Omics on Parade: Genomics, Transcriptomics, Proteomics & Chemoproteomics in Drug Discovery

What is omics?

The past several decades have seen an explosion of ‘omics’ approaches, a broad term for any technique or field of study that seeks to characterize all of the molecules of a particular class within a cell, organism, or other biological system. Made possible by advances in sequencing, mass spectrometry, and other technologies, omics-level methods have allowed researchers to comprehensively investigate a range of cellular phenomena. No longer restricted to studying a single gene, protein, etc. in isolation (or a panel of pre-selected targets of interest), Omics techniques enable the full repertoire of biomolecules to be appreciated in a single experimental analysis. As a result, scientists can obtain a holistic, unbiased view of cellular activity, empowering them to identify off-targets, compensation, downstream effects, and more. Read on to learn more about how omics techniques can be applied to various targets of interest and how this approach has revolutionized our understanding of molecular biology, with wide-reaching implications for drug discovery and development efforts.

What is genomics?

Genomics refers to the comprehensive study of the genome, or the full set of DNA present in a cell, organisms, etc. Early genomics efforts led to the full sequencing of eukaryotic genomes in the 1990s, with a first draft of the complete human genome released in 2001. Since then, high-throughput methodologies and computational analysis pipelines have made whole-genome sequencing rapid and routine; typical processing times can be as short as a single day, with the fastest human whole-genome sequencing and analysis completed in under four hours in 2025.

In a genomic experiment, the DNA from a sample of interest is isolated and fragmented, with fragment sizes varying from 50 bp to >50,000 bp depending on sequencing methodology. In some approaches (such as Illumina sequencing) shorter fragments are amplified and sequenced by synthesis. Other approaches (such as Oxford nanopore sequencing) allow longer fragments to be ‘read’ based on changes in electrical signal as they move through a specialized channel. Once all fragments have been sequenced, computational algorithms are used to reconstruct contiguous stretches of the genome based on sequence overlap (de novo assembly) or by aligning reads to a reference genome.

Whole-genome sequencing (or variants such as whole-exome sequencing, in which coding regions of the genome are specifically enriched for sequencing) can provide valuable information about a patient or model organism and is particularly effective in the identification of genetic mutations that may drive a disease phenotype. In cancer, sequencing of patient or tumor genomes can be used to identify oncogenic drivers, select effective treatment options, monitor disease or treatment progression, and uncover resistance mechanisms. Genome sequencing can also be used in cases of rare disease or suspected genetic disease to identify specific pathogenic variants, enabling diagnosis or guiding treatment choices. In addition to identifying relevant gene mutations, whole-genome sequencing can illuminate pathogenic mutations in non-coding elements, such as regulatory regions or other non-coding loci, that would be missed by transcriptomic or proteomic approaches.

What is transcriptomics?

Transcriptomics refers to the comprehensive study of the transcriptome, or all of the RNA transcripts present in a cell or organism at a given time. In this way, transcriptomics provides insight into active gene expression in a particular context. While an organism’s genome remains essentially static, its transcriptome is in constant flux, changing from second to second as RNA molecules are continually transcribed and degraded. Following the introduction of RNA-seq c. 2008, variant approaches have been developed to analyze particular subsets of the transcriptome. For example, PRO-seq can be used to capture the nascent transcriptome, or genes actively being transcribed, while Ribo-seq identifies transcripts undergoing translation by ribosomes. Variations such as single cell RNA sequencing (scRNA-seq) even enable transcriptome analysis on the single-cell level.

In a typical transcriptomic experiment, RNA is isolated from a sample, then reverse-transcribed to generate cDNA. Depending on the experimental goals, certain enrichment or depletion steps may be performed before reverse transcription, allowing researchers to select for or against rRNAs, polyadenylated RNAs, etc. Once a cDNA library has been generated, it can be sequenced using the same next-generation sequencing technologies used for genomic sequencing. Following transcriptome assembly, computational analysis is used to quantify the number of reads at each locus, providing a metric of gene expression levels. This measurement can be made relative to other transcript levels or as an absolute value (if an RNA spike-in of known concentration is incorporated during library preparation). One of the most common ways to process transcriptomic data is differential expression analysis, which compares the transcriptomes of two or more samples and identifies transcripts that are up- or down-regulated across conditions.

Transcriptomic analysis can provide crucial insights into the molecular mechanisms underlying a cellular state, helping to clarify fundamental biology and determine relevant drug targets for a particular disease. Researchers can identify differences in gene expression between patients and controls or in subjects treated with a given drug. These perturbations can serve as biomarkers, help elucidate a disease’s etiology or a drug’s mechanism of action, or reveal a treatment’s off-target effects. Transcriptome data has been used for the diagnosis and prognosis of various diseases, as well as for the classification of cancers into distinct molecular subtypes.

What is proteomics?

Proteomics refers to the comprehensive study of the proteome, or all of the proteins present in a cell, organism, or other biological system at a given time. Similar to the transcriptome, the proteome varies over time as proteins are synthesized, degraded, or post-translationally modified. The field of proteomics began to emerge in the last decades of the 20^th century, with techniques such as 2D gel electrophoresis originally used to characterize protein identity. However, it was the application of mass spectrometry to proteomics, starting in the 1990s and continuing through present day, that ultimately took the field to new heights.

In a typical mass spectrometry-based proteomics experiment, proteins are extracted from a sample, fragmented into peptides, and ionized. The ionized molecules are separated by their mass to charge (m/z) ratios and detected. Finally, each obtained mass spectrum is computationally analyzed and compared to reference spectra to assign its amino acid sequence and protein identity. In addition to determining which proteins are present in a sample and at what levels, mass spectrometry-based proteomics can also be used to detect post-translational modifications on a protein of interest. Mass spectrometry experiments can be designed for the unbiased detection of all proteins in a given sample, as well as for the targeted analysis of specific proteins designated a priori. Other variant workflows remove the need for sample fragmentation, instead analyzing intact proteins, or introduce isobaric labels to enable different samples or conditions to be analyzed in a single workflow.

In the context of drug discovery and development, proteomics experiments provide valuable information to elucidate cellular mechanisms, therapeutic efficacy, and more. Proteomics can be used to identify biomarkersof disease state, progression, or prognosis, as well as to understand the cell-state dysfunction underlying a particular disease. Global proteomics can be used in the screening stage of pharmaceutical research to discover compounds that degrade a target protein, confirming on-target protein loss while detecting any off-target effects or downstream consequences. By comparing the proteomes of different samples (disease vs. control, treated vs. untreated, perturbed vs. un-perturbed, etc.), researchers can comprehensively characterize cellular phenotypes and understand how a given treatment restores system function.

What is chemoproteomics?

Chemoproteomics (also known as chemical proteomics) leverages the techniques of proteomics to study drug:target interactions on a global scale. In contrast to genomics, transcriptomics, and proteomics, which study endogenous repertoires of genes, transcripts, and proteins, respectively, chemoproteomics analysis requires the introduction of an exogenous small molecule, whose interactions are interrogated in a proteome-wide context. In this way, chemoproteomics provides a key pharmacological addition to the omics toolkit. Thanks to recent advances in the scale and scope of chemoproteomic approaches, this pharmacological dimension can be integrated from the first stages of a research program, accelerating therapeutic development and de-risking early discovery.

Similar to proteomics, many chemoproteomic techniques leverage the analytic power of mass spectrometry. However, these experiments differ in their set-up and workflows in order to address distinct research questions. For example, activity-based protein profiling (ABPP) is a chemoproteomic method that uses reactive probes to label sites of interest across the treated proteome. In one ABPP workflow, samples are treated either with a pan-cysteine probe alone or with a pan-cysteine probe following pre-incubation with a covalent drug candidate. By comparing ligandable sites in the presence and absence of drug treatment, researchers can comprehensively identify drug-bound loci (from which probes are excluded by the presence of the drug under treatment conditions). Other methods, such as thermal proteome profiling and chaotrope-induced proteome profiling, leverage changes in the stability of a drug-bound target to enable all proteins binding a ligand of interest to be identified and characterized.

Chemoproteomics plays a central role in the discovery and development of novel therapeutics, from target identification through comprehensive pharmacological profiling of a lead compound. For drugs discovered by phenotype-based screening, chemoproteomics enables target deconvolution, or the identification of the binding partner through which a drug exerts its therapeutic effect. For drugs with known targets, chemoproteomics experiments can be used to comprehensively assess off-target interactions and potential toxicity. Chemoproteomics has shown particular value in the discovery and development of covalent therapeutics, protein degraders, and molecular glues, demonstrating how this approach can accelerate research timelines for next-generation drug modalities.

What other omics fields of study exist?

Following in the pattern of genomics, transcriptomics, proteomics, and chemoproteomics, scientists have applied omics terminology to many other fields of study that comprehensively assess a given class of molecules, interactions, etc. Selected examples are provided below to highlight the diversity and value of biological omics studies:

• Epigenomics: The study of the epigenome, or all of the epigenetic marks (such as methylation or acetylation) that are present across the genome and modulate the expression of associated genes
• Metabolomics: The study of the metabolome, or all of the metabolites present in a cell or biological system, to gain insight into cellular metabolism and other biological processes
• Lipidomics: The study of the lipidome, or the full complement of lipids present in a biological sample, often to discover disease-relevant biomarkers
• Surfaceomics: The study of the surfaceome, or all of the proteins present on the cell surface, comprising the majority of drug targets and having further relevance for drug uptake
• Interactomics: The study of the interactome, or the network of physical and functional interactions between proteins and other molecules within a cell, to better understand the complex interrelationships underlying cellular activity
• Multiomics: The synthesis of multiple classes of omic information to generate a holistic picture of cellular state across different domains of study

How does omics research empower drug discovery & development?

From genomics and transcriptomics through proteomics and chemoproteomics, omics research provides valuable information to guide drug discovery and development research. Using these technologies, researchers can elucidate disease mechanisms, identify targets of interest, interrogate drug activity, and monitor treatment efficacy. As the collection and analysis of data on the omic scale has become feasible in recent decades, research teams have benefitted incalculably from being able to comprehensively assess a given class of molecules in a biological system. In contrast to targeted approaches, which may miss unexpected phenomena, unbiased global omics approaches detect the full repertoire of off-targets, downstream effects, and unknown interactors. More recently, omics data has been used as training materialby AI and machine learning platforms, providing a holistic view of cell state to inform computational models. By leveraging omics information, drug discovery and development researchers can accelerate research timelines and bring valuable new therapeutics to the clinic more efficiently than ever.

 

To learn more about how Momentum Biotechnologies can accelerate your drug discovery and development research with our deep proteomics and chemoproteomics expertise, connect with our team.

 

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