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.

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.

The Paired Omics Data Platform is a community-based initiative standardizing links between genomic and metabolomics data in a computer readable format to further the field of natural products discovery. The goals are to link molecules to their producers, find large scale genome-metabolome associations, use genomic data to assist in structural elucidation of molecules, and provide a centralized database for paired datasets. This dataset contains the projects in http://pairedomicsdata.bioinformatics.nl/. The JSON documents adhere to the http://pairedomicsdata.bioinformatics.nl/schema.json JSON schema.

As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO (http://bioinformatics.cau.edu.cn/AppleMDO/) was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

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.

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

Mangroves are dominant flora of intertidal zones along tropical and subtropical coastline around the world that offer important ecological and economic value. Recently, the genomes of mangroves have been decoded, and massive omics data were generated and deposited in the public databases. Reanalysis of multi-omics data can provide new biological insights excluded in the original studies. However, the requirements for computational resource and lack of bioinformatics skill for experimental researchers limit the effective use of the original data. To fill this gap, we uniformly processed 942 transcriptome data, 386 whole-genome sequencing data, and provided 13 reference genomes and 40 reference transcriptomes for 53 mangroves. Finally, we built an interactive web-based database platform MangroveDB (https://github.com/Jasonxu0109/MangroveDB), which was designed to provide comprehensive gene expression datasets to facilitate their exploration and equipped with several online analysis tools, including principal components analysis, differential gene expression analysis, tissue-specific gene expression analysis, GO and KEGG enrichment analysis. MangroveDB not only provides query functions about genes annotation, but also supports some useful visualization functions for analysis results, such as volcano plot, heatmap, dotplot, PCA plot, bubble plot, population structure etc. In conclusion, MangroveDB is a valuable resource for the mangroves research community to efficiently use the massive public omics datasets.

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.

## 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.

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.

omics metadata project data: Nanosafety-relevant omics data - a database covering metadata for transcriptomics, proteomics and microRNA expression data relevant to safety assessment analyses of nanomaterials

Datesets used in "Finding the best cell lines across pan-cancer to use in pre-clinical research as a proxy for patient tumor samples considering immune cells, multi-omics, and cancer pathways". These datasets include pre-processed multi-omics, such as gene expression, DNA methylation, copy number aberration from 22 different cancer types from TCGA and CCLE database along with the drug response data, reference methylation profiles of immune cells, datasets for evaluations and the results from CTDPathSim2.0 software to create the figures and tables in the paper." Currently, you have this statement: "Multi-omics datasets used in CTDPathSimv2.0 software. These datasets include gene expression, DNA methylation, copy number aberration from 22 different cancer types from TCGA database".

Single-cell RNA-seq, single-cell ATAC-seq, and genotypes used in the analysis for the study "Altered and allele-specific open chromatin landscape reveal epigenetic and genetic regulators of innate immunity in COVID-19". The RNA-seq and ATAC-seq are raw data in FASTQ format while the genotypes are in the VCF format which was filtered and imputed (more details are available in the main text of the study).

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

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)

This database contain protein sequences of aquatic microbial eukaryotes, or protists. The purpose of this is to make a database that is of reasonable quality to serve as resource for both taxonomy and functional interpretation of metagenomic and metatranscriptomic studies of protists. The source of the sequences were mainly from Marine Microbial Eukaryotes Transcriptome Sequencing Project (MMETSP), and supplemented with various genomes and transcriptomes of organisms that were not a part of MMETSP.

To use this database, one has to understand the main function of the three files here.

(1) The protein sequences are stored in .faa file. You can build an alignment/search database out of that and search your meta-omics sequences against it. Each sequence in the FASTA file has an ID which always consists of two parts like this: "MMETSP0004_1234567". The text before the first underscore is the source ID of that sequence.

(2) Taxonomy information of each source ID are stored in "EukZoo_taxonomy_table_v_0.2.tsv". One can use the information within in conjunction with database search results to assign taxonomy to sequences.

(3) KEGG annotation of each sequence are stored in "EukZoo_KEGG_annotation_v_0.2.tsv". One can use the information within in conjunction with database search results to assign KEGG functional annotation (KO ID) to sequences.

I also provide scripts to assign taxonomy and KEGG annotation from database search results. You can also find the scripts and explanations on how to use them on the EukZoo GitHub page. You will find details on how the database was created and curated on there as well.

Please contact me at zhenfeng.liu1@gmail.com if you have any questions or requests. Thank you for your interest in EukZoo.

This FAIRsharing record describes: NODE (The National Omics Data Encyclopedia) provides an integrated, compatible, comparable, and scalable multi-omics resource platform that supports flexible data management and effective data release. NODE uses a hierarchical data architecture to support storage of muti-omics data including sequencing data, MS based proteomics data, MS or NMR based metabolomics data, and fluorescence imaging data. Launched in early 2017, NODE has collected and published over 900 terabytes of omics data for researchers from China and all over the world in last three years, 22% of which contains multiple omics data. NODE provides functions around the whole life cycle of omics data, from data archive, data requests/responses to data sharing, data analysis, data review and publish.

Additional file 2. Significant biomarkers for each disease. Excel spreadsheet with the significant biomarkers found in the use case 2 for each disease, including the mean log2 FC between case and control samples for each gene.

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

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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