Team
The lab opened in January 2026 — we are actively building the team.
We develop methods to build and analyze pangenomes, with applications in cancer and complex disease.
Translational Genomics Research Institute · Phoenix, AZ
We span the full pangenomics stack, from pangenome×assembly free of single-reference bias, through pangenome×compression into unified queryable graphs, to pangenome×association powered by graph-based genotyping — applying it all to cancer×pangenomics and complex×diseases.
We reduce the cost and complexity of germline genome assembly, making personalized pangenomics practical for clinical and research applications. This includes developing streamlined pipelines and benchmarking assembly approaches across sequencing platforms.
We develop algorithms and data structures for compressing pangenome alignments and graphs. Using techniques like tracepoints and implicit representations, we enable storage and querying of population-scale genomic data without sacrificing base-level resolution.
We leverage pangenomes for genome-wide association studies, enabling discovery of trait associations with structural variants and sequences absent from linear reference genomes. Our methods use pangenome graph features as genetic markers rather than variants called against a reference.
We construct personalized pangenomes from tumor and matched normal samples using long-read sequencing and de novo assembly. This approach reveals somatic structural variation that single reference-based approaches miss.
PanGenome Graph Builder. Reference-free pangenome graph construction from whole-genome alignments.
github.com/pangenome/pggbOptimized Dynamic Genome/Graph Implementation. Toolkit for manipulating and analyzing pangenome graphs.
github.com/pangenome/odgiImplicit Pangenome Graph. Memory-efficient pangenome queries without explicit graph construction.
github.com/pangenome/impgAlignment compression using adaptive tracepoints for compact storage and fast reconstruction.
github.com/AndreaGuarracino/tracepointsThe lab opened in January 2026 — we are actively building the team.
Develop computational methods for cancer pangenomics. Access to in-house PacBio Revio and ONT PromethION platforms. Collaborate with T2T and HPRC consortia. Pursue your own research ideas.
Required: PhD in Bioinformatics/CS/CompBio, DNA sequencing analysis, Python/R, Linux/HPC.
Preferred: Long-read analysis, C/C++/Rust, genome assemby, pangenome methods, workflow managers.
Full job posting coming soon.
APPLYFull list on ORCID or Google Scholar.
