Interpretation Of Genomic Data Research Articles

Genomic Epidemiology of Antimicrobial Resistance: Tracking the Global Spread of Resistant Pathogens

The rapid development and spread of antimicrobial resistance have been one of the most prominent health crises globally, which significantly challenges treating infectious diseases. This paper will address the contribution that genomic epidemiology makes to track and understand the global spread of antimicrobial-resistant pathogens. Genomic epidemiology, an area combining pathogen genomics with epidemiological data, allows for an extremely powerful approach in the identification of resistance mechanisms and tracing transmission routes for guiding public health interventions. This review Emphasizes major bacterial pathogens, such as Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae, which harbor major resistance genes: blaCTX-M, mecA, and blaKPC. These genes contribute to the worldwide dissemination of resistance, mainly through mechanisms involving horizontal gene transfer and selective antibiotic pressure. Thus, genomic surveillance, phylogenetic analysis, and bioinformatics approaches allow for the pinpointing of transmission hotspots, mapping of resistance gene distribution, and tracking of emerging resistant strains in real time. It also provides information on how genomic data are being used to improve antimicrobial stewardship programs and inform clinical treatment decisions. Public health responses can further be tailored by recognizing regional resistance patterns, hence enhancing interventions. However, several challenges persist—for example, the need for standardized sharing of data, interpretation of genomic data, and integration of these insights into public health systems, especially in low-resource settings. This abstract highlights the transformative impact of genomic epidemiology in the management of the AMR crisis while emphasizing the continued need for global cooperation, enhanced data infrastructures, and policy initiatives to ensure the effective use of genomic data in public health.

B-119 Comparative performance of GPT-4 and CNV-ETLAI in extracting copy number variations from medical journals: Bridging the gap between large language models and specialized NLP tools in genomic data interpretation

Abstract Background Copy Number Variations (CNVs) are critical genetic markers in diversity and disease, yet their accurate extraction from medical literature remains challenging due to the complexity of genetic data. While specialized NLP models like CNV-ETLAI have been developed for this task, the advent of Large Language Models (LLMs) such as GPT-4 presents a potential alternative with broader applicability. This study evaluates the efficacy of GPT-4 against CNV-ETLAI in extracting CNVs from medical journal articles, aiming to enhance genetic research and clinical decision-making. Methods We configured GPT-4 to process and interpret medical journal PDFs, designing custom prompts for CNV information extraction. The performance of GPT-4 was benchmarked against CNV-ETLAI using a dataset of 146 true positive CNVs extracted from 23 journal articles. Performance metrics focused on accuracy in extracting CNVs from both text and tables, recognizing the importance of structured data interpretation in genomic analysis. Results CNV-ETLAI demonstrated superior accuracy, achieving a 98% success rate in CNV extraction, compared to GPT-4’s 49%. Specifically, CNV-ETLAI outperformed GPT-4 in table extraction accuracy (99% vs. 41.2%) and context extraction accuracy (96% vs. 63.2%). Despite GPT-4's lower performance, its capacity for improvement and adaptability was noted, indicating potential future applicability in medical data extraction. Conclusions The study highlights CNV-ETLAI's current superiority in extracting CNVs from medical texts, particularly in interpreting structured data like tables. However, the adaptability and potential for growth in LLMs like GPT-4 suggest they could soon become valuable tools for medical data extraction, offering a more versatile and powerful solution across a broader range of applications. The promise of LLMs, despite their current limitations, underscores the need for continued research and development in AI technologies for genomic data interpretation.

A Statistical Methodology for Evaluating Asymmetry after Normalization with Application to Genomic Data

This study evaluates the symmetry of data distributions after normalization, focusing on various statistical tests, including a few explored test named Rp. We apply normalization techniques, such as variance stabilizing transformations, to ribonucleic acid sequencing data with varying sample sizes to assess their effectiveness in achieving symmetric data distributions. Our findings reveal that while normalization generally induces symmetry, some samples retain asymmetric distributions, challenging the conventional assumption of post-normalization symmetry. The Rp test, in particular, shows superior performance when there are variations in sample size and data distribution, making it a preferred tool for assessing symmetry when applied to genomic data. This finding underscores the importance of validating symmetry assumptions during data normalization, especially in genomic data, as overlooked asymmetries can lead to potential inaccuracies in downstream analyses. We analyze postmortem lateral temporal lobe samples to explore normal aging and Alzheimer’s disease, highlighting the critical role of symmetry testing in the accurate interpretation of genomic data.

Meaning, concept and application of bioinformatics

Bioinformatics, originating from the term coined in 1979 by Paulien Hogeweg and Ben Hesper, is a burgeoning interdisciplinary field at the nexus of biology, computer science, and information technology. It encompasses the collection, classification, storage, and analysis of vast biological datasets using computational methods, aiming to unravel the complexities of biological systems. This abstract provides a comprehensive overview of bioinformatics, emphasizing its definition, scope, branches, aims, research areas, databases, and applications, particularly highlighting its role in drug discovery through structural bioinformatics. Bioinformatics integrates diverse branches such as computational biology, genomics, proteomics, structural biology, systems biology, pharmacogenomics, and bioprogramming. Each branch applies mathematical modeling, molecular interactions analysis, or genomic data interpretation to advance understanding in biology and medicine. Key aims include applying advanced computational technologies to biological problems, presenting complex data clearly, and providing robust statistical tools for genomic analysis. The field also fosters collaborations across academic, commercial, and government sectors to leverage resources and expertise. Research areas span computational evolutionary analysis, genome annotation, gene expression analysis, cancer mutation studies, and comparative genomics, illuminating disease mechanisms and evolutionary relationships. Bioinformatics databases play a crucial role by storing and organizing biological data. Primary repositories like GenBank and the Protein Data Bank (PDB), secondary databases such as SWISS-Prot, and specialized databases support research and clinical applications by providing access to genomic, proteomic, and structural information. In drug discovery, structural bioinformatics techniques like homology modeling, molecular docking, and simulations predict protein structures, analyze interactions, and aid in designing therapies. These computational methods accelerate drug development, optimize drug-target interactions, and reduce costs associated with bringing new drugs to market. Overall, bioinformatics drives innovation in biological sciences and healthcare by leveraging computational methods to interpret biological data. As technology advances, bioinformatics will continue to play a pivotal role in addressing global health challenges, advancing personalized medicine, and facilitating groundbreaking discoveries in biology and medicine. The evolving field of bioinformatics promises continued growth and impact, shaping the future of biological research and its applications in improving human health and understanding the natural world.

Meaning, Concept and Application of Bioinformatics

Bioinformatics, originating from the term coined in 1979 by Paulien Hogeweg and Ben Hesper, is a burgeoning interdisciplinary field at the nexus of biology, computer science, and information technology. It encompasses the collection, classification, storage, and analysis of vast biological datasets using computational methods, aiming to unravel the complexities of biological systems. This abstract provides a comprehensive overview of bioinformatics, emphasizing its definition, scope, branches, aims, research areas, databases, and applications, particularly highlighting its role in drug discovery through structural bioinformatics. Bioinformatics integrates diverse branches such as computational biology, genomics, proteomics, structural biology, systems biology, pharmacogenomics, and bioprogramming. Each branch applies mathematical modeling, molecular interactions analysis, or genomic data interpretation to advance understanding in biology and medicine. Key aims include applying advanced computational technologies to biological problems, presenting complex data clearly, and providing robust statistical tools for genomic analysis. The field also fosters collaborations across academic, commercial, and government sectors to leverage resources and expertise. Research areas span computational evolutionary analysis, genome annotation, gene expression analysis, cancer mutation studies, and comparative genomics, illuminating disease mechanisms and evolutionary relationships. Bioinformatics databases play a crucial role by storing and organizing biological data. Primary repositories like GenBank and the Protein Data Bank (PDB), secondary databases such as SWISS-Prot, and specialized databases support research and clinical applications by providing access to genomic, proteomic, and structural information. In drug discovery, structural bioinformatics techniques like homology modeling, molecular docking, and simulations predict protein structures, analyze interactions, and aid in designing therapies. These computational methods accelerate drug development, optimize drug-target interactions, and reduce costs associated with bringing new drugs to market. Overall, bioinformatics drives innovation in biological sciences and healthcare by leveraging computational methods to interpret biological data. As technology advances, bioinformatics will continue to play a pivotal role in addressing global health challenges, advancing personalized medicine, and facilitating groundbreaking discoveries in biology and medicine. The evolving field of bioinformatics promises continued growth and impact, shaping the future of biological research and its applications in improving human health and understanding the natural world. Keywords: Bioinformatics, Computational biology, Structural bioinformatics, Drug discovery, Genomics.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.

3Dmapper: a command line tool for BioBank-scale mapping of variants to protein structures.

The interpretation of genomic data is crucial to understand the molecular mechanisms of biological processes. Protein structures play a vital role in facilitating this interpretation by providing functional context to genetic coding variants. However, mapping genes to proteins is a tedious and error-prone task due to inconsistencies in data formats. Over the past two decades, numerous tools and databases have been developed to automatically map annotated positions and variants to protein structures. However, most of these tools are web-based and not well-suited for large-scale genomic data analysis. To address this issue, we introduce 3Dmapper, a stand-alone command-line tool developed in Python and R. It systematically maps annotated protein positions and variants to protein structures, providing a solution that is both efficient and reliable. https://github.com/vicruiser/3Dmapper.

Comparing Classifications from Multiple Variant Annotation Software Solutions Using Real-World Next Generation Sequencing Data from Oncology Testing

Variant annotation is an important step in deciphering the functional impact of genomic variants and their association with diseases. In this study, we analyzed 80 pan-cancer cases that underwent comprehensive genomic testing and compared the auto-classified variant tiers among four globally-available software solutions for variant interpretation from Roche, SOPHiA GENETICS, QIAGEN, and Genoox. The results revealed striking differences in tier classifications, which are believed to be a result of several factors, including subjectivity in the AMP/ASCO/CAP guidelines, threshold settings for variant allele frequencies and population allele frequencies, as well as variation in disease ontologies. Although the software tools described here provide a time-saving and repeatable process for interpretation of genomic data, it is crucial to understand the nuances and various settings for these solutions, as they can strongly influence variant tier classifications and downstream management.

LimoRhyde2: Genomic analysis of biological rhythms based on effect sizes.

Genome-scale data have revealed daily rhythms in various species and tissues. However, current methods to assess rhythmicity largely restrict their focus to quantifying statistical significance, which may not reflect biological relevance. To address this limitation, we developed a method called LimoRhyde2 (the successor to our method LimoRhyde), which focuses instead on rhythm-related effect sizes and their uncertainty. For each genomic feature, LimoRhyde2 fits a curve using a series of linear models based on periodic splines, moderates the fits using an Empirical Bayes approach called multivariate adaptive shrinkage (Mash), then uses the moderated fits to calculate rhythm statistics such as peak-to-trough amplitude. The periodic splines capture non-sinusoidal rhythmicity, while Mash uses patterns in the data to account for different fits having different levels of noise. To demonstrate LimoRhyde2's utility, we applied it to multiple circadian transcriptome datasets. Overall, LimoRhyde2 prioritized genes having high-amplitude rhythms in expression, whereas a prior method (BooteJTK) prioritized "statistically significant" genes whose amplitudes could be relatively small. Thus, quantifying effect sizes using approaches such as LimoRhyde2 has the potential to transform interpretation of genomic data related to biological rhythms.

Ancestry, race and ethnicity: the role and relevance of language in clinical genetics practice

BackgroundThe terms ancestry, race and ethnicity are used variably within the medical literature and within society and clinical care. Biological lineage can provide an important context for the interpretation of...

Species conservation in the era of genomic science.

The exponential increase in the availability of genomic data, derived from sequencing thousands of loci or whole genomes, provides exciting new insights into the diversity of life. However, it can also challenge established species concepts and existing management regimes derived from these concepts. Genomic data can help inform decisions about how to manage genetic diversity, but policies that protect identified taxonomic entities can generate conflicting recommendations that create challenges for practitioners. We outline three dimensions of management concern that arise when facing new and potentially conflicting interpretations of genomic data: defining conservation entities, deciding how to manage diversity, and evaluating the risks and benefits of management actions. We highlight the often-underappreciated role of values in influencing management choices made by individuals, scientists, practitioners, the public, and other stakeholders. Such values influence choices through mechanisms such as the Rashomon effect, whereby management decisions are complicated by conflicting perceptions of the causes and consequences of the conservation problem. To illustrate how this might operate, we offer a hypothetical example of this effect for the interpretation of genomic data and its implications for conservation management. Such value-based decisions can be challenged by the rigidity of existing management regimes, making it difficult to achieve the necessary flexibility to match the changing biological understanding. We finish by recommending that both conservation geneticists and practitioners reflect on their respective values, responsibilities, and roles in building a more robust system of species management. This includes embracing the inclusion of stakeholders in decision-making because, as in many cases, there are not objectively defensible right or wrong decisions.

Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria.

Since the discovery of the second chromosome in the Rhodobacter sphaeroides 2.4.1 by Suwanto and Kaplan in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. This phenomenon shattered the dogma of a unipartite genome (a single circular chromosome) in bacteria. Recently, Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL) have emerged as powerful tools in the investigation of big data in a plethora of disciplines to decipher complex patterns in these data, including the large-scale analysis and interpretation of genomic data. An important inquiry in bacteriology pertains to the genetic factors that underlie the structural evolution of multipartite and unipartite bacterial species. Towards this goal, here we have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of, in general, bacteria with multipartite genomes and bacteria with unipartite genomes. In this study, deploying ML algorithms yielded two gene lists of interest: one that contains 46 discriminatory genes obtained following an assessment on all gene sets, and another that contains 35 discriminatory genes obtained based on an investigation of genes that are differentially present (or absent) in the genomes of the multipartite bacteria and their respective close relatives. Our study revealed a small pool of genes that discriminate bacteria with multipartite genomes and their close relatives with single-chromosome genomes. Machine learning thus aided in uncovering the genetic factors that underlie the differentiation of bacterial multipartite and unipartite traits.

Neuromuscular disease genetics in under-represented populations: increasing data diversity.

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

A Genomic Conceptualization of Species.

Species concepts have been defined through a number of lenses, but are almost entirely empirical in nature. Fundamentally linked to various existing species concepts, an interpretation of genomic data through a species classification filter based upon a theoretical genotype-phenotype map with a monophyly requirement is discussed.

Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory

PurposeThe analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an artificial intelligence model, based on machine learning for automating variant prioritization for diagnosing rare genetic diseases in the Baylor Genetics clinical laboratory. MethodsThe automated analysis model was developed using a supervised learning approach based on thousands of manually curated variants. The model was evaluated on 2 cohorts. The model accuracy was determined using a retrospective cohort comprising 180 randomly selected exome cases (57 singletons, 123 trios); all of which were previously diagnosed and solved through manual interpretation. Diagnostic yield with the modified workflow was estimated using a prospective “production” cohort of 334 consecutive clinical cases. ResultsThe model accurately pinpointed all manually reported variants as candidates. The reported variants were ranked in top 10 candidate variants in 98.4% (121/123) of trio cases, in 93.0% (53/57) of single proband cases, and 96.7% (174/180) of all cases. The accuracy of the model was reduced in some cases because of incomplete variant calling (eg, copy number variants) or incomplete phenotypic description. ConclusionThe automated model for case analysis assists clinical genetic laboratories in prioritizing candidate variants effectively. The use of such technology may facilitate the interpretation of genomic data for a large number of patients in the era of precision medicine.

Split k-mer analysis compared to cgMLST and SNP-based core genome analysis for detecting transmission of vancomycin-resistant enterococci: results from routine outbreak analyses across different hospitals and hospitals networks in Berlin, Germany.

The increase of Vancomycin-resistant Enterococcus faecium (VREfm) in recent years has been partially attributed to the rise of specific clonal lineages, which have been identified throughout Germany. To date, there is no gold standard for the interpretation of genomic data for outbreak analyses. New genomic approaches such as split k-mer analysis (SKA) could support cluster attribution for routine outbreak investigation. The aim of this project was to investigate frequent clonal lineages of VREfm identified during suspected outbreaks across different hospitals, and to compare genomic approaches including SKA in routine outbreak investigation. We used routine outbreak laboratory data from seven hospitals and three different hospital networks in Berlin, Germany. Short-read libraries were sequenced on the Illumina MiSeq system. We determined clusters using the published Enterococcus faecium-cgMLST scheme (threshold ≤20 alleles), and assigned sequence and complex types (ST, CT), using the Ridom SeqSphere+ software. For each cluster as determined by cgMLST, we used pairwise core-genome SNP-analysis and SKA at thresholds of ten and seven SNPs, respectively, to further distinguish cgMLST clusters. In order to investigate clinical relevance, we analysed to what extent epidemiological linkage backed the clusters determined with different genomic approaches. Between 2014 and 2021, we sequenced 693 VREfm strains, and 644 (93 %) were associated within cgMLST clusters. More than 74 % (n=475) of the strains belonged to the six largest cgMLST clusters, comprising ST117, ST78 and ST80. All six clusters were detected across several years and hospitals without apparent epidemiological links. Core SNP analysis identified 44 clusters with a median cluster size of three isolates (IQR 2-7, min-max 2-63), as well as 197 singletons (41.4 % of 475 isolates). SKA identified 67 clusters with a median cluster size of two isolates (IQR 2-4, min-max 2-19), and 261 singletons (54.9 % of 475 isolates). Of the isolate pairs attributed to clusters, 7 % (n=3064/45 596) of pairs in clusters determined by standard cgMLST, 15 % (n=1222/8500) of pairs in core SNP-clusters and 51 % (n=942/1880) of pairs in SKA-clusters showed epidemiological linkage. The proportion of epidemiological linkage differed between sequence types. For VREfm, the discriminative ability of the widely used cgMLST based approach at ≤20 alleles difference was insufficient to rule out hospital outbreaks without further analytical methods. Cluster assignment guided by core genome SNP analysis and the reference free SKA was more discriminative and correlated better with obvious epidemiological linkage, at least recently published thresholds (ten and seven SNPs, respectively) and for frequent STs. Besides higher overall discriminative power, the whole-genome approach implemented in SKA is also easier and faster to conduct and requires less computational resources.

GenoREC: A Recommendation System for Interactive Genomics Data Visualization.

Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in data visualization, their choices are often based on trial and error or guided by technical details, such as data formats that a specific tool can load. This approach prevents them from making effective visualization choices for the many combinations of data types and analysis questions they encounter in their work. Visualization recommendation systems assist non-experts in creating data visualization by recommending appropriate visualizations based on the data and task characteristics. However, existing visualization recommendation systems are not designed to handle domain-specific problems. To address these challenges, we designed GenoREC, a novel visualization recommendation system for genomics. GenoREC enables genomics analysts to select effective visualizations based on a description of their data and analysis tasks. Here, we present the recommendation model that uses a knowledge-based method for choosing appropriate visualizations and a web application that enables analysts to input their requirements, explore recommended visualizations, and export them for their usage. Furthermore, we present the results of two user studies demonstrating that GenoREC recommends visualizations that are both accepted by domain experts and suited to address the given genomics analysis problem. All supplemental materials are available at https://osf.io/y73pt/.

Avances en genética clínica y sus retos actuales

The great advances in the development of genomic technologies and their incorporation into routine clinical practice is bringing about a change in which an individual's genetic information is becoming increasingly relevant to their medical care. This is known as genomic medicine. Its implementation is not without barriers, including difficulties in the assessment and interpretation of genomic data, deficient training of professionals and patients in this field, unequal access to units with expertise, and a lack of professional profiles and infrastructures necessary for the incorporation of genomic technologies into routine clinical practice. This article reviews the advances and challenges of genomic medicine.