How to read a genome in 2025: a computational perspective on the code of life

In 2001, sequencing the first human genome was a monumental feat: a US$3 billion, multi-institutional effort that took over a decade. Two decades later, the cost of genome sequencing has plummeted to under US$200, and the turnaround time is measured in hours, not years. While generating the data has become nearly trivial, interpreting the genome and extracting actionable meaning from billions of nucleotides remains profoundly complex. I’m often asked: “So if I get my genome sequenced, what will it tell me?” The answer is both exciting and humbling. In 2025, we can read nucleotides with unprecedented ease. However, extracting knowledge for health, disease and identity still requires sophisticated algorithms, deep biological context and expert human judgment; the true challenge is bridging data and understanding. Your genome is composed of approximately 3 billion base pairs of DNA, organized across 23 pairs of chromosomes. It encodes the instructions for building and regulating every cell in your body. Yet only about 1–2% of the genome is protein-coding; the rest consists of regulatory elements, noncoding RNAs, structural regions and vast stretches we still don’t fully understand. Think of the genome as a dense manuscript. Only fragments are annotated, and many sections remain unexplored. To move beyond simply reading the sequence, we must become fluent in the language of biology, variation and probability. This fluency is especially critical as we process raw sequencing data. The average human genome differs from the reference at approximately 4–5 million positions. Most of these variations are benign, while a smaller fraction – such as nonsense mutations in disease-associated genes – can have significant consequences. Furthermore, many variations lie in a grey zone; these are called variants of uncertain significance. Computational tools lend a helping hand We use population frequency data (e.g., gnomAD), functional annotations (e.g., Ensembl, RefSeq), and in silico prediction tools (e.g., CADD, REVEL, SpliceAI) to assess potential impact. However, computational predictions must always be interpreted in context – considering gene function, inheritance patterns and the patient's phenotype. Interpretation isn’t binary. Instead, it is a probabilistic, evolving process shaped by both new discoveries and advances in computational methods. In recent years, machine learning and deep learning models have begun to augment human interpretation. Tools like AlphaMissense, EVE and SpliceAI use evolutionary conservation and neural networks to assess the likelihood that a variant is deleterious. These models are powerful but not infallible. They perform well on certain variant classes but may generalize poorly in underrepresented genomic contexts or populations. As with any AI application in healthcare, transparency, bias mitigation and validation are essential. In practice, AI techniques serve to augment expert judgment, rather than replace it. These insights set the stage for what is now possible through genome interpretation in 2025. Practical tips for using these tools effectively: Cross-reference multiple tools: no single predictor is definitive. Use ensemble approaches (e.g., combining CADD, REVEL and SpliceAI) to strengthen confidence in variant interpretation. Check versioning and genome build compatibility: tools like SpliceAI and CADD are sensitive to genome builds (e.g., GRCh37 vs GRCh38). Mismatches can lead to incorrect predictions. Use population-specific frequency filters: when using gnomAD, filter by ancestry-matched subpopulations to avoid misclassifying common variants as rare. Document assumptions and thresholds: clearly state why a variant was flagged as potentially deleterious (e.g., CADD > 20, REVEL > 0.7), especially when sharing results with collaborators or clinicians. Common pitfalls and quirks to watch for when using these tools: Overreliance on scores: high CADD or REVEL scores do not guarantee pathogenicity. These tools are probabilistic, not diagnostic. Misinterpretation of noncoding variants: tools like SpliceAI can predict splicing effects, but predictions in deep intronic regions often require experimental validation. Bias in training data: many models are trained on datasets enriched for European ancestry, which can reduce accuracy in underrepresented populations. Tool updates and reclassifications: as models evolve, variant scores may change. Periodic reanalysis is essential, especially for variants of uncertain significance. Hosting your bioinformatics software on GitHub: a comprehensive guide In the rapidly evolving field of bioinformatics, sharing your software and tools is essential for collaboration and progress. GitHub, a leading platform for version control and collaboration, offers an excellent environment for hosting your bioinformatics software. In this guide, we’ll walk you through the steps to get your software on GitHub, ensuring it’s accessible, maintainable and ready for collaboration. Our current capabilities and the gaps we need to bridge In 2025, we can extract meaningful insights from personal genomes, including: Carrier status for recessive Mendelian conditions; Pharmacogenomic variants that influence drug metabolism; Pathogenic variants in high-penetrance genes (e.g., BRCA1/2, LDLR); Ancestry and genetic relationships, with increasing precision. However, what remains challenging are polygenic traits (like diabetes or depression), gene–environment interactions and rare variants in genes with limited functional annotation. In these domains, interpretation is still largely inferential, underscoring gaps in our current capabilities. A persistent issue in genomics is the lack of diversity in reference datasets. Most large-scale genomic resources are disproportionately composed of individuals of European ancestry. This introduces significant bias into variant interpretation and reduces accuracy for underrepresented populations. Efforts like All of Us, H3Africa and GA4GH are addressing this gap, but equitable interpretation remains a global challenge – and a computational one. Building inclusive reference panels, designing population-aware models and ensuring open data sharing will be critical to scientific and clinical progress. Looking ahead By 2030, we may see: Universal newborn sequencing, with automated clinical reporting; Real-time integration of genomic data into electronic health records; Multi-omic interpretation, integrating genomics with transcriptomics, proteomics and environmental exposures. As we move forward, the central role of computational biology will only grow, bridging data, prediction and understanding. Each advance brings us closer to routine, actionable use of genomic information. About the author Learn more about BioTechniques Advisory Board Member Jasmine Baker. Jasmine Baker has been working in the field of genomics and bioinformatics, doing translational and clinical work for approximately 9 years. Her journey into this space began during her PhD at Louisiana State University (LA, USA), where she was fascinated by the extensive insights one could gain from sequencing data and the computational pipelines to streamline data analysis. 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