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Precision medicine is the practice of matching prevention, diagnosis, and treatment to the biological characteristics of an individual patient rather than to the average patient in a disease category. It becomes possible when three things connect: molecular data about the patient (genomics, proteomics, and other omics layers), a measurable marker that links that biology to a clinical outcome (a biomarker), and evidence strong enough to justify acting on it. The McDonnell Genome Institute (MGI) at WashU describes itself as working across genomics, proteomics, and precision medicine, and its technology hubs illustrate the kinds of data generation this approach depends on.

How precision medicine differs from conventional treatment

Conventional, or "one-size-fits-all," medicine typically starts from a diagnosis defined by symptoms, tissue appearance, and population-level statistics. Treatment follows the protocol that works best on average for that diagnosis.

Precision medicine starts from the same diagnosis but adds a molecular question: what is different about this patient's disease that changes which therapy is likely to work?

The practical consequence: precision medicine does not replace conventional medicine. It narrows who is likely to benefit from a given intervention and, increasingly, who should avoid it.

The data layers that make matching possible

Genomics

Genomics reads DNA to find inherited variants, somatic mutations, copy-number changes, and structural rearrangements. In precision medicine, this is often the first layer because it is stable, sequenceable from many sample types, and directly actionable when a variant maps to an approved or investigational therapy.

Proteomics and other omics

DNA alone does not tell you what a cell is actually doing. Proteomics measures proteins and their modifications; metabolomics and lipidomics capture small-molecule state. MGI's Mass Spectrometry Technology Access Center lists proteomics, metabolomics, lipidomics, deep-scale PTM (post-translational modification) analysis, native mass spectrometry, and spatial multi-omics MS technologies. These layers matter when the actionable signal is not in the genome — for example, when a protein is overexpressed without a DNA-level explanation.

Multi-omics integration

Multi-omics means combining these layers rather than reading them in isolation. A variant may be present in DNA but not expressed; a protein may be abundant without a corresponding genomic change. Integration is what turns separate measurements into a candidate mechanism.

From sample to actionable insight: the workflow

The general path looks like this. Exact steps vary by institution and assay.

1. Sample collection and accessioning. Input: patient tissue, blood, or derived cells. Action: assess quality and quantity, especially for scarce or degraded material. Expected result: a sample that passes the assay's input requirements.
2. Data generation. Input: qualified sample. Action: sequencing, mass spectrometry, spatial profiling, or imaging depending on the question. Expected result: raw reads, spectra, or images.
3. Processing and QC. Input: raw data. Action: alignment, quantification, normalization, batch assessment. Expected result: a clean feature matrix with documented quality metrics.
4. Variant or feature interpretation. Input: processed features. Action: annotate variants, compare to reference databases, rank candidate markers. Expected result: a shortlist of candidates with supporting evidence.
5. Functional validation. Input: candidate list. Action: test in model systems. MGI's Genome Engineering & Stem Cell Center lists genome-edited stem cell and cancer cell lines, genome-edited model organisms, patient-derived iPSCs, stem cell differentiation, and CRISPR screening; its Functional Imaging group lists massively parallel genetic cellular screening, drug screening, isolating live cells from specific phenotypes, spatial transcriptomics, and image analysis. Expected result: evidence that the candidate changes a measurable phenotype.
6. Clinical decision support. Input: validated marker. Action: match to therapy, trial, or monitoring plan. Expected result: a recommendation that a care team can act on.

Steps 1–5 are where most of the time and cost sit. Step 6 is where the evidence bar is highest.

Where precision medicine is applied

• Biomarker discovery. Finding a measurable feature that predicts response, resistance, or prognosis. This is the bridge between a molecular finding and a clinical decision.
• Variant interpretation. Deciding whether a specific variant is meaningful, uncertain, or benign — the step that determines whether a genomic result changes care.
• Drug screening and functional testing. Testing candidate therapies against patient-derived cells to see what actually kills or inhibits the disease.
• Clinical trial design. Using marker-defined subgroups to enrich trials for likely responders, which is also why such trials are often smaller than traditional ones.

MGI states it has 65 current academic institute partners and 56 current biopharma partners, and reports a 95% repeat partner rate — a signal that multi-omic service work is often repeated rather than one-off.

Limitations and open problems

• Data standards. Different assays, pipelines, and reference versions produce results that are hard to compare across sites. MGI's own news commentary notes that research-grade data standards are critical in clinical trials, particularly with scarce or degraded patient material, where analytical precision determines whether samples can be used at all.
• Evidence requirements. A marker is not a treatment. Moving from association to clinical action requires trials that show acting on the marker improves outcomes.
• Sample constraints. Small biopsies, degraded RNA, and low tumor content limit which assays are feasible.
• Interpretation of uncertain findings. Many variants remain variants of uncertain significance, and multi-omic data can add ambiguity as easily as it resolves it.
• Equity and access. Genomic reference data have historically underrepresented some populations, which affects interpretation accuracy for those groups.

How to decide whether a precision medicine approach fits

Ask three questions before committing:

1. Is there an actionable marker for this disease? If no validated marker exists, molecular profiling may generate research value but not a changed treatment plan.
2. Can the sample support the assay? Check input requirements before assuming a result is possible.
3. What will you do with the result? If the answer is "nothing changes," the data may still be worth generating for research — but that should be the stated goal.

For institutions and research groups, the practical entry point is usually a technology access center or core facility that offers the specific assay layer you need, rather than a single end-to-end product. MGI's structure — separate hubs for genome technology, genome engineering and stem cells, mass spectrometry, and functional imaging — reflects that multi-omic work is assembled from distinct capabilities rather than purchased as one service.

The McDonnell Genome Institute (MGI) is a genomics and proteomics research institute at Washington University in St. Louis that provides multi-omic services to academic and biopharma partners. It traces its origins to a central role in the Human Genome Project and now operates four technology hubs covering sequencing, genome engineering, mass spectrometry, and functional imaging. If you have a genomics, proteomics, or multi-omics project—especially a difficult one—MGI is structured to take it from sample to data on a defined timeline and budget.

What MGI Is and What Makes It Distinctive

MGI describes itself as a world leader in genomics with a broad technology portfolio and long-standing collaboration and outreach pipelines. The institute emphasizes that its work is unusual because of the breadth of resources available in one location—you can move between sequencing, cell engineering, mass spectrometry, and imaging without coordinating separate vendors.

Two figures from MGI's own reporting illustrate its track record with partners:

The high repeat-partner rate is the most useful signal if you are evaluating MGI as a service provider: most organizations that use it come back.

The Four Technology Hubs and What Each Provides

MGI organizes its services into four access centers. Which one you contact depends on what your project actually needs.

Genome Technology Access Center

This is the hub for sequencing and related genomics work:

• Short-read sequencing
• Long-read sequencing
• High-throughput proteomics
• Single cell genomics
• Spatial transcriptomics
• Computational biology and bioinformatics

If your project is primarily "sequence these samples and help me analyze the data," this is the entry point.

Genome Engineering & Stem Cell Center

For projects that require modified cells or organisms rather than sequencing alone:

• Genome-edited stem cell and cancer cell lines
• Genome-edited model organisms
• Patient-derived iPSCs
• Stem cell differentiation
• CRISPR screening

This hub is relevant when you need a model system built or a genetic screen run before downstream analysis.

Mass Spectrometry Technology Access Center

This hub covers mass spectrometry–based measurement:

• Proteomics
• Metabolomics
• Lipidomics
• Deep-scale PTM (post-translational modification) analysis
• Native mass spectrometry
• Spatial multi-omics MS technologies

Choose this hub when your question is about proteins, metabolites, or modifications rather than nucleic acids.

Functional Imaging for Variant Elucidation

This hub focuses on linking genetic variants to function through imaging and screening:

• Massively parallel genetic cellular screening
• Drug screening
• Isolating live cells from specific phenotypes
• Spatial transcriptomics
• Image analysis

This is the relevant hub when you have variants of uncertain significance and need functional evidence, or when you want to screen compounds against a cellular phenotype.

How the Hubs Fit Together

The four hubs are not independent silos. A typical multi-omic project might move through several:

1. Genome Engineering builds a CRISPR-edited cell line or runs a screen.
2. Genome Technology Access Center sequences the resulting clones or populations.
3. Mass Spectrometry measures protein or metabolite changes in those same samples.
4. Functional Imaging confirms the phenotype visually or isolates cells of interest.

Because all four sit within one institute, samples and data can pass between them without leaving MGI—this is the practical meaning of MGI's claim about breadth of resources in one location.

Who MGI Serves

MGI works with two main partner categories, both listed explicitly on its site:

• Academic institutes (65 current partners)
• Biopharma organizations (56 current partners)

The institute states it is "built to deliver results on time and on budget" and "thrives on difficult projects." That positioning matters if your project has failed elsewhere, involves degraded or scarce material, or requires methods that a standard core facility does not offer.

MGI also publishes material on why research-grade data standards matter in clinical trials, noting that decisions at every trial stage depend on data that is accurate, reproducible, and interpretable—particularly when working with scarce or degraded patient material, where analytical precision determines whether samples can be used at all.

How to Start a Project With MGI

MGI's site provides a "Start a Project" entry point, and each of the four technology hubs has its own "Learn More" page describing its services in detail. The practical sequence is:

1. Identify which hub matches your primary need. Use the lists above—sequencing goes to the Genome Technology Access Center, cell models to Genome Engineering, protein/metabolite measurement to Mass Spectrometry, functional variant work to Functional Imaging.
2. Go to that hub's page to review the specific service list and confirm your assay is offered.
3. Use the "Start a Project" path to begin the conversation with MGI about scope, timeline, and deliverables.

If your project spans multiple hubs, start with the hub that handles the earliest step in your workflow and raise the downstream needs when you make contact.

What to Check Before You Commit

Because MGI's public materials describe capabilities and partner counts but do not publish service pricing or turnaround guarantees, confirm the following directly with MGI during project scoping:

• Whether your specific assay is currently offered at the hub you identified—service lists can change.
• Timeline and cost for your sample type and scale, since "on time and on budget" is a stated commitment rather than a published schedule.
• What the deliverable includes—raw data, analyzed results, or both—and whether bioinformatics support from the Genome Technology Access Center is part of the scope.
• Sample requirements, especially if your material is scarce or degraded, since that is the scenario where analytical precision is most consequential.

For difficult projects or multi-omic designs that need sequencing, cell engineering, mass spectrometry, and imaging under one roof, MGI's combination of breadth and a 95% repeat-partner rate makes it a reasonable first contact. For routine, single-assay work, compare it against local core facilities on turnaround and cost before deciding.

Proteomics is the large-scale study of proteins—their identities, abundances, structures, modifications, and interactions—within a biological sample. It answers questions that genomics alone cannot, because protein levels and states often diverge from what the underlying genes predict. This explainer covers the core concepts, the main technology routes (mass spectrometry, affinity-based assays, and high-plex spatial methods), the applications that matter most, and the practical constraints you should weigh before choosing an approach.

Proteomics vs. Genomics and Transcriptomics

Genomics reads DNA—the blueprint. Transcriptomics measures RNA—the working copies. Proteomics measures the proteins that actually carry out most cellular functions. Each layer adds information the previous one cannot supply.

The practical consequence: if you want to know whether a variant actually changes cell behavior, or whether a drug hits its intended target, you need protein-level measurement. As the McDonnell Genome Institute (MGI) frames it, its Functional Imaging hub exists for "variant elucidation"—connecting genetic changes to functional, protein-level outcomes.

Core Concepts in Proteomics

Protein abundance and dynamic range

Protein concentrations in a cell can span many orders of magnitude. High-abundance proteins (like albumin in blood) can mask low-abundance ones (like many signaling proteins and biomarkers). Most workflows include depletion, fractionation, or enrichment steps to manage this.

Post-translational modifications (PTMs)

Proteins are chemically modified after translation—phosphorylation, glycosylation, ubiquitination, and many others. These modifications control activity, localization, and stability. Deep-scale PTM analysis is a distinct capability; MGI's Mass Spectrometry Technology Access Center lists "Deep-scale PTM analysis" among its services.

Protein–protein interactions

Proteins rarely act alone. Interaction mapping reveals complexes, signaling networks, and drug targets. This is often done alongside abundance measurements rather than instead of them.

Spatial context

Knowing a protein is present in a tissue is different from knowing which cells contain it. Spatial methods preserve location information, which matters for tumor heterogeneity, tissue architecture, and biomarker validation.

Main Technology Routes

Mass spectrometry (MS)

MS identifies and quantifies proteins by measuring the mass-to-charge ratios of peptides (usually after enzymatic digestion). It is the most broadly unbiased route—you don't need to know in advance which proteins to look for.

Workflow in outline:

1. Sample preparation — lyse cells or tissue, extract protein, digest into peptides.
2. Separation — liquid chromatography separates peptides before they enter the instrument.
3. Ionization and measurement — peptides are ionized and their masses measured; fragmentation gives sequence information.
4. Data analysis — spectra are matched to sequences and quantified.

Expected result: a catalog of identified proteins with relative or absolute abundance, and optionally modification sites. MGI's Mass Spectrometry hub covers proteomics, metabolomics, lipidomics, deep-scale PTM analysis, native mass spectrometry, and spatial multi-omics MS technologies.

Affinity- and antibody-based assays

These use antibodies or other affinity reagents to detect specific proteins. They trade breadth for sensitivity and throughput—excellent when you know your targets. High-plex proteomics panels let you measure many proteins simultaneously from limited sample.

High-plex and spatial methods

High-plex proteomics and spatial multi-omics combine many-analyte measurement with location. MGI lists "High-throughput proteomics," "Single cell genomics," and "Spatial transcriptomics" under its Genome Technology Access Center, and "Spatial multi-omics MS technologies" under its Mass Spectrometry hub—reflecting how these approaches increasingly overlap.

Functional and imaging readouts

For connecting proteins to phenotype, functional imaging and massively parallel cellular screening (including drug screening and isolating live cells from specific phenotypes) provide a bridge. MGI's Functional Imaging hub lists these capabilities explicitly.

Applications

• Biomarker discovery — finding proteins that distinguish disease states, treatment responders, or disease stages.
• Drug target validation — confirming that a drug engages its intended protein target and measuring downstream effects.
• Clinical research — as MGI notes in its September 2025 piece, "research-grade data matters in clinical trials," especially with scarce or degraded patient material where analytical precision determines whether samples can be used at all.
• Multi-omics integration — combining proteomics with genomics and transcriptomics for a fuller picture. MGI describes itself as offering "a full suite of multi-omic services," and its June 2025 piece "Clinical OMICS: Where Multi-Omics Meets Clinical Reality" addresses how these technologies are moving toward clinical use.
• Variant elucidation — determining whether a genetic variant has functional consequences at the protein level.

Practical Constraints to Weigh

• Sample quality and quantity. Degraded or scarce material limits what any platform can detect. This is a recurring theme in clinical research contexts.
• Dynamic range. Without depletion or fractionation, low-abundance proteins can be invisible.
• Throughput vs. depth. High-plex panels give breadth and speed; deep MS workflows give depth and modification detail. You often cannot maximize both.
• Reproducibility. Batch effects, instrument drift, and sample handling all affect results. Research-grade standards and rigorous QC are not optional.
• Bioinformatics load. Proteomics datasets are large and require specialized computational analysis. MGI lists "Computational biology/bioinformatics" as a core capability within its Genome Technology Access Center—a signal that this is not an afterthought.

Choosing an Approach

If you need unbiased discovery across many proteins, start with mass spectrometry. If you have known targets and limited sample, affinity-based high-plex panels are often more practical. If spatial context matters—tumor architecture, tissue heterogeneity—prioritize spatial proteomics or spatial multi-omics. If the goal is linking a variant to function, pair proteomic measurement with functional screening or imaging.

MGI's own positioning is instructive: it emphasizes breadth of resources "in one location," with 16 years of average employee tenure, a 95% repeat partner rate, and current partnerships spanning 65 academic institutes and 56 biopharma partners. For teams that need multiple omic layers coordinated rather than outsourced piecemeal, that integration is the relevant selection criterion.

Where to Start

Define your biological question first, then let it dictate the technology. A biomarker discovery question, a target engagement question, and a variant function question each point to different platforms and sample requirements. MGI's project intake ("Start a Project") and its four technology hubs—Genome Technology Access Center, Genome Engineering & Stem Cell Center, Mass Spectrometry Technology Access Center, and Functional Imaging—map onto these distinct needs. Match your question to the hub, confirm sample requirements early, and plan the bioinformatics analysis before data generation begins.

Genome research is the study of an organism's complete DNA sequence—how it is structured, how it varies, and what it does—and it is carried out at scale through a sequence of technologies rather than a single method. At a large institute, that work typically runs from short-read and long-read sequencing through single-cell and spatial assays, then into functional follow-up such as CRISPR screening and mass spectrometry. This article explains what genome research covers, how it differs from adjacent fields, and how institutes like the McDonnell Genome Institute (MGI) organize these capabilities so you can decide what a project needs.

Genome research vs. genomics, proteomics, and multi-omics

These terms overlap but are not interchangeable:

• Genome research is the broad pursuit of understanding genomes—sequencing them, mapping variation, and determining what specific sequences do. It includes both technology development and biological interpretation.
• Genomics is the discipline and toolset focused on the genome itself: sequencing, assembly, variant calling, and comparative analysis.
• Proteomics studies proteins—their abundance, modifications, and interactions—which often requires different instrumentation, such as mass spectrometry.
• Multi-omics combines data layers (genome, transcriptome, proteome, metabolome, and others) to connect genotype to function.

A practical way to think about it: genome research is the program; genomics, proteomics, and multi-omics are the methods it draws on. MGI describes itself as offering "a full suite of multi-omic services," which reflects this layered structure.

Core methods in genome research

Sequencing: short-read and long-read

Short-read sequencing remains the workhorse for high-accuracy variant detection and large cohort studies. Long-read sequencing resolves regions that short reads handle poorly—structural variants, repetitive regions, and complex rearrangements. MGI lists both under its Genome Technology Access Center, alongside high-throughput proteomics, single-cell genomics, spatial transcriptomics, and computational biology/bioinformatics.

Single-cell and spatial approaches

Single-cell genomics separates signal by individual cell, which matters when a tissue sample contains mixed cell types. Spatial transcriptomics adds location, letting you see where gene expression occurs within a tissue section. Together they move genome research from "what variants exist" toward "which cells are affected and where."

Genome engineering and functional follow-up

Sequence alone rarely answers whether a variant matters. Genome engineering closes that gap. MGI's Genome Engineering & Stem Cell Center covers genome-edited stem cell and cancer cell lines, genome-edited model organisms, patient-derived iPSCs, stem cell differentiation, and CRISPR screening. This is the step where a statistical association becomes a testable causal claim.

Mass spectrometry and functional imaging

MGI's Mass Spectrometry Technology Access Center lists proteomics, metabolomics, lipidomics, deep-scale PTM (post-translational modification) analysis, native mass spectrometry, and spatial multi-omics MS technologies. A separate Functional Imaging group handles massively parallel genetic cellular screening, drug screening, isolating live cells from specific phenotypes, spatial transcriptomics, and image analysis. These capabilities connect genome-level findings to protein-level and cellular-level evidence.

How institutes organize genome research at scale

Large genome research centers are usually structured as shared technology hubs rather than single labs, because no one project needs every instrument. MGI's model illustrates the pattern:

MGI states it has been involved in genomics research since its central role in the Human Genome Project and reports 16 years of average employee tenure, a 95% repeat partner rate, 65 current academic institute partners, and 56 current biopharma partners. Those figures describe the institute's own collaboration history; they are useful context for judging operational stability, not a guarantee of outcomes for any specific project.

What to consider when choosing a genome research partner

• Match the method to the question. Variant discovery, structural variation, cell-type-specific expression, and causal validation each point to different assays. Confirm the partner offers the specific technique your question requires.
• Check whether functional follow-up is in-house. If your project needs to move from association to mechanism, having genome engineering and screening available in the same place reduces handoffs between vendors.
• Ask about computational support. Sequencing volume is only useful with bioinformatics capacity to process it; MGI lists computational biology/bioinformatics as part of its Genome Technology Access Center.
• Look at the collaboration record. Repeat partner rates and partner counts indicate whether an institute routinely handles external projects, as opposed to only internal research.
• Plan for sample constraints. MGI notes that research-grade data standards are especially critical with scarce or degraded patient material—worth raising early if your samples are limited.

Getting started

MGI's site provides a "Start a Project" entry point and "Learn More" links for each technology hub. Before contacting any genome research provider, define three things: the biological question, the sample type and quantity, and whether you need data generation only or data plus functional validation. Those three answers determine which hub—and which combination of methods—your project actually needs.

Genomics is the large-scale study of an organism's complete set of genetic material—its genome—including how genes are structured, expressed, regulated, and how they vary across individuals and populations. It differs from classical genetics, which typically studies one or a few genes at a time, and from proteomics, which studies the full complement of proteins. Genomics matters most when you need a system-level view: identifying disease-associated variants, mapping expression across tissues, or connecting genotype to clinical outcome. The sections below cover the core concepts, the main subfields and technologies, and how genomics links to proteomics and precision medicine.

Genomics vs. genetics vs. proteomics

These three fields answer different questions and often work together:

Genomics and proteomics are complementary: DNA sequence tells you what could be expressed, while proteomics tells you what is actually present and modified in a sample. Multi-omics combines these layers—genomics, transcriptomics, proteomics, metabolomics—to build a more complete picture than any single layer provides.

Main subfields of genomics

• Functional genomics: how genes and regulatory elements drive biological function, often via CRISPR screens and gene engineering.
• Single-cell genomics: resolves cell-to-cell differences that bulk sequencing averages out.
• Spatial transcriptomics: measures gene expression while preserving where in a tissue it occurs.
• Comparative and population genomics: variation across species or across large groups of individuals.

Core sequencing and analysis technologies

Sequencing platforms differ mainly in read length, throughput, and what they resolve:

• Short-read sequencing: high accuracy and throughput; strong for variant calling and counting, weaker for repetitive or structurally complex regions.
• Long-read sequencing: spans repeats and structural variants that short reads miss.
• Single-cell and spatial platforms: add resolution in cell identity and tissue location.
• Computational biology / bioinformatics: turns raw reads into variants, expression values, and interpretable results—an inseparable part of any genomics pipeline.

The McDonnell Genome Institute (MGI) at WashU describes offering these capabilities through dedicated technology hubs, including a Genome Technology Access Center (short-read, long-read, high-throughput proteomics, single-cell genomics, spatial transcriptomics, bioinformatics), a Genome Engineering & Stem Cell Center (genome-edited cell lines and model organisms, patient-derived iPSCs, CRISPR screening), a Mass Spectrometry Technology Access Center (proteomics, metabolomics, lipidomics, PTM analysis, native mass spectrometry, spatial multi-omics), and a Functional Imaging group (genetic cellular screening, drug screening, live-cell isolation, image analysis).

How genomics feeds proteomics, multi-omics, and precision medicine

The flow is roughly: genome → transcriptome → proteome → phenotype. Genomics identifies candidate variants; functional and single-cell methods test what those variants do; proteomics and mass spectrometry confirm whether the effect appears at the protein level; multi-omics integrates the layers. In precision medicine, this chain supports biomarker discovery and variant interpretation—for example, using functional imaging and cellular screening to work out whether a specific variant is likely to matter clinically.

Applications and where to start

Common real-world uses include biomarker discovery, clinical research, drug screening, and variant elucidation. MGI reports 16 years of average employee tenure, a 95% repeat partner rate, 65 current academic institute partners, and 56 current biopharma partners—useful signals if you are evaluating a genomics collaborator for a difficult or large-scale project.

If you are scoping a project, match the method to the question: short-read sequencing for variant discovery at scale, long-read for structural complexity, single-cell or spatial methods for heterogeneity and location, and mass spectrometry when the protein layer is the decision point. MGI's site provides a "Start a Project" entry point and per-hub "Learn More" pages for each technology area.