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It’s been over 100 years since the word `gene’ is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it’s about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene–disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene–disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene–disease data.

BackgroundThe 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 accelerated the process of identifying genetic variations. Adopting NGS with Whole‐Genome and RNA sequencing in a diagnostic context has the potential to improve disease‐risk detection in support of precision medicine and drug discovery. Several bioinformatics pipelines have been developed to strengthen variant interpretation by efficiently processing and analyzing sequence data, whereas many published results show how genomics data can be proactively incorporated into medical practices and improve utilization of clinical information. To utilize the wealth of genomics and health, there is a crucial need to generate appropriate gene‐disease annotation repositories accessed through modern technology. ResultsOur focus here is to create a comprehensive database with mobile access to actionable genes and classified diseases, considered the foundation for clinical genomics and precision medicine. We present a publicly available iOS app, PAS‐Gen, which invites global users to freely download it on iPhone and iPad devices, quickly adopt its easy to use interface, and search for genes and related diseases. PAS‐Gen was developed using Swift, XCODE, and PHP scripting that uses Web and MySQL database servers, which includes over 59,000 protein‐coding and non‐coding genes, and over 90,000 classified gene‐disease associations. PAS‐Gen is founded on the clinical and scientific premise that easier healthcare and genomics data sharing will accelerate future medical discoveries. ConclusionsWe present a cutting‐edge gene‐disease database with a smart phone application, integrating information on classified diseases and related genes. The PAS‐Gen app will assist researchers, medical practitioners, and pharmacists by providing a broad and view of genes that may be implicated in the likelihood of developing certain diseases. This tool with accelerate users’ abilities to understand the genetic basis of human complex diseases and by assimilating genomic and phenotypic data will support future work to identify gene‐specific designer drugs, target precise molecular fingerprints for tumors, suggest appropriate drug therapies, predict individual susceptibility to disease, and diagnose and treat rare illnesses.

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Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system, debilitating disability manifesting as severe fatigue and post-exertional malaise. The chronic dysfunctions in ME/CFS are increasingly recognized as significant health factors with potential parallels with ‘long COVID’. However, the etiology of ME/CFS remains elusive with limited high-resolution human studies. In addition, reliable biomarker-based diagnostics have not been well-established, but may assist in disease classification, particularly during different temporal phases of the disease. Here, we performed deep multi-‘omics (shotgun metagenomics of gut microbiota and plasma metabolomics) and clinical phenotyping of healthy controls (n=79) vs. two cohorts of ME/CFS patients – those with short-term disease (<4 years, n=75), and patients with long-term disease (>10y, n=79). Overall, ME/CFS was characterized by reduced gut microbiome diversity and richness with high heterogeneity, and depletion of sphingomyelins and short-chain fatty acids in the plasma. We found significant differences when stratifying by cohort; short-term ME/CFS was associated with more microbial dysbiosis, but long-term ME/CFS was associated with markedly more severe phenotypic and metabolic abnormalities. We identified a reduction in the gene-coding capacity (and relative abundance of butyrate producers) of microbial butyrate biosynthesis together with a reduction in the plasma concentration of butyrate, especially in the short-term group. Global co-association and detailed gene pathway correlation analyses linking the microbiome and metabolome identified additional potential biological mechanisms underlying host-microbiome interactions in ME/CFS, including bile acids and benzoate pathways. Finally, we built multiple state-of-the-art classifiers to identify microbes, microbial gene pathways, metabolites, and clinical features that individually or together, were most able to differentiate short or long-term MECFS, or MECFS vs. healthy controls. Taken together, our study presents the highest resolution, multi-cohort and multi-‘omics analysis to date, providing an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.