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.

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.

## 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.

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

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.

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.

Acetyl-CoA is a key metabolite in all organisms, implicated in transcriptional regulation, post-translational modification as well as fuelling the TCA-cycle and the synthesis and elongation of fatty acids (FAs). The obligate intracellular parasite Toxoplasma gondii possesses two enzymes which produce acetyl-CoA in the cytosol and nucleus: acetyl-CoA synthetase (ACS) and ATP-citrate lyase (ACL), while the branched-chain α-keto acid dehydrogenase-complex (BCKDH) generates acetyl-CoA in the mitochondrion. To obtain a global and integrative picture of the role of distinct sub-cellular acetyl-CoA pools, we measured the acetylome, transcriptome, proteome and metabolome of parasites lacking ACL/ACS or BCKDH. Loss of ACL/ACS results in the hypo-acetylation of nucleo-cytosolic and secretory proteins, alters gene expression broadly and is required for the synthesis of parasite-specific FAs. In contrast, loss of BCKDH causes few specific changes in the acetylome, transcriptome and proteome which allow these parasites to rewire their metabolism to adapt to the obstruction of the TCA-cycle.

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

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.

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 ...

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.

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.

Data deposition of the article 'Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence'

This contains the data for the manuscript Feugere et al., "Heat induces multiomic and phenotypic stress propagation in zebrafish embryos" (2023). Zebrafish embryos were exposed to thermal stress ("TS") and stress metabolites ("SM") released by heat-stressed conspecifics in a two-way factorial design ("TSxSM"). The folder includes raw molecular data (cortisol levels, HSP70 protein levels, and gene expression acquired with LAMP and RNA-seq) and raw phenotypic data (morphology, hatching, survival, and behaviour) of zebrafish Danio rerio at 1 day and 4 days of development.

The .csv files contain all quantitative data, whilst the .tab files contain the gene count data required for gene expression analysis. The data were analysed in R using the code shared in the "TSxSM2.stats.Rmd" file. The "Metadata" document provides the reader with an extensive description of each file.

All code is based on the dada2 pipeline in R, which uses text-based files.

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Raw data required by Cell Reports data archiving and data transparency initiative

This repository is a part of the supplementary document in Park et al., "Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis" published in iScience 2022.

Abstract: To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e., healthy, gastritis, cancer). We uncover associations specific to disease states. For example, uniquely in gastritis, Helicobacteraceae is highly correlated with the expression of FAM3D, which has been previously implicated in gastrointestinal inflammation. Additionally, in gastric cancer but not in adjacent gastritis, Lachnospiraceae is highly correlated with the expression of UBD, which regulates mitosis and cell cycle time. Furthermore, lower abundances of B cells in gastric cancer compared to gastritis may suggest a previously unidentified immune evasion process in gastric carcinogenesis. Our integrative analysis provides the most comprehensive description of microbial, host transcriptomic, and immune cell factors of the gastric carcinogenesis pathway.