Genomic Diagnostics: Choosing the Right Test for Clinical and Veterinary Applications

Genomic diagnostics now sit at the center of clinical and veterinary decision-making, yet the choice of test is often driven by habit, vendor availability, or cost instead of by the clinical question at hand. This article provides a decision framework for selecting genomic diagnostic tests based on clinical question, sample type, turnaround time, and cost. The framework applies to both human clinical diagnostics and veterinary applications, with attention to the shared technical foundations and the distinct regulatory and practical constraints of each field.

The Clinical Question Determines the Test Class

The first decision in genomic diagnostics is not which platform to use but what biological question must be answered. Different clinical scenarios require different resolutions of genomic information, and selecting a test that answers the wrong question produces misleading results regardless of technical quality.

For a suspected monogenic disorder with a clear candidate gene list, targeted sequencing or a focused gene panel delivers the needed information at lower cost and with simpler interpretation than whole-genome approaches. For a patient or animal with an undifferentiated presentation where the differential diagnosis spans many genes, whole-exome sequencing provides broader coverage. For structural rearrangements, copy number changes, or aneuploidy, chromosomal microarray or cytogenomic methods are the appropriate choice. The review of cytogenetics and cytogenomics in clinical diagnostics describes the chronological progression of diagnostic tools from conventional karyotyping to high-resolution genomic and single-cell techniques, each with distinct strengths for particular classes of genomic alteration [7].

The clinical question also determines whether germline or somatic testing is appropriate. Germline testing asks about inherited variants present in every cell and is relevant for hereditary cancer syndromes, inherited metabolic disorders, and breed-associated genetic conditions in animals. Somatic testing asks about variants acquired in specific tissues, most commonly tumors, and requires tumor tissue or liquid biopsy samples. The distinction matters for sample selection, bioinformatic analysis, and result interpretation.

At a Glance: Test Selection Decision Table

Core Principles of Genomic Test Selection

Analytical Validity Precedes Clinical Utility

A genomic test must first measure what it claims to measure. Analytical validity refers to the accuracy and reliability of the test in detecting the variant or genomic feature of interest. This includes sensitivity, specificity, and reproducibility. Before any clinical decision is made, the laboratory must demonstrate that the test performs consistently across runs, operators, and sample types.

Clinical utility is a separate question. A test may be analytically valid yet provide no actionable information for a particular patient or animal. The translational gap between genomic knowledge and clinical application is well documented, with limited evidence supporting the clinical utility of many genetic tests and challenges in implementing new testing programs [12]. Clinicians and veterinarians should ask whether a positive or negative result will change management before ordering the test.

Sample Type Constrains Test Choice

The sample type available often determines which tests are feasible. Blood and buccal samples provide high-quality germline DNA for most applications. Tissue samples may be required for somatic testing. Degraded DNA from formalin-fixed paraffin-embedded tissue limits the resolution of some assays and may require specialized library preparation methods.

For pathogen genomics, the sample must contain sufficient pathogen nucleic acid for sequencing. In outbreak investigations, the quality of the isolate and the timing of sample collection relative to disease onset directly affect the success of whole-genome sequencing for subtyping and epidemiological linkage.

Turnaround Time Must Match the Clinical Decision

The urgency of the clinical question determines acceptable turnaround time. For acute clinical decisions such as antimicrobial therapy selection or surgical planning, a test that requires weeks for results may be clinically useless even if technically superior. For reproductive planning or breed selection in animals, longer turnaround times are acceptable.

Genomic newborn screening studies have demonstrated that sequencing-based approaches can be integrated into screening programs, but the design parameters including turnaround time and confirmatory testing pathways require careful optimization before broad implementation [14]. The same principle applies in veterinary contexts where results inform breeding decisions or herd management.

Practical Workflow for Test Selection

Step 1: Define the Clinical Question Precisely

Write the clinical question in a form that specifies the genetic information needed. For example, "Does this dog carry the breed-associated variant for exercise-induced collapse?" is a different question from "What is the genetic cause of this dog's episodic weakness?" The first requires targeted genotyping. The second may require exome or genome sequencing.

Step 2: Determine the Inheritance Pattern and Allele Frequency Context

For germline testing, the suspected inheritance pattern narrows the test choice. Autosomal dominant conditions with high penetrance may be identified by targeted sequencing of known genes. Autosomal recessive conditions require interrogation of both alleles. De novo variants require trio analysis, which changes the samples needed and the cost structure.

Step 3: Assess the Pretest Probability

When the pretest probability for a specific gene is high, targeted testing is appropriate. When the phenotype is heterogeneous and many genes could be responsible, broader testing is justified. The cost difference between a targeted panel and whole-exome sequencing has narrowed, but interpretation burden and the likelihood of uncertain results remain higher with broader tests.

Step 4: Evaluate the Laboratory and Its Quality Controls

The laboratory must participate in external quality assessment programs and maintain documented internal quality controls. For clinical applications, the laboratory should be accredited by the relevant regulatory body. For veterinary applications, accreditation requirements vary by jurisdiction, and the clinician should verify the laboratory's quality framework before relying on results.

Step 5: Consider the Interpretation and Reporting Pathway

A genomic test result is only as useful as its interpretation. The reporting laboratory must have documented variant interpretation procedures, and the ordering clinician must have a pathway for understanding and acting on the results. For complex results, referral to a genetics specialist may be required.

Options and Tradeoffs Across Test Platforms

Targeted Sequencing and Gene Panels

Targeted sequencing interrogates a defined set of genes or genomic regions. Gene panels are designed around clinical phenotypes, such as epilepsy panels, cardiomyopathy panels, or breed-specific panels in veterinary medicine. The advantages include lower cost, faster turnaround, simpler interpretation, and fewer incidental findings. The limitations include the inability to detect variants outside the targeted regions and the need to update panels as new disease genes are discovered.

The design of gene panels requires careful consideration of which genes to include. In mainstream germline genetic testing for breast cancer, the outline of gene panels and the amount of pretest genetic counseling require careful planning, particularly when testing is performed by non-genetic healthcare specialists [16]. The same principle applies to veterinary panels, where the gene content must match the breed and the clinical presentation.

Whole-Exome Sequencing

Whole-exome sequencing interrogates the protein-coding regions of the genome, approximately 1 to 2 percent of the total genome. This approach is appropriate when the differential diagnosis spans many genes or when prior targeted testing has been negative. The advantages include broad coverage of coding regions and the ability to reanalyze data as new disease genes are discovered. The limitations include incomplete coverage of some exons, the inability to detect most structural variants and regulatory variants, and the higher burden of variants of uncertain significance.

In cerebral palsy, microarray and whole-exome sequencing have been the primary methods for establishing new gene-disease relationships and providing a genetic etiology for individual patients, yet many patients remain without a molecular diagnosis [5]. This illustrates both the power and the limitation of exome-based approaches and the need for additional technologies to uncover variations that escape detection by current methods.

Whole-Genome Sequencing

Whole-genome sequencing interrogates the entire genome, including non-coding regions, and can detect structural variants, some repeat expansions, and variants in regulatory regions. The cost has decreased substantially, and the accuracy of variant interpretation continues to improve. The limitations include higher cost, larger data storage requirements, more complex bioinformatic analysis, and a higher likelihood of incidental findings.

Whole-genome sequencing is particularly valuable in veterinary contexts where reference genome quality varies by species and where breed-specific structural variation may be relevant. The choice between exome and genome sequencing should be based on the likelihood that the causal variant lies in coding versus non-coding regions and on the availability of species-specific reference data.

Chromosomal Microarray and Cytogenomics

Chromosomal microarray detects copy number variants across the genome and is the test of choice for suspected microdeletion or microduplication syndromes, unexplained developmental delay, and many congenital anomalies. The review of cytogenetics and cytogenomics in clinical diagnostics describes how these methods have evolved from conventional karyotyping to high-resolution genomic techniques and how they integrate into diagnostic workflows for genetic and neoplastic diseases [7].

The limitations of chromosomal microarray include the inability to detect balanced rearrangements, low-level mosaicism, and sequence-level variants. In cancer diagnostics, cytogenomic methods remain essential for detecting the structural variants that define many hematologic malignancies.

Somatic Tumor Testing

Somatic testing requires tumor tissue or liquid biopsy and asks about variants acquired in the tumor genome. The choice of test depends on the tumor type, the treatment options available, and the sample quality. Targeted panels are commonly used to identify actionable mutations for treatment selection. Whole-exome or whole-genome sequencing of tumors provides broader information but with higher cost and more complex interpretation.

Tumor heterogeneity creates a sampling problem. A single biopsy may not represent the full spectrum of variants present in the tumor. The high molecular concordance between primary tumors and matched lymph node metastases in differentiated thyroid carcinoma supports the clinical utility of primary tumor testing to guide lymph node management, but this concordance must be demonstrated for each tumor type instead of assumed [13].

Pathogen Genomics

Pathogen whole-genome sequencing serves two distinct purposes in clinical and veterinary diagnostics. The first is identification and characterization of the pathogen, including antimicrobial resistance prediction and virulence factor detection. The second is epidemiological subtyping for outbreak investigation and transmission tracking.

In the context of zoonotic disease control, genomic monitoring of pathogens at the animal-human interface provides critical information for containment strategies. The ongoing circulation of Middle East respiratory syndrome coronavirus in dromedary camels and the recurrent zoonotic transmissions documented by genomic studies demonstrate the value of integrated genomic surveillance across species [18]. The fragmented surveillance systems and delayed genomic data integration identified in that context highlight the operational challenges that limit the impact of genomic diagnostics.

Records and Measurements for Test Selection

What to Record Before Ordering a Test

The clinical indication for testing should be documented in the medical record or herd health record. This includes the phenotype, the suspected differential diagnoses, the pretest probability for specific genetic conditions, and the clinical action that will be taken based on the result. For veterinary applications, the breed, pedigree, and any relevant breed-specific genetic considerations should be recorded.

The sample type, collection date, and handling conditions must be documented. Sample quality directly affects test performance, and degraded or contaminated samples may produce failed runs or unreliable results. The laboratory should provide a sample quality assessment with the results.

What to Record When Results Are Returned

The test method, the genes or regions interrogated, the analytical platform, and the bioinformatic pipeline version should be recorded. This information is essential for interpreting the result and for comparing results across time or across laboratories. Variant interpretation should include the classification system used and the evidence supporting the classification.

The turnaround time from sample receipt to result should be recorded. This information is useful for planning future testing and for identifying laboratories that consistently meet clinical needs.

Quality Metrics to Track

Laboratories should track the failure rate for each test type, the rate of variants of uncertain significance, and the confirmation rate for clinically significant findings. For veterinary applications, the confirmation rate for breed-associated variants should be tracked against the expected allele frequencies for the breed.

The reproducibility of results should be assessed through repeat testing of selected samples and through participation in external quality assessment programs. Discrepant results between laboratories or between runs should trigger investigation and documentation.

Common Failure Patterns in Genomic Test Selection

Testing Without a Clear Clinical Question

The most common failure is ordering a genomic test without specifying the clinical question the test must answer. This leads to inappropriate test selection, uninterpretable results, and wasted resources. The remedy is to require a written clinical indication before testing and to review the indication against the test characteristics.

Ignoring the Limitations of the Test Platform

Each test platform has specific limitations that affect the interpretation of negative results. A negative targeted panel does not exclude variants outside the targeted regions. A negative chromosomal microarray does not exclude balanced rearrangements or sequence-level variants. A negative exome does not exclude variants in poorly covered regions or in non-coding regulatory elements. Clinicians must understand these limitations before ordering the test and must communicate them when reporting results.

Overinterpreting Variants of Uncertain Significance

Variants of uncertain significance are common in genomic testing, particularly with broader tests. The clinical response to an uncertain variant should be guided by the phenotype and by the available evidence, not by the variant classification alone. In the absence of clear evidence for pathogenicity, an uncertain variant should not drive clinical management.

Failing to Consider the Target Population

The allele frequency of a variant in the relevant population affects its interpretation. A variant that is rare in one population may be common in another. For veterinary applications, breed-specific allele frequencies are essential for interpreting variant significance. The same variant may have different clinical implications in different breeds or in mixed-breed animals.

Selecting Tests Based on Cost Alone

The cheapest test is not always the most cost-effective. A targeted test that fails to identify the causal variant may require additional testing, increasing the total cost. A broader test with a higher upfront cost may provide a diagnosis in a single run. The cost-effectiveness calculation must include the probability of a diagnostic result and the cost of subsequent testing.

Limitations and Interpretation Boundaries

The Limits of Genomic Information

A genomic test result provides information about the genome, not about the phenotype. Many genetic variants have incomplete penetrance, meaning that not all individuals carrying the variant will express the associated condition. Variable expressivity means that the severity and range of clinical features can vary widely among individuals with the same variant. These factors limit the predictive value of genomic information for individual patients and animals.

The clinical utility of genomic risk assessment for common complex diseases remains limited. The evidence supporting genetic test use is often limited, and expectations for personal genomics are sometimes unrealistic [12]. For polygenic conditions, genomic information provides probabilistic risk estimates instead of definitive diagnoses.

The Challenge of Variant Interpretation

Variant interpretation requires evidence from multiple sources, including population databases, functional studies, segregation data, and computational predictions. The classification of a variant can change as new evidence accumulates. A variant classified as uncertain today may be reclassified as pathogenic or benign in the future. This creates challenges for clinical management and for communication with patients and animal owners.

The Problem of Incidental Findings

Broader genomic tests can identify variants unrelated to the clinical indication. These incidental findings may have medical significance for the patient or the animal owner. The approach to incidental findings should be defined before testing, and the consent process should address the possibility of such findings.

Species-Specific Limitations in Veterinary Genomics

Veterinary genomic diagnostics face additional challenges related to reference genome quality, variant databases, and functional annotation. The reference genome for many species is less complete than the human reference genome, and the variant databases are less comprehensive. This limits the interpretation of variants in veterinary species and increases the likelihood of variants of uncertain significance.

Quality Controls and Professional Escalation Criteria

Laboratory Quality Controls

The laboratory should have documented procedures for sample handling, nucleic acid extraction, library preparation, sequencing, and bioinformatic analysis. Internal quality controls should include positive and negative controls, replicate samples, and metrics for sequencing depth and coverage uniformity. External quality assessment participation provides an independent check on laboratory performance.

When to Escalate to a Specialist

A clinician or veterinarian should escalate to a genetics specialist when the test result is complex, when the result does not match the clinical presentation, or when the result has implications beyond the immediate clinical question. Specific escalation criteria include:

• A variant of uncertain significance in a gene relevant to the phenotype, where the result may influence management
• A negative result on a broad test where the clinical suspicion for a genetic etiology remains high
• A result that suggests a condition outside the ordering clinician's area of expertise
• A result with implications for family members or for breeding decisions in animals
• A discrepancy between the test result and the clinical phenotype that cannot be resolved by the ordering clinician

When to Consider Reanalysis

Genomic data can be reanalyzed as new disease genes are discovered and as variant interpretation improves. Reanalysis of exome or genome data may yield a diagnosis when the initial analysis was negative. The decision to reanalyze should be based on the time since the original analysis, the availability of new phenotype information, and the likelihood that new gene discoveries are relevant to the case.

Safety and Regulatory Context

Human Clinical Diagnostics

Genomic tests for human clinical use are regulated differently across jurisdictions. In the United States, laboratory-developed tests are regulated by the Centers for Medicare and Medicaid Services through the Clinical Laboratory Improvement Amendments, while test developers may seek Food and Drug Administration approval or clearance for specific tests. In the European Union, the In Vitro Diagnostic Regulation imposes requirements for the performance and clinical evidence of diagnostic tests.

The regulatory status of a test affects the evidence required to support its use and the oversight of its performance. Clinicians should verify the regulatory status of a test before ordering it and should understand the implications of that status for result interpretation.

Veterinary Diagnostics

Veterinary genomic tests are subject to less regulatory oversight than human tests in most jurisdictions. The American Veterinary Medical Association and other professional organizations have issued guidance on the use of genetic testing in animals, but the regulatory framework varies by country and by test type. The clinician should verify the laboratory's quality framework and the evidence supporting the test's claims.

Data Sharing and Privacy

Genomic data are sensitive information that requires appropriate protection. The National Institutes of Health Genomic Data Sharing Policy establishes expectations for the sharing of genomic data generated through NIH-funded research, including provisions for informed consent, data security, and data use limitations [3]. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable [4]. These principles apply to clinical and veterinary genomic data, with appropriate attention to privacy and consent requirements.

The European Bioinformatics Institute provides training resources on the management and analysis of biological data, including genomic data [1]. The National Center for Biotechnology Information provides databases and tools for genomic data storage, analysis, and interpretation [2]. These resources support the responsible use of genomic information in clinical and veterinary practice.

The Role of Emerging Technologies

Transcriptomics and Epigenomics

RNA sequencing and epigenetic profiling provide information about gene expression and regulation that is not available from DNA sequencing alone. These technologies can identify variants that affect splicing, gene expression, or epigenetic regulation. In cerebral palsy, recent advancements in genomic technologies offer opportunities to uncover variations in transcriptomes and epigenomes that have previously escaped detection [5]. These approaches are increasingly integrated into diagnostic workflows for cases where DNA sequencing has been negative.

Proteomics and Other Omics

Proteomic technologies measure the proteins present in a sample and can identify biomarkers for diagnosis, prognosis, and treatment monitoring. Mass spectrometry-based assays are already used in routine diagnostics for therapeutic drug monitoring, toxicology, endocrinology, pediatrics, and microbiology [8]. Plasma proteomics and peptidomics are emerging as tools for studying disease pathogenesis and identifying biomarkers, though the complexity and dynamic range of plasma proteins present analytical challenges [9].

In veterinary medicine, proteomics has contributed to the discovery of potential biomarkers for diagnosis, prognosis, and treatment monitoring in diseases such as leishmaniasis, though the capabilities have not yet been fully explored for routine clinical analysis [10]. The combined application of multiple omics methods can provide complementary information and improve diagnostic accuracy [6].

Artificial Intelligence in Genomic Diagnostics

Artificial intelligence methods are being developed to assist with variant interpretation, phenotype analysis, and diagnostic decision-making. These methods have the potential to improve the efficiency and accuracy of genomic diagnostics, but they require careful validation and oversight [21]. The integration of AI into laboratory diagnostics for genomic applications is an active area of development [20].

The application of AI in genomic diagnostics should be evaluated with the same rigor as any other diagnostic tool. The performance of AI-based methods must be validated on appropriate datasets, and the limitations of these methods must be understood by the clinicians who use them.

Practical Implementation Steps

Step 1: Establish a Testing Protocol

Develop a written protocol for genomic test selection that includes the clinical questions that warrant testing, the test options available, the sample requirements, and the interpretation pathway. The protocol should be reviewed and updated regularly as new tests become available and as evidence accumulates.

Step 2: Create a Test Ordering Checklist

The checklist should include the clinical indication, the suspected differential diagnoses, the pretest probability for specific genetic conditions, the sample type and quality, the turnaround time required, the cost and insurance or owner authorization, and the pathway for result interpretation and follow-up.

Step 3: Document the Decision Process

Record the rationale for test selection, including the clinical question, the test characteristics, and the alternatives considered. This documentation supports quality improvement and provides a basis for reviewing testing decisions.

Step 4: Review Testing Outcomes

Periodically review the outcomes of genomic testing, including the diagnostic yield, the time to diagnosis, the clinical actions taken based on results, and the cost per diagnosis. This review identifies opportunities to improve test selection and to reduce unnecessary testing.

Step 5: Educate the Clinical Team

Ensure that all members of the clinical team understand the indications for genomic testing, the limitations of different test types, and the pathway for result interpretation. Ongoing education is essential as the field evolves.

Frequently Asked Questions

What is the difference between a targeted gene panel and whole-exome sequencing?

A targeted gene panel interrogates a defined set of genes selected for their known association with a particular phenotype or condition. Whole-exome sequencing interrogates the protein-coding regions of the entire genome. A targeted panel is appropriate when the clinical question points to a specific set of genes. Whole-exome sequencing is appropriate when the differential diagnosis is broad or when targeted testing has been negative. The targeted panel offers lower cost, faster turnaround, and simpler interpretation. Whole-exome sequencing offers broader coverage but with a higher burden of variants of uncertain significance.

When should I choose chromosomal microarray over sequencing?

Chromosomal microarray is the test of choice when the clinical question involves copy number changes, such as microdeletions or microduplications, or when the phenotype suggests a chromosomal imbalance. Sequencing methods detect sequence-level variants but are less reliable for structural variants. For a patient or animal with unexplained developmental delay, congenital anomalies, or a suspected chromosomal syndrome, chromosomal microarray should be considered before or alongside sequencing approaches.

How do I interpret a negative genomic test result?

A negative result means that the test did not identify a pathogenic variant in the regions interrogated. It does not exclude a genetic etiology. The limitations of the test platform must be considered. A negative targeted panel does not exclude variants outside the targeted regions. A negative exome does not exclude variants in poorly covered regions or in non-coding regions. A negative chromosomal microarray does not exclude balanced rearrangements. If the clinical suspicion for a genetic etiology remains high, additional testing or reanalysis may be appropriate.

What should I do when a variant of uncertain significance is reported?

A variant of uncertain significance should not drive clinical management in the absence of supporting evidence. The clinician should review the available evidence for the variant, consider the phenotype and the pretest probability, and determine whether additional testing or family studies would clarify the significance. Referral to a genetics specialist may be appropriate. The variant classification may change as new evidence accumulates, and periodic reanalysis should be considered.

How do I choose between germline and somatic testing?

Germline testing asks about inherited variants present in every cell and is appropriate for hereditary conditions. Somatic testing asks about variants acquired in specific tissues, most commonly tumors, and requires tumor tissue or liquid biopsy. The choice depends on the clinical question. For a suspected hereditary cancer syndrome, germline testing is appropriate. For tumor mutation profiling to guide treatment selection, somatic testing is required.

What sample types are acceptable for genomic testing?

Blood, saliva, and buccal swabs provide high-quality germline DNA for most applications. Tissue samples may be required for somatic testing. The sample quality directly affects test performance, and degraded or contaminated samples may produce failed runs or unreliable results. The laboratory should provide guidance on sample collection, storage, and shipping requirements.

How long does genomic testing take?

Turnaround time varies by test type and laboratory. Targeted genotyping can return results in days. Targeted gene panels typically require one to two weeks. Whole-exome and whole-genome sequencing typically require two to six weeks. The turnaround time must match the clinical decision. For urgent clinical decisions, a test that requires weeks for results may be clinically useless even if technically superior.

What are the regulatory requirements for genomic tests?

Human genomic tests are regulated differently across jurisdictions. In the United States, laboratory-developed tests are regulated through the Clinical Laboratory Improvement Amendments, and test developers may seek Food and Drug Administration approval for specific tests. In the European Union, the In Vitro Diagnostic Regulation imposes requirements for performance and clinical evidence. Veterinary genomic tests are subject to less regulatory oversight in most jurisdictions. The clinician should verify the regulatory status of a test and the laboratory's quality framework before ordering.

Related Bioinformatics Guides

• Genomic Selection in Animal Breeding
• Epigenetics and Computational DNA Methylation Analysis: Mechanisms, Methods, and Veterinary Applications
• Variant Calling in Whole Exome Sequencing (WES): Principles, Algorithms, and Veterinary Applications
• Flux Balance Analysis in Metabolic Networks: Principles, Computational Advances, and Applications in Veterinary Systems Biology
• Molecular Dynamics Simulation of Antibody-Antigen Binding: Principles, Methodologies, and Applications in Veterinary Structural Biology

References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• Potential clinical applications of advanced genomic analysis in cerebral palsy.. EBioMedicine, 2024.
• Methods and clinical biomarker discovery for targeted proteomics using Olink technology.. Proteomics. Clinical applications, 2024.
• Cytogenetics and Cytogenomics in Clinical Diagnostics: Genome Architecture, Structural Variants, and Translational Applications.. Genes, 2025.
• Clinical applications of MS-based protein quantification.. Proteomics. Clinical applications, 2016.
• Clinical applications of plasma proteomics and peptidomics: Towards precision medicine.. Proteomics. Clinical applications, 2022.
• Proteomics and Leishmaniasis: Potential Clinical Applications.. Proteomics. Clinical applications, 2019.
• Proteomics in Pathology: The Special Issue.. Proteomics. Clinical applications, 2019.
• Closing the gap between knowledge and clinical application: challenges for genomic translation.. PLoS genetics, 2015.
• High Concordance in Molecular Profiles Between Primary Tumors and Matched Lymph Node Metastases in Differentiated Thyroid Carcinoma: Implications for Clinical Sample Selection. 2026.
• Perspectives on genomic newborn screening studies: design, implementation, and outcomes.. 2026.
• Genetic biomarker screening for drug-resistant epilepsy in low- and middle-income countries (LMICs).. 2026.
• Implementing mainstream germline genetic testing in breast cancer across Europe.. 2026.
• Diagnostics, epidemiological observations and genomic subtyping in an outbreak of pullorum disease in non-commercial chickens.. Veterinary Microbiology, 2018.
• Operational zoonotic containment of Middle East respiratory syndrome coronavirus in Saudi Arabia: An implementation-oriented One Health genomic framework. Veterinary World, 2026.
• Application of array-based comparative genomic hybridization to clinical diagnostics. Journal of Molecular Diagnostics, 2006.
• GenDAI - AI-Assisted Laboratory Diagnostics for Genomic Applications. Proceedings 2021 IEEE International Conference on Bioinformatics and Biomedicine Bibm 2021, 2021.
• Artificial intelligence in clinical and genomic diagnostics. Genome Medicine, 2019.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.