Complexity Landscape of Genomics and Transcriptomics for Precision Medicine
Dr. Myroslav Zapukhlyak
February 21, 2025
10 min read
Dr. Myroslav Zapukhlyak
February 21, 2025
10 min read
Ongoing improvements in sequencing technology, big data analytics and AI are radically transforming our ability to understand the molecular basis of disease and expanding our diagnostic horizons. Significant advances in short-read genomic platforms and third-generation systems have reshaped cancer genomics, rare disease research, and broader areas of personalized medicine. At the same time, advances in transcriptomics—from single-cell RNA sequencing (scRNA-seq) to spatial transcriptomics (ST)—are shedding light on previously unappreciated aspects of cellular heterogeneity and tissue organization. When combined with robust computational methods and AI, these tools can reveal clinically important signals that guide diagnostic and therapeutic interventions.
Recent innovations in third-generation sequencing (TGS) are addressing the limitations of earlier next-generation sequencing (NGS) platforms. While established short-read NGS technologies such as Illumina-based approaches are adept at high-throughput variant detection and expression profiling, they can struggle with complex genomic regions and extensive rearrangements [1,2]. In contrast, TGS technologies, including Oxford Nanopore and Pacific Biosciences’ Single-Molecule Real-Time (SMRT) technology, achieve long contiguous reads that resolve large insertions, deletions, and repetitive sequences [3,4]. SMRT sequencing benefits from new polymerase chemistries and “HiFi” read protocols, reducing once high error rates to levels approaching those of short-read systems and capturing entire genes and transcripts in a single pass [5,6]. This complete view of the genome is critical for identifying structural variants relevant to tumor development, drug resistance and inherited diseases. TGS platforms can also detect epigenetic marks – such as DNA methylation – directly, eliminating the need for separate assays [7]. As these methods continue to mature, future improvements in throughput and affordability are likely to see them integrated more regularly into clinical workflows, including rapid tumor profiling and precision diagnostics in neonatal care. On the other side, industry leaders continue to push boundaries in next-generation sequencing. Roche has just unveiled its new Sequencing by Expansion (SBX) technology, a high-throughput, ultra-rapid approach that uses novel chemistry to enhance signal-to-noise ratios and boost accuracy. By reducing the time from sample to genome from days to hours, SBX could transform both large-scale NGS genomics research and clinical diagnostics workflows in terms of the scalability and cost-effectiveness potentially bringing powerful NGS capabilities closer to point-of-care settings [8].
Parallel developments in transcriptomics have increased the resolution at which we can study gene expression, revealing cellular and spatial contexts that are typically obscured by bulk RNA sequencing. Single-cell RNA sequencing (scRNA-seq) now allows researchers to map the transcriptional states of individual cells and identify rare or transient subpopulations within complex tissues [9,10,11]. This approach has already proven invaluable in oncology, where dissecting the microenvironment of a tumor can explain why certain cells resist therapy or promote metastasis. In addition, spatial transcriptomics preserves the physical location of each cell, making it possible to analyze not only the molecular characteristics of cells, but also their proximity and local interactions [12]. Clinically, these combined transcriptomic strategies can elucidate how tumor cells and immune cells co-exist in situ, allowing physicians to develop more targeted interventions for solid tumors and inflammatory diseases.
Illumina has recently announced a next-generation spatial transcriptomics technology, set to launch commercially in 2026, which will offer new scale and resolution for mapping complex tissues. By capturing a wider area and enabling unbiased whole-transcriptome profiling with cellular-level precision, this will further accelerate progress in spatially resolved transcriptomics, lowering barriers to large-scale experiments [13].
The merging of these data modalities and data-intensive methods – genomics, transcriptomics, epigenomics, scRNA-seq and spatial transcriptomics – presents analytical hurdles. Laboratories struggle with variability in data formats, inconsistent workflows, and unpredictable batch effects [14]. Advanced computational pipelines are required to harmonize large datasets, and hardware for computing power, storage resources, and other systemic requirements are becoming unsustainable for individual organizations or laboratories. With its scalability, distributability and reliability, moving the entire infrastructure to the cloud is the right decision. Many established NGS vendors offer access to their own cloud-based solutions, but these are often limited in terms of time, access and supported formats. PAICON offers an integrated platform with a focus on scalable, flexible, and variable data pipelines that can be easily extended or modified for domain-driven specifications, including multimodal analysis, and software integration that seamlessly combines cloud storage solutions with cloud-based AI-driven development. By unifying organizations, teams, and individual users under one cohesive system, PAICON ensures a streamlined learning curve and enables immediate, in-depth data exploration. Interdisciplinary teams from bioinformatics, software engineering and clinical medicine must work together effectively to interpret results in a way that is transparent enough for regulatory review and meaningful for patient diagnosis. Explainable AI and machine learning (ML) algorithms are rapidly evolving to meet these demands. They can detect complex patterns that might elude human analysts, and they also offer traceability to confirm whether particular genomic variants or transcriptomic profiles indeed drive a model’s predictions.
PAICON addresses these complexities and tackles simultaneously the inherent challenges of modern histopathology by systematically integrating diverse sequencing data modalities into the data lake—including short-read genomic sequencing, RNA-seq, and spatial transcriptomics—for a robust development of AI-driven diagnostic models. The PAICON AI team is constantly working on unifying these molecular insights with detailed tissue morphology coming from the Whole Slide Imaging (WSI) data, for more precise cancer profiling, improved tumor subtyping, and enhanced identification of clinically actionable biomarkers. Each additional modality (in our case genomic or any multi-omic) in the AI model can increase the accuracy and speed of histopathological assessment, enabling more personalized treatment strategies and better patient outcomes.
One example is in breast cancer subtyping, where these approaches reveal subtle variant-expression correlations that might guide personalized treatment strategies. Genomic alterations, cell-type composition, or spatial patterns of gene expressions can all be consolidated into comprehensive models that continually improve as more data become available. Such dynamic models can refine how subtypes are defined, allowing clinicians to predict therapy response more accurately than with classification strategies focused on only a handful of known biomarkers.
The quality and variety of data types, along with other factors such as model architecture and integration strategies, are often more effective in improving multimodal AI performance than increasing the volume of training data, as there are diminishing returns beyond certain thresholds for any single data type. At PAICON, we have expertise in developing pipelines to pre-process and harmonize such data to the highest standards.
Regulatory considerations, particularly around data privacy and assay reproducibility, remain key practical barriers. Healthcare standards, such as GDPR in Europe (and HIPAA in the US), require stringent data management solutions to ensure that patient information remains confidential [15]. The cost of TGS platforms and advanced transcriptomic assays can also be prohibitive in some settings, slowing adoption into routine clinical practice [2]. Nonetheless, ongoing innovation and strict adherence to quality management system (QMS) procedures are reducing these barriers, and incentives for healthcare systems to adopt predictive, individualized diagnostics are increasing in parallel with evidence of their clinical utility.
Bright future directions point to an even more integrated multi-omics paradigm. Single-cell and spatial methods are likely to eventually converge with TGS technologies that offer high accuracy alongside real-time measurements, deepening our knowledge of how transcripts and epigenetic states evolve dynamically. CRISPR-based targeted sequencing, increasingly refined polymerase chemistries and machine-learning error-correction techniques all promise greater sensitivity and speed. In oncology particularly, the vision of semi-real-time, on-the-spot sequencing capable of providing instant genomic and transcriptomic profiles is coming into view. As these capabilities advance, clinicians will be empowered to make decisions based on a complete readout of a patient’s genetic architecture, regulatory modifications, and cellular interactions, opening a new era of precision care across diverse medical domains.
This convergence of genomics, emerging transcriptomic technologies, and integrative data analysis heralds important changes in how modern medicine identifies disease mechanisms and manage patient care [16]. By weaving together improvements in NGS/TGS sequencing, single-cell analysis, and spatial transcriptomics—and pairing them with robust AI pipelines and models— PAICON aims to deliver solutions that are as dynamic as the biology they measure. Such efforts ultimately point toward a future where personalized medicine is based on an unprecedented level of molecular detail and clinical decisions are made in near real-time, providing the right treatment to the right patient with greater confidence than ever before.
