Bridging Bench and Code: RSEs Driving Omics

•Integrate multiple omics layers for deeper biological insight. •Design intuitive tools for data exploration and visualisation. •Apply mathematical models (e.g. GRNs) to reveal regulatory mechanisms. •Optimise storage and handling of large genomic datasets (e.g. VCFs). •Develop faster parsing and analysis tools for timely results. •Implement scalable workflows for both research and clinical use. •Evaluate and integrate new tools into workflows efficiently. •Build adaptable, modular pipelines that evolve with emerging methods. •Ensure reproducibility and robustness despite rapid change. Bridging Bench and Code: RSEs Driving Omics Ruxandra Neatu Humans have 23 pairs of chromosomes that carry our DNA. DNA is built from four bases: A, C, T, and G. Genes are specific sequences of these bases, and their precise order encodes the instructions that define who we are. Modern sequencing machines convert DNA into digital files of A, C, T, and G sequences forming the basics of omics. "Omics" are large-scale studies of biological molecules: •Genomics - DNA, our genetic blueprint. •Transcriptomics - RNA, copied from active DNA. •Proteomics - proteins, built from RNA. •Epigenomics - chemical modifications affecting gene activity. Sequencing machines read short DNA fragments (“reads”), which bioinformaticians reassemble – like a puzzle – to identify genes and variations in the genome. Once reads are assembled, scientists have a digital replica of the genome. Each chromosome is reconstructed end to end, with every base assigned a position (no.), allowing precise reference for analysis. Downstream analysis varies widely, driven by evolving tools and software, as well as specific research questions. As a result, methods and best practices shift rapidly over time. Genetic variations are often studied using VCF files (see above), which record mutations across the ~3 billion bases of the genome. Files range from megabytes to gigabytes, with multi-sample sets even larger. Compression saves space but slows analysis, posing challenges for timely clinical diagnosis. Cells operate across several layers: Genomics, Transcriptomics, Proteomics, and Epigenomics. For example, Gene Regulatory Networks (GRNs) (see above) integrate transcriptomics and epigenomics. GRNs can be modelled mathematically, enabling network analysis, statistics, and machine learning to reveal regulatory mechanisms and disease pathway. RSEs in life sciences Domain-Specific Insight Software Development Data Manipulation Figures: Created using a combination of BioRender, AI-generated illustrations, and original designs. Introduction Context