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Cancer genomics
We investigate tumor evolution through structural variation, copy-number profiles, fusion detection, ecDNA, and multi-omic analysis with a focus on clinically relevant and underrepresented cancer cohorts.
Di Genoma Lab develops algorithms, sequencing workflows, and reproducible software to study tumor evolution, structural variation, genome assembly, and Chilean genomic diversity. We combine sequencing, high-performance computing, and method development inside one research group.
Our work sits at the intersection of cancer genomics, long-read technologies, algorithm design, and population-scale analysis. We prioritize methods that can survive contact with real data and real collaborators.
We investigate tumor evolution through structural variation, copy-number profiles, fusion detection, ecDNA, and multi-omic analysis with a focus on clinically relevant and underrepresented cancer cohorts.
We use long-read and hybrid sequencing to resolve assemblies, complex loci, haplotypes, epigenetic marks, and genomic regions that remain difficult to interpret with standard short-read approaches.
We study Chilean genomic diversity, metagenomic communities, phylogenomics, and evolutionary processes through scalable computation and reproducible statistical analysis.
These are the projects that best describe the group’s current profile: method development, Chilean genomics, and cancer-focused translational analysis.
Building haplotype-resolved genomic resources to better represent Chilean population diversity and improve downstream clinical and research interpretation.
Characterizing structural complexity, copy-number change, and multi-omic tumor states in prevalent and underrepresented Chilean cancer cohorts.
Designing fast, portable algorithms and workflows for genome assembly, metagenomic reconstruction, and production-scale genomic computing.
The group runs local sequencing and high-performance computing resources so projects can move directly from raw signal to interpretation without fragile handoffs or ad hoc environments.
We operate Oxford Nanopore platforms including GridION and P2 Solo for long-read sequencing, structural variant detection, transcriptomics, targeted assays, and epigenomic applications.
Kütral is our local computational backbone for large-scale genomics. It supports production workflows, algorithm development, high-memory genome assembly, and institutionally controlled analysis.
We write software as part of the science. Our tools are designed for reproducibility, portability, and strong performance on institutional servers and HPC environments.
The group combines bioinformatics, engineering, genomics, and multi-omics analysis. We build methods, operate infrastructure, and work directly with biological questions rather than separating those roles.
Leads the group’s work in cancer genomics, genome assembly, long-read sequencing, and algorithm development for large genomic datasets.
Focuses on multi-omics integration, cancer biology, complex trait analysis, and phylogenomics.
Works on hybrid assembly, haplotype-aware genome reconstruction, HPC environments, and workflow design.
Contributes comparative genomics, phylogenetics, evolutionary analysis, and cancer-related genomic research.
Brings biotechnology, protein engineering, and translational cancer initiative experience to the group.
Applies deep learning and machine learning to genomics, epigenetics, protein engineering, and medical image analysis.
Our publication record spans cancer biology, genome assembly, population genomics, and computational method development.
Generating accurate genome assemblies of large, repeat-rich human genomes has proved difficult using only long, error-prone reads, and most human genomes assembled from long reads add accurate short …
Malignant pleural mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Through a large series of whole-genome sequencing data, integrated with …
This study reports a high-quality assembly of the male Silene latifolia genome and resolves one of the largest known plant Y chromosomes. The work links repeat accumulation, recombination suppression, …
Across discovery and validation cohorts, this work shows that TERT expression separates pulmonary carcinoids into biologically and clinically distinct groups. High TERT expression is associated with …
We welcome collaborations in cancer genomics, genome assembly, long-read sequencing, comparative genomics, and reproducible workflow engineering.