Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DGKI, MEF2C, MAB21L2, MYL7, LDB3, SLC7A11, MAB21L2, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.

Integrated epigenomic exposure signature discovery.

Aim: The epigenome influences gene regulation and phenotypes in response to exposures. Epigenome assessment can determine exposure history aiding in diagnosis.Materials & methods: Here we developed and implemented a machine learning algorithm, the exposure signature discovery algorithm (ESDA), to identify the most important features present in multiple epigenomic and transcriptomic datasets to produce an integrated exposure signature (ES).Results: Signatures were developed for seven exposures including Staphylococcus aureus, human immunodeficiency virus, SARS-CoV-2, influenza A (H3N2) virus and Bacillus anthracis vaccinations. ESs differed in the assays and features selected and predictive value.Conclusion: Integrated ESs can potentially be utilized for diagnosis or forensic attribution. The ESDA identifies the most distinguishing features enabling diagnostic panel development for future precision health deployment.

This article introduces ESDA, a new analytic tool for integrating multiple data types to identify the most distinguishing features following an exposure. Using the ESDA, we were able to identify signatures of infectious diseases. The results of the study indicate that integration of multiple types of large datasets can be used to identify distinguishing features for infectious diseases. Understanding the changes from different exposures will enable development of diagnostic tests for infectious diseases that target responses from the patient. Using the ESDA, we will be able to build a database of human response signatures to different infections and simplify diagnostic testing in the future.

Maternal Innate Immune Reprogramming After Complicated Pregnancy.

PROBLEM: Preeclampsia (PE) and fetal growth restriction (FGR) are often associated with maternal inflammation and an increased risk of cardiovascular and metabolic disease in the affected mothers. The mechanism responsible for this increased risk of subsequent disease may involve reprogramming of innate immune cells, characterized by epigenetic modifications. METHOD OF STUDY: Circulating monocytes from women with PE, FGR, or uncomplicated pregnancies (control) were isolated before labor. Cytokine release from monocytes following exposure to lipopolysaccharide (LPS) and the presence of lysine 4-trimethylated histone 3 (H3K4me3) within TNF promoter sequences were evaluated. Single-cell transcriptomic profiles of circulating monocytes from women with PE or uncomplicated pregnancies were assessed. RESULTS: Monocytes from women with PE or FGR exhibited increased IL-10 secretion and decreased IL-1ß and GM-CSF secretion in response to LPS. While TNFα secretion was not significantly different in cultures of control monocytes versus those from complicated pregnancies with or without LPS exposure, monocytes from complicated pregnancies had significantly decreased levels of H3K4me3 associated with TNF promoter sequences. Cluster quantification and pathway analysis of differentially expressed genes revealed an increased proportion of anti-inflammatory myeloid cells and a lower proportion of inflammatory non-classical monocytes among the circulating monocyte population in women with PE. CONCLUSIONS: Monocytes from women with PE and FGR exhibit an immune tolerance phenotype before initiation of labor. Further investigation is required to determine whether this tolerogenic phenotype persists after the affected pregnancy and contributes to increased risk of subsequent disease.

Transcriptional and epigenetic characterization of a new in vitro platform to model the formation of human pharyngeal endoderm.

BACKGROUND: The Pharyngeal Endoderm (PE) is an extremely relevant developmental tissue, serving as the progenitor for the esophagus, parathyroids, thyroids, lungs, and thymus. While several studies have highlighted the importance of PE cells, a detailed transcriptional and epigenetic characterization of this important developmental stage is still missing, especially in humans, due to technical and ethical constraints pertaining to its early formation. RESULTS: Here we fill this knowledge gap by developing an in vitro protocol for the derivation of PE-like cells from human Embryonic Stem Cells (hESCs) and by providing an integrated multi-omics characterization. Our PE-like cells robustly express PE markers and are transcriptionally homogenous and similar to in vivo mouse PE cells. In addition, we define their epigenetic landscape and dynamic changes in response to Retinoic Acid by combining ATAC-Seq and ChIP-Seq of histone modifications. The integration of multiple high-throughput datasets leads to the identification of new putative regulatory regions and to the inference of a Retinoic Acid-centered transcription factor network orchestrating the development of PE-like cells. CONCLUSIONS: By combining hESCs differentiation with computational genomics, our work reveals the epigenetic dynamics that occur during human PE differentiation, providing a solid resource and foundation for research focused on the development of PE derivatives and the modeling of their developmental defects in genetic syndromes.

CUT&Tag for Efficient Epigenomic Profiling of Frozen Tissues.

Cleavage Under Targets and Tagmentation (CUT&Tag) provides high-resolution sequencing libraries for profiling diverse chromatin components. This protocol details the steps to generate CUT&Tag libraries from fresh or frozen tissues. This CUT&Tag workflow has nine main steps: isolation of nuclei from tissues, binding of nuclei to Concanavalin A-coated beads, binding of the primary antibody, binding of the secondary antibody, binding pA-Tn5 adapter complex, tagmentation, DNA extraction, PCR, and post-PCR cleanup and size selection. This protocol enabled us to generate and sequence CUT&Tag libraries across a broad range of fresh and frozen tissue types.

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people's lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.

The Private Practitioner: A Veterinary Practitioner's Perspective to the Application of Bovine Genomics in Client Herds.

This article talks about how genomic evaluation has changed how sires are selected in the major dairy and beef breeds. The population of bulls being evaluated for dairy herd sire usage has grown perhaps 10 to 20 fold through the use of genomic screening of potential sires prior to purchase and admission to a bull stud. The number actually standing at stud has not changed. Genomic evaluation in both beef and dairy breeds has been an important component in the substantial growth of the beef (sire) on dairy (cow) phenomena.

Genomic, epigenomic and transcriptomic landscape of glioblastoma.

The mostly aggressive and extremely malignant type of central nervous system is Glioblastoma (GBM), which is characterized by an extremely short average survival time of lesser than 16 months. The primary cause of this phenomenon can be attributed to the extensively altered genome of GBM, which is characterized by the dysregulation of numerous critical signaling pathways and epigenetics regulations associated with proliferation, cellular growth, survival, and apoptosis. In light of this, different genetic alterations in critical signaling pathways and various epigenetics regulation mechanisms are associated with GBM and identified as distinguishing markers. Such GBM prognostic alterations are identified in PI3K/AKT, p53, RTK, RAS, RB, STAT3 and ZIP4 signaling pathways, metabolic pathway (IDH1/2), as well as alterations in epigenetic regulation genes (MGMT, CDKN2A-p16INK4aCDKN2B-p15INK4b). The exploration of innovative diagnostic and therapeutic approaches that specifically target these pathways is utmost importance to enhance the future medication for GBM. This study provides a comprehensive overview of dysregulated epigenetic mechanisms and signaling pathways due to mutations, methylation, and copy number alterations of in critical genes in GBM with prevalence and emphasizing their significance.

Unraveling clonal CD8 T cell expansion and identification of essential factors in γ-herpesvirus-induced lymphomagenesis.

Alcelaphine gammaherpesvirus 1 (AlHV-1) asymptomatically persists in its natural host, the wildebeest. However, cross-species transmission to cattle results in the induction of an acute and lethal peripheral T cell lymphoma-like disease (PTCL), named malignant catarrhal fever (MCF). Our previous findings demonstrated an essential role for viral genome maintenance in infected CD8+ T lymphocytes but the exact mechanism(s) leading to lymphoproliferation and MCF remained unknown. To decipher how AlHV-1 dysregulates T lymphocytes, we first examined the global phenotypic changes in circulating CD8+ T cells after experimental infection of calves. T cell receptor repertoire together with transcriptomics and epigenomics analyses demonstrated an oligoclonal expansion of infected CD8+ T cells displaying effector and exhaustion gene signatures, including GZMA, GNLY, PD-1, and TOX2 expression. Then, among viral genes expressed in infected CD8+ T cells, we uncovered A10 that encodes a transmembrane signaling protein displaying multiple tyrosine residues, with predicted ITAM and SH3 motifs. Impaired A10 expression did not affect AlHV-1 replication in vitro but rendered AlHV-1 unable to induce MCF. Furthermore, A10 was phosphorylated in T lymphocytes in vitro and affected T cell signaling. Finally, while AlHV-1 mutants expressing mutated forms of A10 devoid of ITAM or SH3 motifs (or both) were able to induce MCF, a recombinant virus expressing a mutated form of A10 unable to phosphorylate its tyrosine residues resulted in the lack of MCF and protected against a wild-type virus challenge. Thus, we could characterize the nature of this γ-herpesvirus-induced PTCL-like disease and identify an essential mechanism explaining its development.

The Contribution of Genetic and Epigenetic Factors: An Emerging Concept in the Assessment and Prognosis of Inflammatory Bowel Diseases.

Inflammatory bowel disease (IBD) represents heterogeneous and relapsing intestinal conditions with a severe impact on the quality of life of individuals and a continuously increasing prevalence. In recent years, the development of sequencing technology has provided new means of exploring the complex pathogenesis of IBD. An ideal solution is represented by the approach of precision medicine that investigates multiple cellular and molecular interactions, which are tools that perform a holistic, systematic, and impartial analysis of the genomic, transcriptomic, proteomic, metabolomic, and microbiomics sets. Hence, it has led to the orientation of current research towards the identification of new biomarkers that could be successfully used in the management of IBD patients. Multi-omics explores the dimension of variation in the characteristics of these diseases, offering the advantage of understanding the cellular and molecular mechanisms that affect intestinal homeostasis for a much better prediction of disease development and choice of treatment. This review focuses on the progress made in the field of prognostic and predictive biomarkers, highlighting the limitations, challenges, and also the opportunities associated with the application of genomics and epigenomics technologies in clinical practice.

Loss of TGFß-Mediated Repression of Angiopoietin-2 in Pericytes Underlies Germinal Matrix Hemorrhage Pathogenesis.

BACKGROUND: TGF (transforming growth factor)-ß pathway is central to blood-brain barrier development as it regulates cross talk between pericytes and endothelial cells. Murine embryos lacking TGFß receptor Alk5 (activin receptor-like kinase 5) in brain pericytes (mutants) display endothelial cell hyperproliferation, abnormal vessel morphology, and gross germinal matrix hemorrhage-intraventricular hemorrhage (GMH-IVH), leading to perinatal lethality. Mechanisms underlying how ALK5 signaling in pericytes noncell autonomously regulates endothelial cell behavior remain elusive. METHODS: Transcriptomic analysis of human brain pericytes with ALK5 silencing identified differential gene expression. Brain vascular cells isolated from mutant embryonic mice with GMH-IVH and preterm human IVH brain samples were utilized for target validation. Finally, pharmacological and genetic inhibition was used to study the therapeutic effects on GMH-IVH pathology. RESULTS: Herein, we establish that the TGFß/ALK5 pathway robustly represses ANGPT2 (angiopoietin-2) in pericytes via epigenetic remodeling. TGFß-driven SMAD (suppressor of mothers against decapentaplegic) 3/4 associates with TGIF1 (TGFß-induced factor homeobox 1) and HDAC (histone deacetylase) 5 to form a corepressor complex at the Angpt2 promoter, resulting in promoter deacetylation and gene repression. Moreover, murine and human germinal matrix vessels display increased ANGPT2 expression during GMH-IVH. Isolation of vascular cells from murine germinal matrix identifies pericytes as a cellular source of excessive ANGPT2. In addition, mutant endothelial cells exhibit higher phosphorylated TIE2 (tyrosine protein kinase receptor). Pharmacological or genetic inhibition of ANGPT2 in mutants improves germinal matrix vessel morphology and attenuates GMH pathogenesis. Importantly, genetic ablation of Angpt2 in mutant pericytes prevents perinatal lethality, prolonging survival. CONCLUSIONS: This study demonstrates that TGFß-mediated ANGPT2 repression in pericytes is critical for maintaining blood-brain barrier integrity and identifies pericyte-derived ANGPT2 as an important pathological target for GMH-IVH.

Integrated genomic/epigenomic analysis stratifies subtypes of clear cell ovarian carcinoma, highlighting their cellular origin.

The cellular origin of clear cell ovarian carcinoma (CCOC), a major histological subtype of ovarian carcinoma remains elusive. Here, we explored the candidate cellular origin and identify molecular subtypes using integrated genomic/epigenomic analysis. We performed whole exome-sequencing, microarray, and DNA methylation array in 78 CCOC samples according to the original diagnosis. The findings revealed that ARID1A and/or PIK3CA mutations were mutually exclusive with DNA repair related genes, including TP53, BRCA1, and ATM. Clustering of CCOC and other ovarian carcinomas (n = 270) with normal tissues from the fallopian tube, ovarian surface epithelium, endometrial epithelium, and pelvic peritoneum mesothelium (PPM) in a methylation array showed that major CCOC subtypes (with ARID1A and/or PIK3CA mutations) were associated with the PPM-lile cluster (n = 64). This cluster was sub-divided into three clusters: (1) mismatch repair (MMR) deficient with tumor mutational burden-high (n = 2), (2) alteration of ARID1A (n = 51), and (3) ARID1A wild-type (n = 11). The remaining samples (n = 14) were subdivided into (4) ovarian surface epithelium-like (n = 11) and (5) fallopian tube-like (considered as high-grade serous histotype; n = 3). Among these, subtypes (1-3) and others (4 and 5) were found to be associated with immunoreactive signatures and epithelial-mesenchymal transition, respectively. These results contribute to the stratification of CCOC into biological subtypes.

DNA Methylation Patterns Associated with Tinnitus in Young Adults-A Pilot Study.

PURPOSE: Tinnitus, the perception of sound without any external sound source, is a prevalent hearing health concern. Mounting evidence suggests that a confluence of genetic, environmental, and lifestyle factors can influence the pathogenesis of tinnitus. We hypothesized that alteration in DNA methylation, an epigenetic modification that occurs at cytosines of cytosine-phosphate-guanine (CpG) dinucleotide sites, where a methyl group from S-adenyl methionine gets transferred to the fifth carbon of the cytosine, could contribute to tinnitus. DNA methylation patterns are tissue-specific, but the tissues involved in tinnitus are not easily accessible in humans. This pilot study used saliva as a surrogate tissue to identify differentially methylated CpG regions (DMRs) associated with tinnitus. The study was conducted on healthy young adults reporting bilateral continuous chronic tinnitus to limit the influence of age-related confounding factors and health-related comorbidities. METHODS: The present study evaluated the genome-wide methylation levels from saliva-derived DNA samples from 24 healthy young adults with bilateral continuous chronic tinnitus (> 1 year) and 24 age, sex, and ethnicity-matched controls with no tinnitus. Genome-wide DNA methylation was evaluated for > 850,000 CpG sites using the Infinium Human Methylation EPIC BeadChip. The association analysis used the Bumphunter algorithm on 23 cases and 20 controls meeting the quality control standards. The methylation level was expressed as the area under the curve of CpG sites within DMRs.The FDR-adjusted p-value threshold of 0.05 was used to identify statistically significant DMRs associated with tinnitus. RESULTS: We obtained 25 differentially methylated regions (DMRs) associated with tinnitus. Genes within or in the proximity of the hypermethylated DMRs related to tinnitus included LCLAT1, RUNX1, RUFY1, NUDT12, TTC23, SLC43A2, C4orf27 (STPG2), and EFCAB4B. Genes within or in the proximity of hypomethylated DMRs associated with tinnitus included HLA-DPB2, PM20D1, TMEM18, SNTG2, MUC4, MIR886, MIR596, TXNRD1, EID3, SDHAP3, HLA-DPB2, LASS3 (CERS3), C10orf11 (LRMDA), HLA-DQB1, NADK, SZRD1, MFAP2, NUP210L, TPM3, INTS9, and SLC2A14. The burden of genetic variation could explain the differences in the methylation levels for DMRs involving HLA-DPB2, HLA-DQB1, and MUC4, indicating the need for replication in large independent cohorts. CONCLUSION: Consistent with the literature on comorbidities associated with tinnitus, we identified genes within or close to DMRs involved in auditory functions, chemical dependency, cardiovascular diseases, psychiatric conditions, immune disorders, and metabolic syndromes. These results indicate that epigenetic mechanisms could influence tinnitus, and saliva can be a good surrogate for identifying the epigenetic underpinnings of tinnitus in humans. Further research with a larger sample size is needed to identify epigenetic biomarkers and investigate their influence on the phenotypic expression of tinnitus.

Beyond the Heartbeat: Single-Cell Omics Redefining Cardiovascular Research.

PURPOSE OF REVIEW: This review aims to explore recent advances in single-cell omics techniques as applied to various regions of the human heart, illuminating cellular diversity, regulatory networks, and disease mechanisms. We examine the contributions of single-cell transcriptomics, genomics, proteomics, epigenomics, and spatial transcriptomics in unraveling the complexity of cardiac tissues. RECENT FINDINGS: Recent strides in single-cell omics technologies have revolutionized our understanding of the heart's cellular composition, cell type heterogeneity, and molecular dynamics. These advancements have elucidated pathological conditions as well as the cellular landscape in heart development. We highlight emerging applications of integrated single-cell omics, particularly for cardiac regeneration, disease modeling, and precision medicine, and emphasize the transformative potential of these technologies to advance cardiovascular research and clinical practice.

Mitochondrial stress-induced H4K12 hyperacetylation dysregulates transcription in Parkinson's disease.

Aberrant epigenetic modification has been implicated in the pathogenesis of Parkinson's disease (PD), which is characterized by the irreversible loss of dopaminergic (DAergic) neurons. However, the mechanistic landscape of histone acetylation (ac) in PD has yet to be fully explored. Herein, we mapped the proteomic acetylation profiling changes at core histones H4 and thus identified H4K12ac as a key epigenomic mark in dopaminergic neuronal cells as well as in MitoPark animal model of PD. Notably, the significantly elevated H4K12ac deposition in post-mortem PD brains highlights its clinical relevance to human PD. Increased histone acetyltransferase (HAT) activity and decreased histone deacetylase 2 (HDAC2) and HDAC4 were found in experimental PD cell models, suggesting the HAT/HDAC imbalance associated with mitochondrial stress. Following our delineation of the proteasome dysfunction that possibly contributes to H4K12ac deposition, we characterized the altered transcriptional profile and disease-associated pathways in the MitoPark mouse model of PD. Our study uncovers the axis of mitochondrial impairment-H4K12ac deposition-altered transcription/disease pathways as a neuroepigenetic mechanism underlying PD pathogenesis. These findings provide mechanistic information for the development of potential pharmacoepigenomic translational strategies for PD.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Omics approaches in asthma research: Challenges and opportunities.

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.