WebSEQ: A New Tool for Democratizing Omics Data Sharing.

BackgroundThrough the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.Aim of reviewTo understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.Key scientific concepts of reviewWe observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

The advent of high-throughput measurement technologies has resulted in the rapid accumulation of "omics" information including genome, transcriptome, proteome, and metabolome data. This increase in data acquisition has lead to a demand for an efficient computational platform for in silico analysis. The G-language software suite provides a comprehensive workbench for large-scale omics research and systems biology. The suite includes a bioinformatics research framework G-language Genome Analysis Environment, which contains a Gene Prediction Accuracy Classification benchmarking tool for the quantification of the sensitivity of genome informatics analysis methods to genome annotation completeness. Omics data processed in this environment can be visualized with KEGG-based pathway mapping web service, and Genome-based Modeling System enables automatic prototyping of metabolic pathway models from the genome. The software suite covers various domains of omics, with the goal of integrating all of these data for research into systems biology.

The rapid growth of multi-omics datasets, in addition to the wealth of existing biological prior knowledge, necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying disease-related molecular markers. We propose a framework for supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the relationships among features from high-dimensional 'omics data and set transformers to integrate low-dimensional representations of 'omics features. Furthermore, our framework incorporates explainability methods to elucidate important biomarkers and extract interaction relationships between biological quantities of interest. We demonstrate the effectiveness of our approach by applying it to Alzheimer's disease (AD) multi-omics data from the ROSMAP cohort, showing that the integration of transcriptomics and proteomics data with AD biological domain network priors improves the prediction accuracy of AD status and highlights functional AD biomarkers.

Constraint-based modeling can mechanistically simulate the behavior of a biochemical system, permitting hypothesis generation, experimental design and interpretation of experimental data, with numerous applications, especially the modeling of metabolism. Given a generic model, several methods have been developed to extract a context-specific, genome-scale metabolic model by incorporating information used to identify metabolic processes and gene activities in each context. However, the existing model extraction algorithms are unable to ensure that a context-specific model is thermodynamically flux consistent. Here we introduce XomicsToModel, a semiautomated pipeline that integrates bibliomic, transcriptomic, proteomic and metabolomic data with a generic genome-scale metabolic reconstruction, or model, to extract a context-specific, genome-scale metabolic model that is stoichiometrically, thermodynamically and flux consistent. One of the key advantages of the XomicsToModel pipeline is its ability to seamlessly incorporate omics data into metabolic reconstructions, ensuring not only mechanistic accuracy but also physicochemical consistency. This functionality enables more accurate metabolic simulations and predictions across different biological contexts, enhancing its utility in diverse research fields, including systems biology, drug development and personalized medicine. The XomicsToModel pipeline is exemplified for extraction of a specific metabolic model from a generic metabolic model; it enables omics data integration and extraction of physicochemically consistent mechanistic models from any generic biochemical network. It can be implemented by anyone who has basic MATLAB programming skills and the fundamentals of constraint-based modeling.

Taking a byte out of big data

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction.

BackgroundThere has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others.MethodsTo fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes.ResultsEvaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle’s predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations.

To facilitate identification of therapeutic strategies targeting pediatric brain tumors, the Children’s Hospital of Philadelphia Center for Data Driven Discovery in Biomedicine (D3b) and Children’s Brain Tumor Network (CBTN), in partnership with foundations, academic, and governmental institutions, are leaders in developing open science models for specimen collection, data generation, and resource sharing. Here we highlight initiatives to generate multimodal proteomics and metabolomics data characterizing over 1000 events from 11 pediatric, adolescent, and young adult brain tumor histologies, along with additional derived preclinical model cell lines. The data comprise combinations of whole cell proteomics, phosphoproteomics, ubiquitin proteomics, glycosylation, acetylation, and metabolomics to complement existing WGS, RNA-Seq, methylation, miRNA-Seq, snRNA-Seq, clinical, and imaging data. Integration of proteomics with genomics and transcriptomics modalities provides evidence for functional or phenotypic manifestations of genomic events, thereby identifying potential therapeutic targets and/or biomarkers. Proteomics and metabolomics data also provide mechanistic insights by measuring changes in protein abundance or function, characterizing signaling networks, and quantifying metabolic activity impacting oncogenesis in tumor cells. We are developing architectures for processing, integrating, and disseminating these data. We are addressing technical challenges associated with harmonizing data from diverse proteomics experimental strategies and mass spectrometry methods. We will develop standards for proteomics data products and harmonization with other modalities. Finally, we will establish pipelines for upstream analysis/re-analysis of proteomics data using custom parameters. The pipelines, raw, and processed data will be deposited within D3b Cavatica, OpenPedCan, and Kids First infrastructures, and be indexed and accessible across platforms. Our efforts will enable open use and interpretation of these unique data modalities, and thus promote engagement within the wider community to advance basic and translational pediatric brain tumor research. One exciting application of our infrastructure will be to enable tumor-specific proteome identification, which has the potential to inform precision-based, personalized medicine approaches.

Due to the complex pathogenesis, the molecular mechanism of nonalcoholic steatohepatitis (NASH) remains unclear. In this study, we aimed to reveal the comprehensive metabolic and signaling pathways in the occurrence of NASH. C57BL/6 mice were treated with high-fat diet for 4 months to mimic the NASH phenotype. After the treatment, the physiochemical parameters were evaluated, and the liver tissues were prepared for untargeted metabolomic analysis with ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Then, three relevant Gene Expression Omnibus (GEO) datasets were selected for integrative analysis of differentiated messenger RNA and metabolites. The levels of phosphatidylethanolamine (PE) (16:1(9Z)/20:4(5Z,8Z,11Z,14Z)), oleic acid, and sphingomyelin (SM) (d18:0/12:0) were significantly increased, and the content of adenosine was severely reduced in NASH mice. The integrated interpretation of transcriptomic and metabolomic data indicated that the glycerophospholipid metabolism and necroptosis signaling were evidently affected in the development of NASH. The high level of SM (d18:0/12:0) may be related to the expression of acid sphingomyelinase (ASMase), and the elevated arachidonic acid was coordinated with the upregulation of cytosol phospholipase A2 (cPLA2) in the necroptosis pathway. In summary, the inflammatory response, necroptosis, and glycerophospholipid may serve as potential targets for mechanistic exploration and clinical practice in the treatment of NASH.

Class prediction using “omics” data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request.

BackgroundMethamphetamine (METH), an abused illicit drug, disrupts many cellularprocesses, including energy metabolism, spermatogenesis, and maintenance ofoxidative status. However, many components of the molecular underpinnings ofMETH toxicity have yet to be established. Network analyses of integratedproteomic, transcriptomic and metabolomic data are particularly well suitedfor identifying cellular responses to toxins, such as METH, which mightotherwise be obscured by the numerous and dynamic changes that areinduced.Methodology/ResultsWe used network analyses of proteomic and transcriptomic data to evaluatepathways in Drosophila melanogaster that are affected byacute METH toxicity. METH exposure caused changes in the expression of genesinvolved with energy metabolism, suggesting a Warburg-like effect (aerobicglycolysis), which is normally associated with cancerous cells. Therefore,we tested the hypothesis that carbohydrate metabolism plays an importantrole in METH toxicity. In agreement with our hypothesis, we observed thatincreased dietary sugars partially alleviated the toxic effects of METH. Oursystems analysis also showed that METH impacted genes and proteins known tobe associated with muscular homeostasis/contraction, maintenance ofoxidative status, oxidative phosphorylation, spermatogenesis, iron andcalcium homeostasis. Our results also provide numerous candidate genes forthe METH-induced dysfunction of spermatogenesis, which have not beenpreviously characterized at the molecular level.ConclusionOur results support our overall hypothesis that METH causes a toxic syndromethat is characterized by the altered carbohydrate metabolism, dysregulationof calcium and iron homeostasis, increased oxidative stress, and disruptionof mitochondrial functions.

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.

EasyOmics: A graphical interface for population-scale omics data association, integration, and visualization.

Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression. To this end, bioinformatics tools and diverse AI techniques are pivotal in identifying and understanding tropical diseases through the analysis of omics data. In this article, we provide a thorough review of opportunities, challenges, and future directions of utilizing Bioinformatics tools and AI-assisted models on tropical disease identification using various omics data types. We conducted the review from 2015 to 2024 considering reliable databases of peer-reviewed journals and conference articles. Several keywords were taken for the article searching and around 40 articles were reviewed. According to the review, we observed that utilization of omics data with Bioinformatics tools like BLAST, and Clustal Omega can make significant outcomes in tropical disease identification. Further, the integration of multiple omics data improves biomarker identification, and disease predictions including disease outbreak predictions. Moreover, AI-assisted models can improve the precision, cost-effectiveness, and efficiency of CRISPR-based gene editing, optimizing gRNA design, and supporting advanced genetic correction. Several AI-assisted models including XAI can be used to identify diseases and repurpose therapeutic targets and biomarkers efficiently. Furthermore, recent advancements including Transformer-based models such as BERT and GPT-4, have been mainly applied for sequence analysis and functional genomics. Finally, the most recent GeneViT model, utilizing Vision Transformers, and other AI techniques like Generative Adversarial Networks, Federated Learning, Transfer Learning, Reinforcement Learning, Automated ML and Attention Mechanism have shown significant performance in disease classification using omics data.

A practical data processing workflow for multi-OMICS projects