In the past decades, quickly evolving sequencing technology accompanied with big data in computational science has driven a lot of novel medical applications, especially enabling the discovery of associations and causalities. Genome-wide association study (GWAS) have identified thousands of genomic variants associated with biological traits of diseases and used to predict disease risk. Furthermore, high throughput genomic sequencing technology boosts such applications into unprecedented scale.

In parallel, proteomics-driven biomarker discovery is essential to explore dysregulated pathways that ultimately cause disease. Despite the central importance of proteins in disease biology, large-scale analyses of proteomic data are only now becoming more feasible, largely due to the technical challenges of profiling proteins in a proteome-wide, populational-scale manner. The proteomics community is constantly seeking methods that could quantify whole-proteome containing thousands of proteins across a wide dynamic range in thousands of samples, while minimising the requirement of sample amount, data acquisition time, and the analysis time needed. Recent progress of mass spectrometry (MS) based approach using data-independent acquisition (DIA), immune-affinity based approach like Olink, and aptamer-based method like SomaScan provides promising insight into human biology.

Proteins promote key functions in all biological processes and serve as an intermediate of how genetic risk factors could be linked to clinical outcomes. Thus, associations between multiple layers of molecular information, such as protein expression and DNA sequence variants that correlate with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers.

Integrating proteomics with genetic information has been an emerging strategy for research and the pharmaceutical industry. For example, protein quantitative trait loci (pQTL) studies identify genetic variants that regulate protein expression and offer a route to a comprehensive molecular interpretation of disease GWAS hits. On the other hand, proteogenomics utilises an approach to customise protein sequence databases generated using genomic and transcriptomic information. Thanks to drastic improvement of the depths and throughput of next generation sequencing (NGS), MS based proteomics, as well as novel artificial intelligence (AI) based computational methodologies, proteogenomics is promising to help identify novel peptides that are not represented in the “classical” proteome reference sequence database.

From an “omics” perspective, the research and medical field were historically dominated by single modality alone. While genomics and proteomics still have a lot of their own challenges and high potential to work with, we expect integrative system biology approaches can illuminate the complex biology that is either difficult to obtain or not possible through standalone regime, helping make precision medicine more precise and drug discovery more druggable.

This Research Topic will accept submissions of the following article types: Brief Research Reports, Clinical Trials, General Commentaries, Hypothesis & Theory, Methods, Mini Reviews, Opinion, Original Research, Perspectives, Reviews, and Systematic Reviews. Specific topics to be addressed include, but are not limited to:

- Genomics and/or Proteomics based discovery of new drugs and vaccines

- Genomics and/or proteomics driven biomarker discovery, diagnosis, mechanism or action, etc.

- MultiOmics approach to investigate disease-associated pathways, revealing novel drug targets and translational biomarkers

- New proteogenomics approach to improve proteomics application like research and precision medicine

- Computational and bioinformatic method for discovery of associations and causalities in human disease

- AI/ML based approach to integrate high dimensional genomic and proteomics information

Please note: If patient data are analysed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities as well as additional clinical information and assessment of clinical response/effects should be included. If genetic, proteomics, metabolomics, or other omics data are analysed, a comprehensive description of the methods and the rationale for the selection of the specific data studied should be provided. Studies related to natural compounds, herbal extracts, or traditional medicine products, are outside the scope of this Research Topic and should instead be submitted to the specialty section Ethnopharmacology.

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