Genomic Data Management Research Articles

SPM4GAC: SPM based approach for genome analysis and classification of macromolecules

Genome sequence analysis and classification play critical roles in properly understanding an organism's main characteristics, functionalities, and changing (evolving) nature. However, the rapid expansion of genomic data makes genome sequence analysis and classification a challenging task due to the high computational requirements, proper management, and understanding of genomic data. Recently proposed models yielded promising results for the task of genome sequence classification. Nevertheless, these models often ignore the sequential nature of nucleotides, which is crucial for revealing their underlying structure and function. To address this limitation, we present SPM4GAC, a sequential pattern mining (SPM)-based framework to analyze and classify the macromolecule genome sequences of viruses. First, a large dataset containing the genome sequences of various RNA viruses is developed and transformed into a suitable format. On the transformed dataset, algorithms for SPM are used to identify frequent sequential patterns of nucleotide bases. The obtained frequent sequential patterns of bases are then used as features to classify different viruses. Ten classifiers are employed, and their performance is assessed by using several evaluation measures. Finally, a performance comparison of SPM4GAC with state-of-the-art methods for genome sequence classification/detection reveals that SPM4GAC performs better than those methods.

A systematic literature review of the 'commercialisation effect' on public attitudes towards biobank and genomic data repositories.

Initiatives that collect and share genomic data to advance health research are widespread and accelerating. Commercial interests in these efforts, while vital, may erode public trust and willingness to provide personal genomic data, upon which these initiatives depend. Understanding public attitudes towards providing genomic data for health research in the context of commercial involvement is critical. A PRISMA-guided search of six online academic databases identified 113 quantitative and qualitative studies using primary data pertaining to public attitudes towards commercial actors in the management, collection, access, and use of biobank and genomic data. The presence of commercial interests yields interrelated public concerns around consent, privacy and data security, trust in science and scientists, benefit sharing, and the ownership and control of health data. Carefully considered regulatory and data governance and access policies are therefore required to maintain public trust and support for genomic health initiatives.

Advances in Molecular Breeding of Forage Crops: Technologies, Applications and Prospects

Molecular breeding has revolutionized the improvement of forage crops by offering precise tools to enhance the yield, quality, and environmental resilience. This review provides a comprehensive overview of the current technologies, applications, and future directions in the field of forage crop molecular breeding. Technological advancements in the field, including Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWASs), genomic selection (GS), and genome-editing tools such as CRISPR-Cas9, have significantly advanced the identification and incorporation of beneficial traits into forage species. These approaches have dramatically shortened the breeding cycles and increased the efficiency of developing cultivars with improved yield, disease resistance, stress tolerance, and nutritional profiles. The implementation of these technologies has led to notable successes, as demonstrated by case studies on various forage crops, showcasing enhanced forage quality and adaptability to challenging environmental conditions. Furthermore, the integration of high-throughput phenotyping with advanced bioinformatics tools has streamlined the management of large-scale genomic data, facilitating more precise selection and breeding decisions. Looking ahead, this review explores the potential of emerging technologies, such as the application of artificial intelligence in predictive breeding, along with the associated ethical and regulatory considerations. While we stand to gain benefit from these innovations, the future of molecular breeding in forage crops must also confront the challenges posed by climate change and the imperative of sustainable agricultural practices. This review concludes by emphasizing the transformative impact of molecular breeding on the improvement of forage crop and the critical need for ongoing research and collaboration to fully realize its potential.

ReUseData: an R/Bioconductor tool for reusable and reproducible genomic data management

BackgroundThe increasing volume and complexity of genomic data pose significant challenges for effective data management and reuse. Public genomic data often undergo similar preprocessing across projects, leading to redundant or inconsistent datasets and inefficient use of computing resources. This is especially pertinent for bioinformaticians engaged in multiple projects. Tools have been created to address challenges in managing and accessing curated genomic datasets, however, the practical utility of such tools becomes especially beneficial for users who seek to work with specific types of data or are technically inclined toward a particular programming language. Currently, there exists a gap in the availability of an R-specific solution for efficient data management and versatile data reuse.ResultsHere we present ReUseData, an R software tool that overcomes some of the limitations of existing solutions and provides a versatile and reproducible approach to effective data management within R. ReUseData facilitates the transformation of ad hoc scripts for data preprocessing into Common Workflow Language (CWL)-based data recipes, allowing for the reproducible generation of curated data files in their generic formats. The data recipes are standardized and self-contained, enabling them to be easily portable and reproducible across various computing platforms. ReUseData also streamlines the reuse of curated data files and their integration into downstream analysis tools and workflows with different frameworks.ConclusionsReUseData provides a reliable and reproducible approach for genomic data management within the R environment to enhance the accessibility and reusability of genomic data. The package is available at Bioconductor (https://bioconductor.org/packages/ReUseData/) with additional information on the project website (https://rcwl.org/dataRecipes/).

GSC: efficient lossless compression of VCF files with fast query.

With the rise of large-scale genome sequencing projects, genotyping of thousands of samples has produced immense variant call format (VCF) files. It is becoming increasingly challenging to store, transfer, and analyze these voluminous files. Compression methods have been used to tackle these issues, aiming for both high compression ratio and fast random access. However, existing methods have not yet achieved a satisfactory compromise between these 2 objectives. To address the aforementioned issue, we introduce GSC (Genotype Sparse Compression), a specialized and refined lossless compression tool for VCF files. In benchmark tests conducted across various open-source datasets, GSC showcased exceptional performance in genotype data compression. Compared with the industry's most advanced tools (namely, GBC and GTC), GSC achieved compression ratios that were higher by 26.9% to 82.4% over GBC and GTC on the datasets, respectively. In lossless compression scenarios, GSC also demonstrated robust performance, with compression ratios 1.5× to 6.5× greater than general-purpose tools like gzip, zstd, and BCFtools-a mode not supported by either GBC or GTC. Achieving such high compression ratios did require some reasonable trade-offs, including longer decompression times, with GSC being 1.2× to 2× slower than GBC, yet 1.1× to 1.4× faster than GTC. Moreover, GSC maintained decompression query speeds that were equivalent to its competitors. In terms of RAM usage, GSC outperformed both counterparts. Overall, GSC's comprehensive performance surpasses that of the most advanced technologies. GSC balances high compression ratios with rapid data access, enhancing genomic data management. It supports seamless PLINK binary format conversion, simplifying downstream analysis.

Assessment of Knowledge, Attitude, and Practices on Genetic Research Data Reuse for Future Research: Clinical Trial Investigators Perspectives

ABSTRACT Introduction: According to the principle of good clinical practice, it is imperative that the personnel involved in the pharmaceutical or genetic research possess an educational background and comprehensive training. The clinical trial investigator must ensure the safeguard the privacy and confidentiality and prevent inadvertent reuse of research results of study participants. Aim: The objective of the current research is to assess the knowledge, attitude, and practices of clinical trial investigators concerning the reuse of genetic research data. Settings and Design: As a cross-sectional study, there was only one-time collection of knowledge, attitude, and practice on genetics and its data for future research from clinical trial investigators from the Tertiary Care Hospital and Clinical Research Centers in Belagavi Karnataka, India. Subjects and Methods: A study was conducted using validated knowledge, attitude, and practices questionnaire on the clinical trial investigators. Statistical Analysis Used: Data were analyzed using the SPSS software version 21one-way analysis of variance. Results: The present study was comprised n = 50 clinical trial investigators calculated by using the general formula for the calculation of sample size with a confidence of interval limit, of whom 64% exhibited awareness of genetic data reuse in clinical trials. Seventy-four percent of clinical trial investigators expressed concerns about confidentiality issues related to the reuse of genomic data. Conclusions: The lack of synchronized international conference on harmonization of technical requirements for pharmaceuticals for human use (ICH) guidelines directives concerning genomic sampling and data management in health-care research presents a formidable obstacle for pharmaceutical sponsors in achieving consistent and standardized genomic research samples and its data execution in pharmaceutical research on a global scale.

Roadmap for implementation of genomics in healthcare: towards equity in access to genomics

Abstract Issue Implementing genomic medicine in healthcare can bring us closer to making personalised medicine a reality, with notable socioeconomic benefits. Description of the problem The 1+Million Genomes Initiative is promoting a reflection in European countries regarding their national strategies for genomic medicine, and how these contribute to research and improve clinical practice. Implementation of genomic medicine is complex and requires investment and adjustments in the organisation and practices of healthcare services, including technical infrastructure for genomic data generation and management, a trained healthcare workforce, and appropriate ethical and legal frameworks. Methods To promote the implementation of genomic medicine in healthcare systems, and improve its societal impact, the Beyond 1+MG (B1MG) project organised three country exchange visits for capacity building, developed and validated in real world settings a comprehensive Maturity Level Model (B1MG MLM) for genomic medicine, and organized workshops on health economics models for genomics. Results The products resulting from these activities provide policy recommendations for adoption of genomic medicine, an innovative MLM Toolkit for health systems to self-assess their current maturity status regarding genomic medicine implementation, and plan the path for improvement, as well as several brief papers highlighting main challenges and key indicators for health economic evaluation of genomic tests. Overall, this set of B1MG products provide a Playbook to support the development of national or regional roadmaps for the implementation of genomic medicine in healthcare systems. Lessons Country healthcare systems have specific practices, barriers and constraints, so the actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system. Key messages • The B1MG Roadmap will support the definition of strategies for adoption of genomics in healthcare systems, towards equity in the access to genomic medicine for all European citizens. • Country healthcare systems actions needed for progress in genomic medicine implementation towards optimization are specific to the context of each healthcare system.

Journeying towards best practice data management in biodiversity genomics.

Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximize the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognize the growing need for guidance on comprehensive data management practices that minimize the risks of data loss, maximize efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting differing user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximize research impact and outcomes.

EXPLORING THE BUILDING BLOCKS OF LIFE: THE HUMAN GENOME PROJECT, ITS IMPACT IN TODAYS WORLD

The Human Genome Projects (HGP) conclusion altered a number of disciplines, including biotechnology, genetics, medicine, and forensics. Key outcomes included personalised medicine, gene therapy, and enhanced DNA profiling. The HGP increased our knowledge of hereditary illnesses and shed light on human evolution. The privacy of genetic information and the ethical application of knowledge were raised. The management and analysis of genomic data require the use of bioinformatics. The future of genetics and biology will be shaped by the HGPs substantial effects on healthcare, innovation, and the economy. In vitro fertilisation and preimplantation genetic diagnosis are two reproductive technology developments made possible by the HGP. Also, it sparked the creation of focused treatments for genetic diseases, completely altering the range of patient treatment options. These advancements have led to a new era of precision medicine and personalised healthcare, in addition to improving patient outcomes.

Overcoming barriers to adoption of precision medicine in community oncology centers in the rural southeastern United States using novel informatics tools and academic collaborations.

e13514 Background: Comprehensive genomic profiling (CGP) is an important tool to identify actionable biomarkers and guide cancer therapy. However, patient (pt) race, socioeconomic status, and clinical setting are associated with lower access to CGP, targeted therapies, and clinical trials. To address this, we expanded a novel framework for genomic data management and clinical decision support developed at a large academic medical center to a network of affiliated community cancer centers located in rural, traditionally underserved communities. Methods: The Duke Precision Cancer Medicine Initiative (PCMI) developed a centralized database to aggregate and normalize CGP results. This database enables CGP review by a multi-disciplinary Molecular Tumor Board (MTB). PCMI established a protocol that allows the MTB to review CGP results and relevant clinical data for consented patients. This protocol opened enrollment at four DCN-affiliated cancer centers in rural North Carolina. Clinical recommendations are captured in a standard note and returned to DCN providers following each review. Our protocol also includes an anonymous survey sent to oncology providers asking about barriers to access and utilization of CGP. Results: Between Jan 2022 and Jan 2023, 62 pts were enrolled and had CGP results reviewed by the MTB; 13 pts (21.0%) were Native American and 20 (32.3%) were Black. 61 actionable genomic alterations were identified from 38 pts, including 16 (25.6%) that molecularly matched clinical trial inclusion criteria. Most DCN providers reported being comfortable ordering (14/20) CGP though fewer expressed comfort with interpretation (9/20) CGP; provider-reported barriers included cost/insurance for targeted therapies (12/20) and distance to clinical trials (14/20). Conclusions: Successful academic-community partnerships to expand access to CGP, clinical trials, and targeted therapies are feasible when supported by fit-for-purpose informatics tools and scalable workflows. Additional work is required to develop comprehensive solutions that ensure equal access to CGP and precision medicine for all patients. [Table: see text]

OpenGenomeBrowser: a versatile, dataset-independent and scalable web platform for genome data management and comparative genomics

BackgroundAs the amount of genomic data continues to grow, there is an increasing need for systematic ways to organize, explore, compare, analyze and share this data. Despite this, there is a lack of suitable platforms to meet this need.ResultsOpenGenomeBrowser is a self-hostable, open-source platform to manage access to genomic data and drastically simplifying comparative genomics analyses. It enables users to interactively generate phylogenetic trees, compare gene loci, browse biochemical pathways, perform gene trait matching, create dot plots, execute BLAST searches, and access the data. It features a flexible user management system, and its modular folder structure enables the organization of genomic data and metadata, and to automate analyses. We tested OpenGenomeBrowser with bacterial, archaeal and yeast genomes. We provide a docker container to make installation and hosting simple. The source code, documentation, tutorials for OpenGenomeBrowser are available at opengenomebrowser.github.io and a demo server is freely accessible at opengenomebrowser.bioinformatics.unibe.ch.ConclusionsTo our knowledge, OpenGenomeBrowser is the first self-hostable, database-independent comparative genome browser. It drastically simplifies commonly used bioinformatics workflows and enables convenient as well as fast data exploration.

HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.

The HL7® fast healthcare interoperability resources (FHIR®) specification has emerged as the leading interoperability standard for the exchange of healthcare data. We conducted a scoping review to identify trends and gaps in the use of FHIR for clinical research. We reviewed published literature, federally funded project databases, application websites, and other sources to discover FHIR-based papers, projects, and tools (collectively, "FHIR projects") available to support clinical research activities. Our search identified 203 different FHIR projects applicable to clinical research. Most were associated with preparations to conduct research, such as data mapping to and from FHIR formats (n = 66, 32.5%) and managing ontologies with FHIR (n = 30, 14.8%), or post-study data activities, such as sharing data using repositories or registries (n = 24, 11.8%), general research data sharing (n = 23, 11.3%), and management of genomic data (n = 21, 10.3%). With the exception of phenotyping (n = 19, 9.4%), fewer FHIR-based projects focused on needs within the clinical research process itself. Funding and usage of FHIR-enabled solutions for research are expanding, but most projects appear focused on establishing data pipelines and linking clinical systems such as electronic health records, patient-facing data systems, and registries, possibly due to the relative newness of FHIR and the incentives for FHIR integration in health information systems. Fewer FHIR projects were associated with research-only activities. The FHIR standard is becoming an essential component of the clinical research enterprise. To develop FHIR's full potential for clinical research, funding and operational stakeholders should address gaps in FHIR-based research tools and methods.

Leveraging Algorithms to Improve Decision-Making Workflows for Genomic Data Access and Management.

Studies on the ethics of automating clinical or research decision making using artificial intelligence and other algorithmic tools abound. Less attention has been paid, however, to the scope for, and ethics of, automating decision making within regulatory apparatuses governing the access, use, and exchange of data involving humans for research. In this article, we map how the binary logic flows and real-time capabilities of automated decision support (ADS) systems may be leveraged to accelerate one rate-limiting step in scientific discovery: data access management. We contend that improved auditability, consistency, and efficiency of the data access request process using ADS systems have the potential to yield fairer outcomes in requests for data largely sourced from biospecimens and biobanked samples. This procedural justice rationale reinforces a broader set of participant and data subject rights that data access committees (DACs) indirectly protect. DACs protect the rights of citizens to benefit from science by bringing researchers closer to the data they need to advance that science. DACs also protect the informational dignities of individuals and communities by ensuring the data being accessed are used in ways consistent with participant values. We discuss the development of the Global Alliance for Genomics and Health Data Use Ontology standard as a test case of ADS for genomic data access management specifically, and we synthesize relevant ethical, legal, and social challenges to its implementation in practice. We conclude with an agenda of future research needed to thoughtfully advance strategies for computational governance that endeavor to instill public trust in, and maximize the scientific value of, health-related human data across data types, environments, and user communities.

EasyGDB: a low-maintenance and highly customizable system to develop genomics portals.

SummaryEasyGDB is an easy-to-implement low-maintenance tool developed to create genomic data management web platforms. It can be used for any species, group of species, or multiple genome or annotation versions. EasyGDB provides a framework to develop a web portal that includes the general information about species, projects and members, and bioinformatics tools such as file downloads, BLAST, genome browser, annotation search, gene expression visualization, annotation and sequence download, and gene ids and orthologs lookup. The code of EasyGDB facilitates data maintenance and update for non-experienced bioinformaticians, using BLAST databases to store and retrieve sequence data in gene annotation pages and bioinformatics tools, and JSON files to customize metadata. EasyGDB is a highly customizable tool. Any section and tool can be enabled or disabled like a switch through a single configuration file. This tool aims to simplify the development of genomics portals in non-model species, providing a modern web style with embedded interactive bioinformatics tools to cover all the common needs derived from genomics projects.Availability and implementation The code and manual to use EasyGDB can be found at https://github.com/noefp/easy_gdb.

Challenges and Gaps in Clinical Trial Genomic Data Management.

Challenges and Gaps in Clinical Trial Genomic Data Management.

Blockchain-Based Privacy-Preserving System for Genomic Data Management Using Local Differential Privacy

The advances made in genome technology have resulted in significant amounts of genomic data being generated at an increasing speed. As genomic data contain various privacy-sensitive information, security schemes that protect confidentiality and control access are essential. Many security techniques have been proposed to safeguard healthcare data. However, these techniques are inadequate for genomic data management because of their large size. Additionally, privacy problems due to the sharing of gene data are yet to be addressed. In this study, we propose a secure genomic data management system using blockchain and local differential privacy (LDP). The proposed system employs two types of storage: private storage for internal staff and semi-private storage for external users. In private storage, because encrypted gene data are stored, only internal employees can access the data. Meanwhile, in semi-private storage, gene data are irreversibly modified by LDP. Through LDP, different noises are added to each section of the genomic data. Therefore, even though the third party uses or exposes the shared data, the owner’s privacy is guaranteed. Furthermore, the access control for each storage is ensured by the blockchain, and the gene owner can trace the usage and sharing status using a decentralized application in a mobile device.

Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data

The management of the exponential growth of data that Next Generation Sequencing techniques produce has become a challenge for researchers that are forced to delve into an ocean of complex data in order to extract new insights to unravel the secrets of human diseases. Initially, this can be faced as a Big Data-related problem, but the genomic data have particular and relevant challenges that make them different from other Big Data working domains. Genomic data are much more heterogeneous; they are spread in hundreds of repositories, represented in multiple formats, and have different levels of quality. In addition, getting meaningful conclusions from genomic data requires considering all of the relevant surrounding knowledge that is under continuous evolution. In this scenario, the precise identification of what makes Genome Data Management so different is essential in order to provide effective Big Data-based solutions. Genomic projects require dealing with the technological problems associated with data management, nomenclature standards, and quality issues that only robust Information Systems that use Big Data techniques can provide. The main contribution of this paper is to present a Big Data-driven approach for managing genomic data, that is adapted to the particularities of the domain and to show its applicability to improve genetic diagnoses, which is the core of the development of accurate Precision Medicine.

An Introduction to MPEG-G: The First Open ISO/IEC Standard for the Compression and Exchange of Genomic Sequencing Data

The development and progress of high-throughput sequencing technologies have transformed the sequencing of DNA from a scientific research challenge to practice. With the release of the latest generation of sequencing machines, the cost of sequencing a whole human genome has dropped to less than $\$ $ 600. Such achievements open the door to personalized medicine, where it is expected that genomic information of patients will be analyzed as a standard practice. However, the associated costs, related to storing, transmitting, and processing the large volumes of data, are already comparable to the costs of sequencing. To support the design of new and interoperable solutions for the representation, compression, and management of genomic sequencing data, the Moving Picture Experts Group (MPEG) jointly with working group 5 of ISO/TC276 “Biotechnology” has started to produce the ISO/IEC 23092 series, known as MPEG-G. MPEG-G does not only offer higher levels of compression compared with the state of the art but it also provides new functionalities, such as built-in support for random access in the compressed domain, support for data protection mechanisms, flexible storage, and streaming capabilities. MPEG-G only specifies the decoding syntax of compressed bitstreams, as well as a file format and a transport format. This allows for the development of new encoding solutions with higher degrees of optimization while maintaining compatibility with any existing MPEG-G decoder.

Sharing genomic data from clinical testing with researchers: public survey of expectations of clinical genomic data management in Queensland, Australia

BackgroundThere has been considerable investment and strategic planning to introduce genomic testing into Australia’s public health system. As more patients’ genomic data is being held by the public health system, there will be increased requests from researchers to access this data. It is important that public policy reflects public expectations for how genomic data that is generated from clinical tests is used. To inform public policy and discussions around genomic data sharing, we sought public opinions on using genomic data contained in medical records for research purposes in the Australian state of Queensland.MethodsA total of 1494 participants completed an online questionnaire between February and May 2019. Participants were adults living in Australia. The questionnaire explored participant preferences for sharing genomic data or biological samples with researchers, and concerns about genomic data sharing.ResultsMost participants wanted to be given the choice to have their genomic data from medical records used in research. Their expectations on whether and how often they needed to be approached for permission on using their genomic data, depended on whether the data was identifiable or anonymous. Their willingness to sharing data for research purposes depended on the type of information being shared, what type of research would be undertaken and who would be doing the research. Participants were most concerned with genomics data sharing that could lead to discrimination (insurance and employment), data being used for marketing, data security, or commercial use.ConclusionsMost participants were willing to share their genomic data from medical records with researchers, as long as permission for use was sought. However, the existing policies related to this process in Queensland do not reflect participant expectations for how this is achieved, particularly with anonymous genomics data. This inconsistency may be addressed by process changes, such as inclusion of research in addition to clinical consent or general research data consent programs.

OpenGDC: Unifying, Modeling, Integrating Cancer Genomic Data and Clinical Metadata

Next Generation Sequencing technologies have produced a substantial increase of publicly available genomic data and related clinical/biospecimen information. New models and methods to easily access, integrate and search them effectively are needed. An effort was made by the Genomic Data Commons (GDC), which defined strict procedures for harmonizing genomic and clinical data of cancer, and created the GDC data portal with its application programming interface (API). In this work, we enhance GDC harmonization by applying a state of the art data model (called Genomic Data Model) made of two components: the genomic data, in Browser Extensible Data (BED) format, and the related metadata, in a tab-delimited key-value format. Furthermore, we extend the GDC genomic data with information extracted from other public genomic databases (e.g., GENCODE, HGNC and miRBase). For metadata, we implemented automatic procedures to extract and normalize them, recognizing and eliminating redundant ones, from both Clinical/Biospecimen Supplements and GDC Data Model, that are present on the two sources of GDC (i.e., data portal and API). We developed and released the OpenGDC software, which is able to extract, integrate, extend, and standardize genomic and clinical data of The Cancer Genome Atlas (TCGA) from the GDC. Additionally, we created a publicly accessible repository, containing such homogenized and enhanced TCGA data (resulting in about 1.3 TB). Our approach, implemented in the OpenGDC software, provides a step forward to the effective and efficient management of big genomic and clinical data of cancer. The strong usability of our data model and utility of our work is demonstrated through the application of the GenoMetric Query Language (GMQL) on the transformed TCGA data from the GDC, achieving promising results, facilitating information retrieval and knowledge discovery analyses.