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Genomic Knowledge Matrix

Genomic Knowledge Matrix for Nursing Scientists

Woman in scientific gear staring intently beyond the frame of the image with genetic imagery floating in the foreground and backgroundThe Genomic Nursing Science Education Workgroup recognizes that Omic nursing science varies widely based on the individual investigator, the nature of the research, and the role a scientist plays on the research team. As such, the Genomic Knowledge Matrix occurs on a knowledge continuum developed from Blooms Taxonomy and Webb’s Depth of Knowledge1, 2. The four knowledge levels include Basic, Intermediate, Proficient, and Applied. See a summary by viewing the Genomic Knowledge Continuum.

View details of the background, purpose, focus and development and ongoing update processes in the Genomic Knowledge Matrix Preamble.

View the entire Genomic Knowledge Matrix for Nursing Scientists.

Access the full text of the Genomic Knowledge Matrix publication.

This section of ONSEN provides details of the key knowledge elements considered essential for integrating Omics into research.

The key concepts from the following topics comprise essential knowledge elements necessary to integrate Omic science into nursing research:

Molecular Biology

Molecular Biology is the knowledge and applied understanding of the formation and function of macromolecules.

Image demonstrating the different types of Omics: Genomics, Microbiomics, Proteomics, Metabolomics, Transcriptomics, Epigenomics

Key Knowledge Elements:

Image of DNA wrapping around in a circle


  • Genome structure/gene structure
  • Genome variation (e.g. molecular evolution, mutations, polymorphism)
  • Gene expression and epigenetics (e.g. methylation)
Image of Mitochondrial dna


  • Structure/function
  • Variants
Image of Epigenetics dna


  • Methylation
  • Histone modification
Small image showing the makeup of a dna strand


  • Transcription and transcription regulation (e.g. polyA sites and miRNA)
  • RNA variants, mutations, base modification
  • Trans-activation-response and open reading frame
Image of dna strands overlapping each other


  • Translation
  • Structure and function of peptides and implication of genetic variation
  • Post-translational modifications
  • Protein-protein interaction
  • Polysome-associated mRNAs
Image of chemical bonds


  • Metabolic byproducts
    • lipidomics
  • Signaling molecules and hormones (e.g. chemokins)
Systems Physiology

Systems physiology is integrated with systems biology to provide a functionally in depth insights into the system as a whole by combining experimental, computational, and theoretical studies to advance understanding of humans and other living creatures.

Key Knowledge Elements:

  1. Cardiovascular
  2. Renal
  3. Lymphatic
  4. Endocrine
  5. Musculoskeletal
  6. Reproductive
  7. Pulmonary
  8. Immunology
  9. Neurology
  10. Nervous
  11. Digestive
  12. Integumentary
Cell Biology

Knowledge of cell formation, structure, components and function

Key Knowledge Elements:

  1. Tissue organization
  2. Cell type and structure
    1. Somatic
    2. Germline
  3. Organelles
  4. Metabolism and transport
    1. Protein, lipid and carbohydrate metabolism (i.e. Kreb cycle, mitochondria)
  5. Cell communication
    1. Cell excitability (ion channels, action potentials)
    2. Endocrine, paracrine, autocrine signaling
  6. Growth, maintenance, repair (conception, development, aging)
    1. Mitosis and meiosis (Chromosome structure and function)
    2. Stem cells and differentiation
  7. Immune cell function

Knowledge and understanding of structure, function and interaction between microbes living in and on the human body (collectively known as the microbiome). Microbiology is the study of microscopic organisms such as bacteria, virus, archaea, fungi, and protozoa

Key Knowledge Elements:

  1. The Human Microbiome (Microbiology in the post-genomic era)
  2. Forms of microorganisms
    1. Bacteria
    2. Archaea
    3. Eukaryota (including animals, fungi, plants)
    4. Virus are not organisms in the same sense but have considerable biological importance.
  3. Bacterial Taxonomy (rank-based classification, of bacteria.)
    1. Kingdom
    2. Phylum
    3. Class
    4. Order
    5. Family
    6. Genus
    7. Species
  4. Composition, diversity, complexity - at different locations in and on the human
    1. Skin
    2. Respiratory track (e.g. nasal passages)
    3. Oral cavity
    4. Gastro-intestinal tract
    5. Urogenital tract
    6. Body fluids (i.e. blood, breast milk, amniotic fluid)
  5. Function
    1. Environmental factors that influence structure of microbial communities (e.g. antibiotics, food contaminants)
    2. Immune system (adaptive, innate, antigen-antibody)
    3. Novel diagnostics
    4. Drug targets
  6. Microbes and disease (pathogen and commensal/indigenous microbes)
    1. Routes of transmission
    2. Biofilms
    3. Structure, function, and dynamics
    4. Virome
  7. Identification methods
    1. Phenotypic analyses
    2. Genetic analyses
      1. DNA-DNA hybridization
      2. DNA profiling
      3. Sequence
  8. Phylogenetic analyses (HMP developed)
    1. 16S-based phylogeny
    2. Whole-genome sequence based analysis
  9. Interaction/ inter-relationships between microbes and the host
Analytic Approaches and Bioinformatics (Translational Bioinformatics)

Translational Bioinformatics is defined by the AMIA as “the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data, into proactive, predictive, preventive, and participatory health”3

Key Knowledge Elements:

  1. Big Data Analytics Potential
    1. Delivery of higher quality of care, i.e., Evidence-based practice
    2. Economically valuable
    3. Save lives
  2. Types of Health-related Data
    1. Clinical
    2. Behavioral
    3. Environmental
    4. Administrative/Operational
    5. Financial
    6. Diet
  3. Why Genomics and Big Data?
    1. Increased availability of genomic and other types of data
    2. Improved analytic tools
    3. Increasingly rapid development of Big Data technology
    4. Need to personalize health care
  4. Sequencing Methods
    1. Sequencing platform, interpretation, and reporting standards
    2. Goal to identify genetic variants that have known impacts on health and disease
    3. Multiple variant results in a single gene, in multiple genes in a single disease and multiple diseases
      at the same time (e.g. gene panels)
    4. Multiple findings are possible including incidental findings
    5. Sequencing results can have variable clinical relevance to patients’ and providers’ decision-making that
      subsequently improve patients’ outcomes.
  5. Genomic Data Processing: Pathway Analysis & The Reconstruction of Networks
    1. Functional effects of genes differentially expressed are analyzed
    2. Reconstruction of networks from signals measured using high-throughput techniques- analyzed to reconstruct underlying regulatory physiological networks
      1. Pathway analysis toolkits (e.g. Onto-Express Go Miner, ClueGo, GSEA, Pathway-Express)
    3. Reconstruction of metabolic networks toolkit
      1. Recon 2
    4. Reconstruction of gene regulatory networks methods
      1. Boolean methods
      2. ODE models
  6. Genomics Study Designs
    1. Twin-based epidemiological studies
      1. Used to estimate disease heritability
    2. Linkage studies
      1. Used to find disease-associated loci
    3. Genome-Wide Association Studies (GWAS)
      1. Used for identification of a large number of disease-associated genomic loci
      2. Group-Based Association Tests
             (1). GWAS analysis evaluates each SNP individually with univariate statistic
             (2). GWAS meta-analysis methods also used.
             (3). Genomic functional annotation is required for prioritizing variants and interpreting results in association studies (statistical tools available)
    4. Next Generation Sequencing/High Throughput Sequencing
      1. Used to identify not only single nucleotide variants (SNVs) but also SVs
      2. Targeted Sequencing and Whole-Genome Sequencing study design
    5. Examples of other DNA measurement techniques
      1. Copy number variants
      2. Cell free DNA (e.g. tumor, mitochondrial, nuclear)
  7. Limitations to Genomics Study Design
    1. Difficulty in interpreting GWAS results,
    2. Missing heritability or large gap between proportion of variance
  8. Limitations to Data-Oriented Science
    1. Sampling Bias
      1. Individuals being analyzed are not representative of the broader population
    2. Completeness
      1. Risk that the most important items have not been measured and/or analyzed
    3. Repeatability
      1. Reproducibility is critical but difficult without controls
    4. Constraints
      1. Much is not known about the data
  9. Innovation First Steps-Build Prospective Analytical Models: Team member requirements
    1. Subject matter expertise
    2. Advanced analytical expertise
    3. Data expertise
    4. Project management experience with software development practices
  1. 1 Anderson, L.W., Krathwohl, D. R., Bloom, B. S., A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. 2001, Boston, MA: Allyn and Bacon.
  2. 2 Webb, N., Research Monograph Number 6: Criteria for alignment of expectations and assessments on mathematics and science education. 1997, CCSSO: Washington, DC.
  3. 3 Tenenbaum, J.D., Translational Bioinformatics: Past, Present, and Future. Genomics Proteomics Bioinformatics, 2016. 14(1): p. 31-41. PMID: 26876718.