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Supplementary Figure from Inhibition of Mtorc1/2 and DNA-PK via CC-115 Synergizes with Carboplatin and Paclitaxel in Lung Squamous Cell Carcinoma

Cardiovascular disease (CVD) is a leading cause of premature mortality in the US and the world. CVD comprises of several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. Here, we report our findings from an integrative analysis of gene expression, disease-causing gene variants, and associated phenotypes among CVD populations, with a focus on high-risk Heart Failure (HF) patients. We built a cohort using electronic health records (EHR) of consented patients with available samples, and then performed high-throughput whole-genome and RNA sequencing (RNA-seq) of key genes responsible for HF and other CVD pathologies. We also incorporated a translational aspect to our study by integrating genomics findings with patient medical records. This involved linking ICD-10 codes with our gene expression and variant data to identify associations with HF and other CVDs. Our in-depth gene expression analysis revealed differentially expressed genes associated with HF (41 genes) and other CVDs (23 genes). Furthermore, a variant analysis of whole-genome sequence data of CVD patients identified genes with altered gene expression (FLNA, CST3, LGALS3, and HBA1) with functional and nonfunctional mutations in these genes. Our study highlights the importance of an integrative approach that leverages gene expression, genetic mutations, and clinical data that will allow the prioritization of key driver genes for complex diseases to improve personalized healthcare.

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets., In this manuscript, we present JWES, a newly developed FAIR bioinformatics‐genomics pipeline for gene‐variant discovery, annotation, prediction, and genotyping, and big data modelling and visualization. JWES is an open‐source and freely available application, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge.

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.

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

Despite significant scientific and medical discoveries, the genetics of novel infectious diseases like COVID-19 remains far from understanding. SARS-CoV-2 is a single-stranded RNA respiratory virus that causes COVID-19 by binding to the ACE2 receptor in the lung and other organs. Understanding its clinical presentation and metabolomic and genetic profile will lead to the discovery of diagnostic, prognostic and predictive biomarkers, which may lead to more effective medical therapy. It is important to investigate correlations and overlap between reported diagnoses of a patient with COVID-19 in clinical data with identified germline and somatic mutations, and highly expressed genes from genomics data analysis. Timely model clinical, genomics and metabolomics data to find statistical patterns across millions of features to identify underlying biological pathways, modifiable risk factors and actionable information that supports early detection and prevention of COVID-19, and development of new therapies for better patient care. Next, ensuring security reconcile noise, need to build and train machine learning prognostic models to find actionable information that supports early detection and prevention of COVID-19. Based on the myriad data, applying appropriate machine learning algorithms to stratify patients, understand scenarios, optimise decision-making, identify high-risk rare variants (including ACE2, TMPRSS2) and making medically relevant predictions. Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments, when confronted with the challenges of pandemic situations.

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.

Lung cancer is one of the most frequently diagnosed cancers with higher incidence and mortality rates in African-American (AA) men compared to European-American (EA) men. Here, we report high-confidence alternative splicing (AS) events from high-throughput, high-depth total RNA-sequencing of lung tumors and non-tumor adjacent tissues (NATs) from two independent cohorts of patients with adenocarcinoma and squamous cell carcinoma. We uncover tumor subtype- and population-specific AS events different between AAs and EAs, associated with oncogenic signaling pathways, cell surface proteins, and cancer driver genes. We highlight significant AS events in SYNE2 specific to LUAD in both populations as well as those in CD44 from EAs and TMBIM6 from AAs specific to LUAD. Our large survey of lung tumors presents a rich data resource that can help uncover new therapeutic vulnerabilities that potentially advance health equity.

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 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. Results Our 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. Conclusions We 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.

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.

Precision medicine is one of the recent and powerful developments in medical care, which has the potential to improve the traditional symptom-driven practice of medicine, allowing earlier interventions using advanced diagnostics and tailoring better and economically personalized treatments. Identifying the best pathway to personalized and population medicine involves the ability to analyze comprehensive patient information together with broader aspects to monitor and distinguish between sick and relatively healthy people, which will lead to a better understanding of biological indicators that can signal shifts in health. While the complexities of disease at the individual level have made it difficult to utilize healthcare information in clinical decision-making, some of the existing constraints have been greatly minimized by technological advancements. To implement effective precision medicine with enhanced ability to positively impact patient outcomes and provide real-time decision support, it is important to harness the power of electronic health records by integrating disparate data sources and discovering patient-specific patterns of disease progression. Useful analytic tools, technologies, databases, and approaches are required to augment networking and interoperability of clinical, laboratory and public health systems, as well as addressing ethical and social issues related to the privacy and protection of healthcare data with effective balance. Developing multifunctional machine learning platforms for clinical data extraction, aggregation, management and analysis can support clinicians by efficiently stratifying subjects to understand specific scenarios and optimize decision-making. Implementation of artificial intelligence in healthcare is a compelling vision that has the potential in leading to the significant improvements for achieving the goals of providing real-time, better personalized and population medicine at lower costs. In this study, we focused on analyzing and discussing various published artificial intelligence and machine learning solutions, approaches and perspectives, aiming to advance academic solutions in paving the way for a new data-centric era of discovery in healthcare.

To efficiently fathom the wealth of genomics and clinical data, there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus was to create a comprehensive database with mobile access to authentic Single Nucleotide Polymorphisms (SNPs) and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present non-profit, academic and publicly available iOS application, PAS-SNP, which invites global users to freely download it on iPhone & iPad devices, effortlessly adopt it's easy to use interface and search for SNPs, genes and related diseases. PAS-SNP is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses a MySQL server database, which includes over 67,000 SNPs reported for over 19,000 genes, from patients located in over 1000 regions, published in over 3000 articles in over 415 journals available at the PubMed, and over 100,000 classified SNP-disease combinations. It includes SNPs released and organized by the Genome-wide Association Study (GWAS). PAS-SNP is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.

Over the recent years, enormous progress has been made in mapping complex traits in humans. These studies have demonstrated that there is a crucial need to generate appropriate gene-disease annotation repositories accessed through modern resources. Our focus here is to create a comprehensive database with mobile access to authentic somatic mutations in cancer and classified diseases worldwide, considered as the foundation for clinical and genomics research, epidemiology and precision medicine. In this manuscript, we present a non-commercial, academic and publically available iOS app i.e., PAS-MUT, which invites global users to freely download it on iPhone & iPad devices, quickly adopt its easy to use interface and search for somatic mutations, genes and related diseases. PAS-MUT is developed with Swift multi-paradigm programming language, XCODE integrated development environment for MacOS, and PHP scripting that uses MySQL database and web servers. PAS-MUT database includes over a million authentic somatic mutations released global, including available at the COSMIC, CNVD, SwissVar, and ClinVar etc. PAS-MUT is founded on the clinical and scientific premise to support and promote healthcare and genomics data sharing with technological advancements.

Today, we stand on the threshold of the new medical revolution, and learning to read the code of life, and this revolution offers big hope to people suffering from all kinds of diseases. The complexity and variability in disease classification is a great challenge to overcome. Disease classification is routinely composed of healthcare units streaming data from multiple sources simultaneously e.g., pathology, genomics, imaging, and electrophysiology etc. To efficiently fathom the wealth of healthcare, there is a crucial need to generate appropriate disease-treatment annotation repositories accessed through modern technologies. One platform that has proven to be an efficient tool in several areas including healthcare, is the multi-purpose mobile computing devices e.g., smart phone and tablet computer. Our focus here was to develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. Innovative and smart systems are necessary to improve the quality and transition of healthcare by understanding heterogeneous healthcare and related data. In this manuscript, we present PAS-Code, an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. It enables users worldwide to install the application on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. PAS-Code is an exclusively academic application founded on the clinical, scientific, and modern technology premise to support healthcare and enable scientific data retrieval using efficient mobile-based tools. It has the potential to assist clinical researchers by providing enhanced exposure to ICD and NDC with greater visibility and easy browsing. These set of codes are highly significant at every level of healthcare.

BACKGROUND Innovative and smart systems are necessary to help improve the quality and transition of healthcare by studying heterogeneous healthcare and related data. OBJECTIVE Develop a mobile application with a comprehensive database for efficient management and access to International Classification of Diseases (ICD) and National Drug Code (NDC) for epidemiology, health management, clinical support, and scientific research. METHODS PAS-Code is an iOS app developed with Swift and PHP scripting that uses a MySQL server database, which includes over 80,000 ICDs maintained by the World Health Organization and over 123,000 NDCs approved by the US Food and Drug Administration. RESULTS PAS-Code enables users worldwide to install it on Apple mobile devices and search for medical classified codes, disease, drugs, and related information such as, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury. CONCLUSIONS PAS-Code will assist clinicians and researchers with efficient and quick mobile access to disease and drug classifications. PAS-Code is founded on the clinical and scientific premise to support healthcare and scientific data sharing.

Modern high throughput sequencing technologies are enormously contributing to the generation of heterogeneous genomic data of different sizes and kinds. In most of the cases, NGS data is first produced in the raw form, which is then demultiplexed into text based formats, representing nucleotide sequences i.e. FASTA and FASTQ formats for secondary analysis. One of the major challenges for the downstream analysis of amplicon data is to first demultiplex FASTQ files based on the different oligonucleotides barcode combinations. Match & Scratch Barcodes (MSB) are a set of interactive bioinformatics tools that support the analysis of PacBio sequenced long read amplicon data by detecting multiple forward and reverse end adapter sequences, generic adapters attached to the region specific oligoes, multiple number of region specific oligos of variable length for the extraction of sequences of interest. These work with zero mismatch, retain only reads which map exactly to adapters and barcodes, report all sequences matched to both single and paired-end adapters and barcodes, and demultiplex FASTQ files based on the common and distinct barcodes combinations. The performance of MSB has been successfully tested using in-house sequenced non-published and external published datasets, which includes PacBio sequenced long read PDX (Patient-Derived Xenograft) amplicon data embedding multiple barcodes of variable lengths. MSB is user friendly and first interactively designed set of tools to empower non-computational scientists to demultiplex their own datasets and export results in different data formats (CSV, FASTA and FASTQ).

Summary: A major challenge for mass spectrometric-based lipidomics, aiming at describing all lipid species in a biological sample, lies in the computational and bioinformatic processing of the large amount of data that arises after data acquisition. Lipid-Pro is a software tool that supports the identification of lipids by interpreting large datasets generated by liquid chromatography—tandem mass spectrometry (LC–MS/MS) using the advanced data-independent acquisition mode MSE. In the MSE mode, the instrument fragments all molecular ions generated from a sample and records time-resolved molecular ion data as well as fragment ion data for every detectable molecular ion. Lipid-Pro matches the retention time-aligned mass-to-charge ratio data of molecular- and fragment ions with a lipid database and generates a report on all identified lipid species. For generation of the lipid database, Lipid-Pro provides a module for construction of lipid species and their fragments using a flexible building block approach. Hence, Lipid-Pro is an easy to use analysis tool to interpret complex MSE lipidomics data and also offers a module to generate a user-specific lipid database. Availability and implementation: Lipid-Pro is freely available at: http://www.neurogenetics.biozentrum.uni-wuerzburg.de/en/project/services/lipidpro/ Contact: zeeshan.ahmed@uni-wuerzburg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.

Machine learning aims of facilitating complex system data analysis, optimization,classification and predictionwith the use of different mathematical and statistical algorithms. In this research, we are interested in establishing the process of estimating best optimal input parameters to train networks. Using WEKA, this paper implements a classifier with Back-propagation Neural Networks and Genetic Algorithm towards efficient data classification and optimization.The implemented classifier is capable of reading and analyzing a number of populations in giving datasets, and based on the identified population it estimates kinds of species in a population, hidden layers, momentum, accuracy, correct and incorrect instances.

The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application ‘Isotopo’. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of 13C-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments. Availability: The ‘Isotopo’ software is available at the following web link (section Download): http://spp1316.uni-wuerzburg.de/bioinformatics/isotopo/. The package contains three additional files: software executable setup (installer), one data set file (discussed in this article) and one excel file (which can be used to convert data from excel to ‘.iso’ format). The ‘Isotopo’ software is compatible only with the Microsoft Windows operating system. Database URL: http://spp1316.uni-wuerzburg.de/bioinformatics/isotopo/.

Background The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. Results The open-source software “Least Square Mass Isotopomer Analyzer” (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman’s least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. Conclusions The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.