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PAS‐Gen (iPhone 8) screenshot examples of gene results (top two shown) from searches for the four most common diseases: a 931 results for Diabetes, b 60 results for Obesity, c 391 results for Schizophrenia, and d 313 results for Autism. Detailed results are attached in Additional file 1
Source publication
Background:
The last decade has seen a dramatic increase in the availability of scientific data, where human-related biological databases have grown not only in count but also in volume, posing unprecedented challenges in data storage, processing, analysis, exchange, and curation. Next generation sequencing (NGS) advancements have facilitated and...
Citations
... From the Sequence Alignment Map file, variant call format files are created, which store information regarding variations, insertions and deletions (6). Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) are two types of NGS that are more accurate methods of DNA sequencing and are used to find variants in a DNA sequence (20). While WGS sequences the whole genome, WES sequences only the protein-coding regions (21). ...
... However, recent developments in the field highlight some promising outcomes in the creation of a unified genomic-EHR system. PROMIS-APP-SUITE (PAS)-Gen mobile application is a publicly available iOS app that leverages a database of over 59 000 coding and non-coding genes along with 90 000 gene-disease associations (20). It was created with the intention of assisting academic researchers and medical professionals in understanding the dynamic between disease and genes (20). ...
... PROMIS-APP-SUITE (PAS)-Gen mobile application is a publicly available iOS app that leverages a database of over 59 000 coding and non-coding genes along with 90 000 gene-disease associations (20). It was created with the intention of assisting academic researchers and medical professionals in understanding the dynamic between disease and genes (20). ...
A timely understanding of the biological secrets of complex diseases will ultimately benefit millions of individuals by reducing the high risks for mortality and improving the quality of life with personalized diagnoses and treatments. Due to the advancements in sequencing technologies and reduced cost, genomics data are developing at an unmatched pace and levels to foster translational research and precision medicine. Over 10 million genomics datasets have been produced and publicly shared in 2022. Diverse and high-volume genomics and clinical data have the potential to broaden the scope of biological discoveries and insights by extracting, analyzing and interpreting the hidden information. However, the current and still unresolved challenges include the integration of genomic profiles of the patients with their medical records. The definition of disease in genomics medicine is simplified, whereas in the clinical world, diseases are classified, identified and adopted with their International Classification of Diseases (ICD) codes, which are maintained by the World Health Organization. Several biological databases have been produced, which include information about human genes and related diseases. However, still, there is no database that exists, which can precisely link clinical codes with relevant genes and variants to support genomic and clinical data integration for clinical and translational medicine. In this project, we focused on the development of an annotated gene–disease–code database, which is accessible through an online, cross-platform and user-friendly application, i.e. PROMIS-APP-SUITE-Gene-Disease-Code. However, our scope is limited to the integration of ICD-9 and ICD-10 codes with the list of genes approved by the American College of Medical Genetics and Genomics. The results include over 17 000 diseases and 4000 ICD codes, and over 11 000 gene–disease–code combinations.
Database URL https://promis.rutgers.edu/pas/
... We applied our in-house developed platform GVViZ [22] for the bioinformatics analysis, including genedisease data integration, annotation and identification of genes associated with the HF, AF, and other CVDs. Furthermore, it assisted in geneexpression analysis, heatmap visualization, and interpretation supported with our in-house modelled gene-disease database [27][28][29]. ...
Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.
... 38 Furthermore, to understand the genetic basis of common diseases, numerous clinical, genomics, and clinical-genomics databases (e.g., Human Gene Mutation Database (HGMD), Catalogue Of Somatic Mutations In Cancer (COSMIC), Online Mendelian Inheritance in Man (OMIM), ClinVar, Ensembl, GeneCards, MalaCard, DISEASES, Disease Ontology, DiseaseEnhancer, DisGeNET, eDGAR, miR2Disease, DNetDB, GenCode, Novoseek, Swiss-Prot, LncRNADisease, Orphanet, and PROMIS-APP-SUITE (PAS), etc.) have been surfaced to support gene-disease data integration, analysis, and annotation. 35,[39][40][41][42][43][44][45][46] Such, authentic information would then be used in real time to improve the care of current and future patients. ...
Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and to improve routine medical and public health practice. Understanding patients' multi-omics make-up in conjunction with the clinical data will lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and to optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. Precision medicine promotes integrating collective and individualized clinical data with patient-specific multi-omics data to develop therapeutic strategies and knowledge bases for predictive and personalized medicine in diverse populations. Artificial intelligence approaches and machine learning algorithms will add additional capabilities to precision medicine that will leverage and extend the information contained within the original data and facilitate modeling patient-specific multi-omics data against publicly available annotation data for better understanding disease mechanisms. This chapter discusses emerging, significant, and recently reported multi-omics, deep phenotyping, and translational approaches to facilitate the implementation of precision medicine, as well as innovative, smart, and robust big-data platforms that are necessary to improve the quality and transition of healthcare by analyzing heterogeneous healthcare and multi-omics data.
... Furthermore, a comparative analysis of gene-associated diseases investigated in this study and reported in the recently published literature to be included among the symptomatology of COVID-19 was performed ( Figure 5). To annotate the genes with diseases, the authors used an in-house developed gene-annotation database [95,98,99]. Many diseases were found shared between the genes. ...
Aim: A human immunogenetics variation study was conducted in samples collected from diverse COVID-19 populations. Materials & methods: Whole-genome and whole-exome sequencing (WGS/WES), data processing, analysis and visualization pipeline were applied to identify variants associated with genes of interest. Results: A total of 2886 mutations were found across the entire set of 13 genomes. Functional annotation of the gene variants revealed mutation type and protein change. Many variants were found to be biologically implicated in COVID-19. The involvement of these genes was also found in multiple other diseases. Conclusion: The analysis determined that ACE2, TMPRSS4, TMPRSS2, SLC6A20 and FYCOI had functional implications and TMPRSS4 was the gene most altered in virally infected patients.
... These bioinformatics applications can be command line, desktop, and web based. To understand the genetic basis of common diseases these must be linked and/or include databases and knowledgebases including authentic information related to the known biomarkers, protein coding and non-coding genes, germline and somatic mutations, and classified disease and drug codes ( Figure 1) [33][34][35][36][37][38][39][40][41][42][43]. ...
Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.
... We used our in-house developed GVViZ platform to perform expression analysis using TPM counts of the protein coding genes computed from RNA-seq data. Furthermore, the expression data were linked to gene-disease annotation databases [27,38,39] to classify and differentiate between CVD and other disease-based functional and non-functional genes. A heatmap of all the CVD genes was constructed (Fig. 4) and annotated with their associated clinical CVD phenotype. ...
Background
Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations.
Methods
We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases.
Results
We report RNA-seq driven case–control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes.
Conclusions
Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.
... Mobile technology (e.g., iOS and Android-based smart phones and tablets) and consumer wearable devices can be used to for the participant recruitment, consenting and onboarding. Mobile Health Platform [25] can support real-time and automatic digital health data collection, annotation, harmonization, transmission and sharing [26,27]. Furthermore, consumer wearable devices can be helpful in collecting information related to the post-acute Sequelae of COVID-19 (PASC) [28] such as beat-to-beat heart rate, cardiovascular hemodynamics (e.g., systole duration, pulse pressure, pulse volume), respiration rate, skin temperature (temperature, fever detection, calibration for oxygen saturation measurements), cough, anxiety, actigraphy, physical (motion), activity levels and sleep patterns [29,30]. ...
Advancing frontiers of clinical research, we discuss the need for intelligent health systems to support a deeper investigation of COVID-19. We hypothesize that the convergence of the healthcare data and staggering developments in artificial intelligence have the potential to elevate the recovery process with diagnostic and predictive analysis to identify major causes of mortality, modifiable risk factors and actionable information that supports the early detection and prevention of COVID-19. However, current constraints include the recruitment of COVID-19 patients for research; translational integration of electronic health records and diversified public datasets; and the development of artificial intelligence systems for data-intensive computational modeling to assist clinical decision making. We propose a novel nexus of machine learning algorithms to examine COVID-19 data granularity from population studies to subgroups stratification and ensure best modeling strategies within the data continuum.
... To advance our clinical genomics and precision medicine study, we modeled and implemented an annotated disease-gene-variants database that includes but is not limited to data collected from several genomics databases worldwide [7], including PAS [14,15], ClinVar [16], GeneCards [17], MalaCard [18], DISEASES [19], HGMD [20], Disease Ontology [21], DiseaseEnhancer [22], DisGeNET [23], eDGAR [24], GTR [25], OMIM [26], miR2Disease [27], DNetDB [28], GTR, CNVD, Ensembl, GenCode, Novoseek, Swiss-Prot, LncRNADisease, Orphanet, and Catalogue Of Somatic Mutations In Cancer (COSMIC) [29]. Our gene datasets consist of 59,293 total genes (19,989 are protein-coding and 39,304 are non-protein-coding) and over 200,000 gene-disease combinations. ...
Background
Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.
Results
In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.
Conclusions
We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.
... Best fitting use of ML and AI algorithms have the potential to predict the existence of life-threatening diseases risk susceptibility, starting from the most common to rare among the population data [19]. AI has the ability to improve identification of relevant variables for patient data stratification with timely detection of statistical patterns across millions of features to identify conditions that are likely to manifest later and discover modifiable risk factors that support the best utilization of known therapies [38]. Impactful and automated implementation of AI and ML can elevate investigating correlation and overlapping of reported diagnoses of a patient in clinical data, and assess genotype and phenotype associations among various diseases to find potential indistinct results for patient care from highly expressed genes and disease-causing variants [9,39]. ...
Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient’s metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.
... 1 We enhanced the scope of our research project with the implementation of a new gene-disease database (PAS-Gen). 136 The goal was to collect all distinct, authentic, and actionable genes-based information and related diseases from maximum possible available sources. In this paper, we have presented further extended research to our research project, with the inclusion of over a million somatic mutations, over 100 thousand germline mutations, and available clinical relevant drug and disease codes, and their variable associations. ...
We are entering the era of personalized medicine in which an individual's genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss‐Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting‐edge gene‐SNP‐disease‐drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein‐coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein‐coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene‐SNP‐disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene‐based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.
