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.

## GMarsGT: Multi-omics data analysis for rare population inference using single-cell graph transformer obtained from a mouse model for XYZ disease. The data was derived from 3 experimental groups: a control group (n=10), a disease group (n=10), and a treatment group (n=10).

## 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.

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.

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.

The mean accuracy values obtained over the 30 bootstrap iterations. Acc – is the overall accuracy, F – is the F-score, G – is the G-score. The highest values are highlighted in bold. NOTE: all the corresponding standard deviations are less than 0.02.Classification performance.

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

Analysis data for the manuscript: "Integration of meta-omics data reveals how microbial ecosystems respond to disturbance"

Files used with the repository: https://git-r3lab.uni.lu/malte.herold/laots_niche_ecology_analysis/

The archive was split into multiple parts for uploading to zenodo which need to be joined in order to extract the files:

cat resultsdir_laots.tar.gz.part_* > resultsdir_laots.tar.gz
tar xvfz resultsdir_laots.tar.gz

Version 2 contains additional files generated in the revision.

Processed PBMC data for integration tutorial in https://github.com/rpmccordlab/SMILE.

For each dataset, the number of samples, the number of features/genes after pre-processing the data, the number of classes and samples specified for each class are reported.Overview of the analyzed datasets.

Biological data analysis is the key to new discoveries in disease biology and drug discovery. The rapid proliferation of high-throughput ‘omics’ data has necessitated a need for tools and platforms that allow the researchers to combine and analyse different types of biological data and obtain biologically relevant knowledge. We had previously developed TargetMine, an integrative data analysis platform for target prioritisation and broad-based biological knowledge discovery. Here, we describe the newly modelled biological data types and the enhanced visual and analytical features of TargetMine. These enhancements have included: an enhanced coverage of gene–gene relations, small molecule metabolite to pathway mappings, an improved literature survey feature, and in silico prediction of gene functional associations such as protein–protein interactions and global gene co-expression. We have also described two usage examples on trans-omics data analysis and extraction of gene-disease associations using MeSH term descriptors. These examples have demonstrated how the newer enhancements in TargetMine have contributed to a more expansive coverage of the biological data space and can help interpret genotype–phenotype relations. TargetMine with its auxiliary toolkit is available at https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

The global omics lab service market size was valued at USD 53.34 billion in 2025 and is projected to expand at a CAGR of 6.67% from 2025 to 2033. Rising demand for personalized medicine, increasing prevalence of chronic diseases, and advancements in sequencing technologies drive market growth. Furthermore, the growing focus on precision medicine and the need to identify biomarkers for early disease diagnosis and treatment contribute to market expansion. Key market trends include the increasing adoption of next-generation sequencing (NGS) technologies, the emergence of single-cell sequencing, and the growing use of omics data in drug discovery and development. Moreover, the integration of artificial intelligence (AI) into omics analysis is expected to enhance data interpretation and improve diagnostic and therapeutic outcomes. However, the high cost of omics technologies and the need for skilled professionals may restrain market growth. Additionally, ethical considerations related to the use of omics data pose challenges that need to be addressed. Key drivers for this market are: Increasing demand for personalized medicine, Advancements in genomics and proteomics; Growth in biomarker discovery programs; Rising prevalence of chronic diseases; Expanding applications in agriculture and environmental sciences. Potential restraints include: Rising demand for personalized medicine, Advancements in genomic technologies; Increasing prevalence of chronic diseases; Growth in research funding and partnerships; Integration of AI in omics analysis.

This data characterizes gene expression levels in THP-1 macrophages. The data was generated using RNA sequencing and analyzed with DeSeq2 (version 1.24.0). The analysis included raw count data and normalized count matrices obtained from DESeq2's dds_deseq objects.This data describes the methylation levels of individual CpG sites in THP-1 macrophages. The data was obtained using the Infinium MethylationEPIC v2.0 Kit (Illumina) and analyzed with the minfi package (version 1.46). Specifically, the data underwent quantile normalization using the preprocessQuantile function within minfi. Only CpG sites with a detection p-value less than 0.05 were included to obtain MatrixProcessedGEO.txt file. The beta values (bValues.xlsx) were obtained using the function “getBeta” from the same package, considering each time point individually.The macrophages were exposed to phorbol 12-myristate 13-acetate (PMA) for 48 hours, followed by treatment with either a combination of LPS (10 pg/ml) and interferon-gamma (IFNγ) (20 ng/ml) or a combination of interleukins 13 (IL-13) (20 ng/ml) and 4 (IL-4) (20 ng/ml) for 24, 48, and 72 hours.

Market Overview: The global single-cell omics market is burgeoning, with a market size of USD xx million in 2025 and a projected CAGR of xx% over the forecast period (2025-2033). Increasing government funding for genomics research, advancements in sequencing technologies, and rising demand for personalized medicine are the primary drivers of this market. However, factors such as high instrumentation costs and data analysis challenges may pose restraints. Geographically, North America dominates the market, followed by Europe and Asia-Pacific. Market Segmentation and Key Players: The single-cell omics market is segmented by application (drug discovery, disease diagnostics, biomarker discovery, and others) and type (single-cell RNA sequencing, single-cell proteomics, and single-cell genomics). Leading companies in the market include ANGLE Plc, BD, Bio-Rad Laboratories, Inc., Biognosys, CELLENION, CYTENA GmbH, Danaher Corporation, Illumina, Inc., Mission Bio, PerkinElmer Inc., Standard BioTools Inc., Vizgen, and 10x Genomics. These companies offer innovative solutions for single-cell analysis, contributing to the market's growth and expansion.

Single cell multiomics analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients

BD AbSeq allows simultaneous measurement of protein and RNA expression at the single-cell level, in combination with the BD Rhapsody high-throughput single-cell capture system. Cells were captured using D Rhapsody cell capture beads. Identity of proteins and mRNA was carried out as per protocol (see manuscript materials & method). We manually gated ILC1, ILC2 and ILCp single cells using FlowJo, enumerated the mRNA count in each cell.

More Information about BD rhapsody: https://www.bdbiosciences.com/content/dam/bdb/marketing-documents/BD-AbSeq-BD-Rhapsody-Simultaneous-mRNA-and-Protein-Quantification-DS.pdf

Description of the data and file structure

The processed dataset is available in Zip file (ReadCount.zip). The ...

Given the limited knowledge of the biological impact of spaceflight a multi-omics systems biology approach was used to investigate NASA xe2 x80 x99s GeneLab data and astronaut biomedical profiles. These data consist of hundreds of samples flown in space human metrics from 59 astronauts and confirmatory data from NASA xe2 x80 x99s Twin Study analyzed together for consistent transcriptomic proteomic metabolomic and epigenetic response to spaceflight. Pathway analysis showed significant enrichment of mitochondrial activity and innate immunity. Muscle and liver tissues showed that chronic inflammation may be a response to mitochondrial dysfunction. Additional pathways altered in spaceflight included cell cycle circadian rhythm and olfactory activity pathways all of which are known to have interactions with mitochondrial activity. Evidence of altered mitochondrial function was also found in the urine and blood metabolic data compiled from the astronaut cohort and NASA Twin Study data all of which indicate mitochondrial stress as a consistent phenotype of spaceflight.

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection.

The size of the spatial omics market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of XXX% during the forecast period. The significant growth of the spatial omics market is attributed to the increasing demand for spatial information in various biological research applications, advancements in spatial omics technologies, rising investments in research and development, and growing collaborations and partnerships among industry players. The market is also driven by the increasing adoption of spatial omics techniques in various fields, including drug discovery, cancer research, and personalized medicine. Recent developments include: In February 2024, DNAnexus, Inc., collaborated with Curio Bioscience to streamline and simplify data analysis for high-resolution, whole transcriptome spatial mapping studies, allowing Curio Bioscience to utilize the DNAnexus Precision Health Data Cloud and its intuitive analysis environment., In June 2023, OMAPiX entered into a strategic partnership with spatial biology platform providers Vizgen, Inc., Ultivue, Inc., and Resolve Biosciences to accelerate life science research and drug development..

The AVATARS project aims to enhance crop improvement by utilizing advanced molecular omics analyses and high-throughput phenotyping. In order to address the challenges posed by the increasing volume and complexity of data, novel analysis tools and approaches are being developed. This interdisciplinary project focuses on the seed formation of Brassica napus, also known as rapeseed or canola, and investigates genetic and environmental influences on seed quality using ultra-high-throughput single seed analyses and deep learning methods. As an integral part of the project, plants of the German winter-type oilseed rape cultivar Express 617 were grown under controlled field-like conditions in the IPK PhenoSphere (2020-2021). Samples were taken at five distinct stages of seed development, from pre-storage to seed maturation, for temporally and spatially resolved multi-omics analysis. The present dataset includes gene expression data obtained by mRNA sequencing and mass spectrometry-based proteomics data. For the first stage (pre-storage), whole seeds were analysed. In the later four stages, developing seeds were dissected into four organs/tissues (SC = seed coat, IC = inner cotyledon, OC = outer cotyledon, and RA = radicle). By employing virtual and augmented reality, researchers can visualize and explore multidimensional data in innovative ways, making them more accessible and enabling more efficient analyses. The project involves academic and industrial partners with expertise in various fields, collaborating to tackle these challenges and integrate data from multiple sources. By developing a time-resolved 3D seed model and utilizing advanced technologies, such as high-resolution MRI and histological data, the project will map complex omics datasets onto gene regulatory and metabolic networks for detailed analyses and intuitive visualization. The acquired datasets were deposited in central infrastructure of EMBL-EBI (ArrayExpress and PRIDE), facilitating efficient access and integration with other large datasets.

The Spatial OMICS product landscape encompasses instruments, consumables, and workflow solutions. Instruments continue to hold the largest market share, while consumables and workflow solutions are also witnessing notable growth. Recent developments include: January 2022:Under a multi-year agreement, Illumina Inc. and Nashville Biosciences LLC, a wholly owned subsidiary of Vanderbilt University Medical Center (VUMC), have collaborated to utilize Illumina's next-generation sequencing (NGS) platforms. Their objective is to create a top-tier clinical genomic resource and expedite the progress of novel medications., May 2022:NanoString unveiled a seamless, cloud-based workflow aimed at enhancing the spatial data analysis process for users of Illumina NextSeq 1000 and NextSeq 2000 sequencing systems, along with the GeoMx Digital Spatial Profiler. This initiative aims to streamline spatial biology research utilizing next-generation sequencing technologies.. Notable trends are: Rising demand for spatially resolved molecular profiling is driving the market growth.

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.