality environments. It incorporates an in-built workflow to automatically pre-process and spatially mine the data within a user-friendly graphical user interface. Benchmarking VR-Omics against other comparable software demonstrates its seamless end-to-end analysis of SRT data, hence making SRT data processing and mining universally accessible. VR-Omics is an open-source software freely available at: https://ramialison-lab.github.io/pages/vromics.html or below.

A tool for mapping transcriptome data and for creating a database with an overview of the entire pathway, a web-based resource consisting of a web-application for the visualization of complex omics data onto KEGG pathways to overview all entities in the context of cellular pathways, and databases created with the software to visualize a series of microarray data. The web-application accepts transcriptome, proteome, metabolome, or the combination of these data as input, and because of this scalability it is advantageous for the visualization of cell simulation results. Several databases of transcriptome data obtained at Mori Laboratory, Nara Institute of Science and Technology, Japan, are also presented.

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work.

This Zenodo repository makes the processed data and the results of our INFIMA paper available.

Our paper presents a method called INFIMA for Integrative Fine-Mapping with Model Organism Multi-Omics Data. INFIMA capitalizes on multi-omics data modalities such as chromatin accessibility and transcriptome from the eight Diversity Outbred (DO) mice founder strains to fine-map DO islet eQTL. In addition, INFIMA employs footprinting and in silico mutation analysis to reveal regulatory genetic variants that mediate strain-specific expression differences.

The source code for reproducing the data can be found at Github. Please also check our Shiny app for data visualization!

These tissue-level multi-omic graphical analysis reports are provided as additional data for the manuscript “Temporal dynamics of the multi-omic response to endurance exercise training across tissues” (MoTrPAC Study Group, bioRxiv, 2022). Find the preprint here. Extensive background is included in each report. Briefly, we used a graphical clustering approach to define and visualize the temporal dynamics of molecular analytes regulated by endurance exercise training at multiple training time points in male and female rats across many data types ("omes") and tissues. The objective of these multi-omic reports is to share representations of >34,000 training-regulated molecular features in interactive HTML reports that allow researchers to extract meaningful biology from a complex dataset. Each report presents a summary of the significantly training-regulated features across omes in a specific tissue and the corresponding graphical analysis results, as well as features and pathway enrichment results corresponding to the largest graphical clusters (nodes, edges, and paths) for that tissue. A graphical cluster is a group of training-regulated features that share temporal behavior at some point during the training time course. These multi-omic reports are generated using data and functions available through the MotrpacRatTraining6mo R package. Install this R package to explore the data yourself! Get started with this tutorial. {"references": ["Ignatiadis N, Klaus B, Zaugg JB, Huber W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods. 2016 Jul;13(7):577-80. doi: 10.1038/nmeth.3885. Epub 2016 May 30. PMID: 27240256; PMCID: PMC4930141.", "Heller R, Yaacoby S, Yekutieli D. repfdr: a tool for replicability analysis for genome-wide association studies. Bioinformatics. 2014 Oct 15;30(20):2971-2. doi: 10.1093/bioinformatics/btu434. Epub 2014 Jul 9. PMID: 25012182.", "Almende B.V. and Contributors, Thieurmel B (2022). visNetwork: Network Visualization using 'vis.js' Library. R package version 2.1.2, https://CRAN.R-project.org/package=visNetwork.", "Gay N, Amar D, Jean Beltran P, MoTrPAC Study Group (2022). MotrpacRatTraining6mo: Analysis of the MoTrPAC endurance exercise training data in 6-month-old rats. R package version 1.5.2, https://motrpac.github.io/MotrpacRatTraining6mo/."]}