Genomic Knowledge Matrix

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

Key Knowledge Elements:

Genomics

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

mtDNA

• Structure/function
• Variants

Epigenetics

• Methylation
• Histone modification

Transcriptomics

• Transcription and transcription regulation (e.g. polyA sites and miRNA)
• RNA variants, mutations, base modification
• Trans-activation-response and open reading frame

Proteomics

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

Metabolomics

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

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

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

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