Omics Data Research Articles

Integrating Multi-omics Data for Alzheimer's Disease to Explore Its Biomarkers Via the Hypergraph-Regularized Joint Deep Semi-Non-Negative Matrix Factorization Algorithm.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Its etiology may be associated with genetic, environmental, and lifestyle factors. With the advancement of technology, the integration of genomics, transcriptomics, and imaging data related to AD allows simultaneous exploration of molecular information at different levels and their interaction within the organism. This paper proposes a hypergraph-regularized joint deep semi-non-negative matrix factorization (HR-JDSNMF) algorithm to integrate positron emission tomography (PET), single-nucleotide polymorphism (SNP), and gene expression data for AD. The method employs matrix factorization techniques to nonlinearly decompose the original data at multiple layers, extracting deep features from different omics data, and utilizes hypergraph mining to uncover high-order correlations among the three types of data. Experimental results demonstrate that this approach outperforms several matrix factorization-based algorithms and effectively identifies multi-omics biomarkers for AD. Additionally, single-cell RNA sequencing (scRNA-seq) data for AD were collected, and genes within significant modules were used to categorize different types of cell clusters into high and low-risk cell groups. Finally, the study extensively explores the differences in differentiation and communication between these two cell types. The multi-omics biomarkers unearthed in this study can serve as valuable references for the clinical diagnosis and drug target discovery for AD. The realization of the algorithm in this paper code is available at https://github.com/ShubingKong/HR-JDSNMF .

The Obese Taste Bud study: Objectives and study design.

Taste modifies eating behaviour, impacting body weight and potentially obesity development. The Obese Taste Bud (OTB) Study is a prospective cohort study launched in 2020 at the University of Leipzig Obesity Centre in cooperation with the HI-MAG Institute. OTB will test the hypothesis that taste cell homeostasis and taste perception are linked to obesity. Here, we provide the study design, data collection process and baseline characteristics. Participants presenting overweight, obesity or normal weight undergo taste and smell tests, anthropometric, and taste bud density (TBD) assessment on Day 1. Information on physical and mental health, eating behaviour, physical activity, and dental hygiene are obtained, while biomaterial (saliva, tongue swap, blood) is collected in the fasted state. Further blood samples are taken during a glucose tolerance test. A stool sample is collected at home prior to Day 2, on which a taste bud biopsy follows dental examination. A subsample undergoes functional magnetic resonance imaging while exposed to eating-related cognitive tasks. Follow-up investigations after conventional weight loss interventions and bariatric surgery will be included. Initial results show that glycated haemoglobin levels and age are negatively associated with TBD, while an unfavourable metabolic profile, current dieting, and vegan diet are related to taste perception. Olfactory function negatively correlates with age and high-density lipoprotein cholesterol. Initial findings suggest that metabolic alterations are relevant for taste and smell function and TBD. By combining omics data from collected biomaterial with physiological, metabolic and psychological data related to taste perception and eating behaviour, the OTB study aims to strengthen our understanding of taste perception in obesity.

Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks

Although papillary thyroid cancer (PTC) has a good prognosis, its recurrence rate is high and remains a core concern in the clinic. Molecular factors contributing to different recurrence risks (RRs) remain poorly defined. Here, we perform an integrative proteogenomic and metabolomic characterization of 102 Chinese PTC patients with different RRs. Genomic profiling reveals that mutations in MUC16 and TERT promoter as well as multiple gene fusions like NCOA4-RET are enriched by the high RR. Integrative multi-omics analyses further describe the multi-dimensional characteristics of PTC, especially in metabolism pathways, and delineate dominated molecular patterns of different RRs. Moreover, the PTC patients are clustered into four subtypes (CS1: low RR and BRAF-like; CS2: high RR and metabolism type, worst prognosis; CS3: high RR and immune type, better prognosis; CS4: high RR and BRAF-like) based on the omics data. Notably, the subtypes display significant differences considering BRAF and TERT promoter mutations, metabolism and immune pathway profiles, epithelial cell compositions, and various clinical factors (especially RRs and prognosis) as well as druggable targets. This study can provide insights into the complex molecular characteristics of PTC recurrences and help promote early diagnosis and precision treatment of recurrent PTC.

Non-traumatic osteonecrosis of the femoral head induced by steroid and alcohol exposure is associated with intestinal flora alterations and metabolomic profiles

ObjectiveOsteonecrosis of the femoral head (ONFH) is a severe disease that primarily affects the middle-aged population, imposing a significant economic and social burden. Recent research has linked the progression of non-traumatic osteonecrosis of the femoral head (NONFH) to the composition of the gut microbiota. Steroids and alcohol are considered major contributing factors. However, the relationship between NONFH caused by two etiologies and the microbiota remains unclear. In this study, we examined the gut microbiota and fecal metabolic phenotypes of two groups of patients, and analyzed potential differences in the pathogenic mechanisms from both the microbial and metabolic perspectives.MethodsUtilizing fecal samples from 68 NONFH patients (32 steroid-induced, 36 alcohol-induced), high-throughput 16 S rDNA sequencing and liquid chromatography with tandem mass spectrometry (LC-MS/MS) metabolomics analyses were conducted. Univariate and multivariate analyses were applied to the omics data, employing linear discriminant analysis effect size to identify potential biomarkers. Additionally, functional annotation of differential metabolites and associated pathways was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Subsequently, Spearman correlation analysis was employed to assess the potential correlations between differential gut microbiota and metabolites.ResultsHigh-throughput 16 S rDNA sequencing revealed significant gut microbial differences. At the genus level, the alcohol group had higher Lactobacillus and Roseburia, while the steroid group had more Megasphaera and Akkermansia. LC-MS/MS metabolomic analysis indicates significant differences in fecal metabolites between steroid- and alcohol-induced ONFH patients. Alcohol-induced ONFH (AONFH) showed elevated levels of L-Lysine and Oxoglutaric acid, while steroid-induced ONFH(SONFH) had increased Gluconic acid and Phosphoric acid. KEGG annotation revealed 10 pathways with metabolite differences between AONFH and SONFH patients. Correlation analysis revealed the association between differential gut flora and differential metabolites.ConclusionsOur results suggest that hormones and alcohol can induce changes in the gut microbiota, leading to alterations in fecal metabolites. These changes, driven by different pathways, contribute to the progression of the disease. The study opens new research directions for understanding the pathogenic mechanisms of hormone- or alcohol-induced NONFH, suggesting that differentiated preventive and therapeutic approaches may be needed for NONFH caused by different triggers.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

Integrative analysis of the methylome and transcriptome of tomato fruit (Solanum lycopersicum L.) induced by postharvest handling

Abstract Tomato fruit ripening is triggered by the demethylation of key genes, which alters their transcriptional levels thereby initiating and propagating a cascade of physiological events. What is unknown, is how these processes are altered when fruit are ripened using postharvest practices to extend shelf-life, as these practices often reduce fruit quality. To address this, postharvest handling-induced changes in the fruit DNA methylome and transcriptome, and how they correlate with ripening speed, and ripening indicators such as ethylene, ABA, and carotenoids, were assessed. This study comprehensively connected changes in physiological events with dynamic molecular changes. Ripening fruit that reached ‘Turning’ (T) after dark-storage at 20°C, 12.5°C, or 5°C chilling (followed by 20°C rewarming), were compared to fresh-harvest fruit ‘FHT’. Fruit stored at 12.5°C, had the biggest epigenetic marks and alterations in gene expression, exceeding changes induced by postharvest chilling. Fruit physiological and chronological age were uncoupled at 12.5°C, as the time-to-ripening was the longest. Fruit ripening to Turning at 12.5°C was not climacteric; there was no respiratory or ethylene burst, rather, fruit were high in ABA. Clear differentiation between postharvest-ripened and ‘FHT’ was evident in the methylome and transcriptome. Higher expression of photosynthetic genes and chlorophyll levels in ‘FHT’ fruit, pointed to light as influencing the molecular changes in fruit ripening. Finally, correlative analyses of the -omics data putatively identified genes regulated by DNA methylation. Collectively, these data improve our interpretation of how tomato fruit ripening patterns are altered by postharvest practices, and long-term are expected to help improve fruit quality.

Abstract LB331: Integrating multiple spatial transcriptomic and proteomic technologies to generate a cancer atlas to understand the tumor microenvironment

Abstract Deep single-cell genomics has provided a monumental leap in our understanding and categorical analysis of cancer cells and their unique molecular properties. With spatial platforms that preserve the tissue architecture, an emerging understanding of complex cell interactions and local environment influences is providing new perspectives for targeted therapies. Here we describe a comprehensive approach at profiling the tumor immune microenvironment across multiple solid malignancies to provide an atlas of tumor biology. To generate this atlas, we created a MERFISH panel of 500 genes representing over 40 cell phenotyping pathways and dozens of signaling pathways including chemokines, cytokines, interferon, and ligand-receptor interactions. This panel was used across 15 patients representing multiple solid malignancies. We also utilized the Immuno-Oncology focused Xenium panel to profile 16 different patients across the same indications. With the Xenium data we simultaneously assessed proteomic expression using a 16 plex panel focused on immuno-oncology targets and subsequently did H&E staining, allowing multiple -omic data collections within a single slide. After initial filtering and quality control this projects produced a total dataset with over 15 million cells and billions of transcripts. The 500 gene MERFISH panel revealed complex relationships between diverse cell types within the tumor microenvironment that was dependent on cellular location and surrounding area. While the presence of major immune cell types was observed in all cancer tissues, the ratio and spatial relationship to cancer cells differed between each tumor. Accordingly, we observed that differences in cellular composition of the microenvironment corresponded with associated changes in cellular signaling. Similarly, with Xenium we identified new spatial relationships between cancer and immune cells with their environment while confirming part of these relationships with subsequent immunofluorescence data. The H&E data provided an opportunity to overlay this data with standard pathological annotation. We further investigated how each cellular microneighborhood varied throughout the tumor and varied from patient. This comprehensive map of the tumor immune microenvironment is a deep cellular dive into cellular architecture. As this Cancer Atlas evolves this will drive the discovery of several new potential biological targets and cell types of interest. This will also be critical for the assessment of current cellular and drug therapies in preclinical and clinical models. This effort will continue with more patient samples across a greater number of disease indications and will include more technologies. We will also expand the development and implementation of analytical tools to maximum the knowledge and insights gained from this Atlas. Together, this will be a critical tool in the continuing fight against cancer. Citation Format: Colles Price, Kazuho Nishimura, Som Bandyopadhyay, Darrell Borger, Russell Weiner. Integrating multiple spatial transcriptomic and proteomic technologies to generate a cancer atlas to understand the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB331.

Abstract LB247: TCPAplus: An LLM-empowered chatbot for analyzing a large protein expression atlas of human cancers

Abstract We present a comprehensive functional proteomics resource comprising nearly 9,000 samples from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, using ~500 high-quality antibodies through reverse phase protein arrays. To enhance its utility, we have developed an analytical chatbot harnessing advanced large language models. This intuitive platform empowers researchers to effortlessly query protein-centered cancer omics data, conduct related analyses, and visualize results using natural language, significantly easing the translation from intricate proteogenomic data to valuable biological insights. Citation Format: Wei Liu, Jun Liu, Yitao Tang, Chaozhong Liu, Meiyi Song, Zhenlin Ju, Shweth V. Kumar, Yiling Lu, Rehan Akbani, Gordon Mills, Han Liang. TCPAplus: An LLM-empowered chatbot for analyzing a large protein expression atlas of human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB247.

Yak genome database: a multi-omics analysis platform

BackgroundThe yak (Bos grunniens) is a large ruminant species that lives in high-altitude regions and exhibits excellent adaptation to the plateau environments. To further understand the genetic characteristics and adaptive mechanisms of yak, we have developed a multi-omics database of yak including genome, transcriptome, proteome, and DNA methylation data.DescriptionThe Yak Genome Database (http://yakgenomics.com/) integrates the research results of genome, transcriptome, proteome, and DNA methylation, and provides an integrated platform for researchers to share and exchange omics data. The database contains 26,518 genes, 62 transcriptomes, 144,309 proteome spectra, and 22,478 methylation sites of yak. The genome module provides access to yak genome sequences, gene annotations and variant information. The transcriptome module offers transcriptome data from various tissues of yak and cattle strains at different developmental stages. The proteome module presents protein profiles from diverse yak organs. Additionally, the DNA methylation module shows the DNA methylation information at each base of the whole genome. Functions of data downloading and browsing, functional gene exploration, and experimental practice were available for the database.ConclusionThis comprehensive database provides a valuable resource for further investigations on development, molecular mechanisms underlying high-altitude adaptation, and molecular breeding of yak.

Abstract LB226: Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Abstract Lung cancer is the leading cause of cancer-related mortality worldwide. Understanding lung cancer evolutionary dynamics can help identify tools to intercept its growth and suggest strategies for treatment. Multiple factors can impact the tumors’ natural history and distinctly affect growth rate. However, research on the evolutionary trajectories of lung cancer across demographic or exposure scenarios remains inadequate. Additionally, the roles of mutational processes and complex genomic alterations on the evolution of lung cancer are still largely unexplored. In the largest genomic study of lung cancer to date, we analyzed deep whole-genome sequencing (~ 81x) and other omics data of 1217 lung cancers from the Sherlock-Lung study. To ensure adequate statistical power for identifying subclone architectures and constructing lung cancer evolutionary histories, we utilized a metric known as NRPCC (number of reads per tumor chromosomal copy) to select 542 lung adenocarcinoma (LUAD) samples for clonal evolution analyses, including 186 and 181 samples from never-smoker subjects of European and Asian ancestry, respectively, and 121 samples from smokers of European ancestry. We found that major driver genes and exogenous mutations contribute to tumor initiation, while copy number gains and endogenous processes appear later in tumor evolution. Tumors harboring EGFR mutations in never-smoker females of European descent show long latency, while tumors with KRAS mutations have shorter latency regardless of ancestry and sex. Notably, tumors harboring the mutational signature ID2 have short latency and aggressive phenotype, accompanied by increased genomic instability, elevated hypoxia scores, high CpG methylator phenotype, low neoantigen burden, and propensity to develop metastasis. We show that LINE-1 retrotransposition-induced mutagenesis contributes to the origin of ID2 mutations. The transcriptional factor ZNF695, a member of the KZFP family, up-regulated in LUAD, appears to contribute to LINE-1 retrotransposition through a dominant-negative effect and LINE-1 promoter demethylation. In a multivariate analysis of genomics, exposures and demographic factors, LUAD latency was most significantly associated with ID2, followed by EGFR mutations, KRAS mutations, and sex, suggesting an independent impact of these factors on LUAD evolution. Our findings underscore the complex interplay of ancestry, sex, exogenous mutagenesis, epigenetic regulation, and LINE-1 retrotransposition in shaping LUAD evolutionary trajectories, paving the way for potential targeted therapeutic interventions. Citation Format: Tongwu Zhang, Wei Zhao, Christopher Wirth, Marcos Díaz-Gay, Jinhu Yin, Phuc H. Hoang, Jian Sang, John McElderry, Alyssa Klein, Azhar Khandekar, Caleb Hartman, Jennifer Rosenbaum, Frank Colon-Matos, Kristine M. Jones, Neil E. Caporaso, Robert Homer, Angela C. Pesatori, Dario Consonni, Lixing Yang, Bin Zhu, Jianxin Shi, Kevin Brown, Nathaniel Rothman, Stephen J. Chanock, Ludmil B. Alexandrov, Jiyeon Choi, Maurizio Cardelli, Qing Lan, Martin A. Nowak, David C. Wedge, Maria Teresa Landi. Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB226.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival.

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity. The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma. To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model, we proposed a novel deep association model (DAM) and corresponding efficient analysis framework. First, the Deep Subspace Reconstruction (DSR) was used to fuse the omics data and diagnostic information, thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises. Second, the Joint Deep Semi-Negative Matrix Factorization (JDSNMF) was applied to identify different latent sample patterns and extract biomarkers from different omics data levels. Third, our newly proposed Deep Orthogonal Canonical Correlation Analysis (DOCCA) can rank features in the collaborative module, which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels. Using DAM, we deeply analyzed the transcriptome and methylation data of childhood asthma. The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset, by ablation experiment and comparison with many baseline methods from clinical and biological studies. The DAM-induced diagnostic model can achieve a prediction AUC of 0.912, which is higher than that of many other alternative methods. Meanwhile, relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels. As an interpretable machine learning approach, DAM simultaneously considers the non-linear associations among samples and those among biological features, which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complex diseases. DAM is publicly available at https://github.com/babykai12345/DAM.

LettuceDB: an integrated multi-omics database for cultivated lettuce.

Crop genomics has advanced rapidly during the past decade, which generated a great abundance of omics data from multi-omics studies. How to utilize the accumulating data becomes a critical and urgent demand in crop science. As an attempt to integrate multi-omics data, we developed a database, LettuceDB (https://db.cngb.org/lettuce/), aiming to assemble multidimensional data for cultivated and wild lettuce germplasm. The database includes genome, variome, phenome, microbiome and spatial transcriptome. By integrating user-friendly bioinformatics tools, LettuceDB will serve as a one-stop platform for lettuce research and breeding in the future. Database URL: https://db.cngb.org/lettuce/.

Network analysis of the proteome and peptidome sheds light on human milk as a biological system

Proteins and peptides found in human milk have bioactive potential to benefit the newborn and support healthy development. Research has been carried out on the health benefits of proteins and peptides, but many questions still need to be answered about the nature of these components, how they are formed, and how they end up in the milk. This study explored and elucidated the complexity of the human milk proteome and peptidome. Proteins and peptides were analyzed with non-targeted nanoLC-Orbitrap-MS/MS in a selection of 297 milk samples from the CHILD Cohort Study. Protein and peptide abundances were determined, and a network was inferred using Gaussian graphical modeling (GGM), allowing an investigation of direct associations. This study showed that signatures of (1) specific mechanisms of transport of different groups of proteins, (2) proteolytic degradation by proteases and aminopeptidases, and (3) coagulation and complement activation are present in human milk. These results show the value of an integrated approach in evaluating large-scale omics data sets and provide valuable information for studies that aim to associate protein or peptide profiles from biofluids such as milk with specific physiological characteristics.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

IBDTransDB: a manually curated transcriptomic database for inflammatory bowel disease.

Inflammatory Bowel Disease (IBD) therapies are ineffective in at least 40% patients, and transcriptomic datasets have been widely used to reveal the pathogenesis and to identify the novel drug targets for these patients. Although public IBD transcriptomic datasets are available from many web-based tools/databases, due to the unstructured metadata and data description of these public datasets, most of these tools/databases do not allow querying datasets based on multiple keywords (e.g. colon and infliximab). Furthermore, few tools/databases can compare and integrate the datasets from the query results. To fill these gaps, we have developed IBDTransDB (https://abbviegrc.shinyapps.io/ibdtransdb/), a manually curated transcriptomic database for IBD. IBDTransDB includes a manually curated database with 34 transcriptomic datasets (2932 samples, 122 differential comparisons) and a query system supporting 35 keywords from 5 attributes (e.g. tissue and treatment). IBDTransDB also provides three modules for data analyses and integration. IBDExplore allows interactive visualization of differential gene list, pathway enrichment, gene signature and cell deconvolution analyses from a single dataset. IBDCompare supports comparisons of selected genes or pathways from multiple datasets across different conditions. IBDIntegrate performs meta-analysis to prioritize a list of genes/pathways based on user-selected datasets and conditions. Using two case studies related to infliximab treatment, we demonstrated that IBDTransDB provides a unique platform for biologists and clinicians to reveal IBD pathogenesis and identify the novel targets by integrating with other omics data. Database URL: https://abbviegrc.shinyapps.io/ibdtransdb/.

Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. Additionally, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC. Supplementary data are available at Bioinformatics online.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

BackgroundGastric cancer (GC) is one of the most common malignant tumors worldwide. Nevertheless, GC still lacks effective diagnosed and monitoring method and treating targets. This study used multi omics data to explore novel biomarkers and immune therapy targets around sphingolipids metabolism genes (SMGs).MethodLASSO regression analysis was performed to filter prognostic and differently expression SMGs among TCGA and GTEx data. Risk score model and Kaplan-Meier were built to validate the prognostic SMG signature and prognostic nomogram was further constructed. The biological functions of SMG signature were annotated via multi omics. The heterogeneity landscape of immune microenvironment in GC was explored. qRT-PCR was performed to validate the expression level of SMG signature. Competing endogenous RNA regulatory network was established to explore the molecular regulatory mechanisms.Result3-SMGs prognostic signature (GLA, LAMC1, TRAF2) and related nomogram were constructed combing several clinical characterizes. The expression difference and diagnostic value were validated by PCR data. Multi omics data reveals 3-SMG signature affects cell cycle and death via several signaling pathways to regulate GC progression. Overexpression of 3-SMG signature influenced various immune cell infiltration in GC microenvironment. RBP-SMGs-miRNA-mRNAs/lncRNAs regulatory network was built to annotate regulatory system.ConclusionUpregulated 3-SMGs signature are excellent predictive diagnosed and prognostic biomarkers, providing a new perspective for future GC immunotherapy.

Precision medicine to personalize medicine in allergic airway disease.

The purpose of this study is to understand the approach to precision medicine and personalized medicine in the management of allergic airway disease. Identification of biomarkers as key tools used in precision medicine has led to the development of multiple biologic drugs being used as new treatments for allergic airway disease. In addition to these advances, there is still much needed effort to improve the feasibility and utility of integrating biologic omics data of precision medicine with physicochemical, behavioral, psychological, and social data to deliver optimized treatments that is personalized for each individual.