Using computational genomics to investigate human disease

Our Software Our Team


We develop software tools for genomic data analysis using a combination of biological, statistical, and engineering approaches to accurately detect and interpret inherited and somatic genetic variants. We focus on developing easy-to-use, web-based, visually-driven, interactive and real-time software tools for intuitive analysis of genomic data. Our overall goal is to develop and apply software to better the care and outcomes of patients.

Clinical Genomics

We collaborate with medical geneticists and clinicians across a broad spectrum of human diseases, investigating the genetic etiology of these diseases and potential therapeutic opportunities.

Precision Oncology

We build and apply methods and visualizations for comprehensive profiling of tumors and their clonal structure across disease progression and therapeutic interventions to directly impact the patient's care.

Variant Discovery

Building on our lab's previous work, we continue to develop computational algorithms for variant discovery, including the accurate detection of germline de novo and somatic cancer variants.

Visually-driven Genomics

Our software development approach prioritizes intuitive and interactive visual presentations of complex genomic data, making our software broadly accessible, regardless of computational expertise.



Realtime genomic data visualization and analysis web tools


K-mer based de novo variant calling

  • direct comparison of k-mers in sequencing reads
  • has no reference alignment bias
  • is powered to call all variant types and sizes


Graph-based variant adjudication

  • improves germline and somatic variant call evidences
  • retains true positives, discards false positives
  • helps during joint calling on n+1 samples


Bayesian haplotype-based variant calling


Computational reconstruction of tumor clones


An applied Bayesian framework for the ACMG/AMP criteria

  • automatically applies ACMG criteria to vcf records
  • calculates a simple pathogencity probability
  • filters and prioritizes variants


Pedigree drawing with ease

  • quick and easy pedigree visualization
  • simple one-liner syntax
  • no dependencies


C++ API & command-line toolkit for working with BAM data


Fast structural variation detection toolbox


Some of our recent and featured publications are shown below, but you can find all of our publications on PubMed and Google Scholar


We are always looking for talented and motivated people to join our team! We are currently looking for post-docs with an emphasis in biostatistics and mathematics, as well as graduate students through the Utah Bioscience PhD and MD/PhD programs. Email us or check our current job postings!


Gabor Marth

Principle Investigator
Professor of Human Genetics

Matt Velinder

Lab Manager
Clinical Genomics Lead / iobio

Alistair Ward

Director of Research and Science
CEO, Co-Founder Frameshift Genomics

Yi Qiao

Director of Research and Science
Precision Oncology Lead / iobio

Andrew Farrell

Research Associate
RUFUS Developer

Tony DiSera

Senior Software Developer
Clinical Genomics / iobio

Xiaomeng Huang

Assoc. Dir. of Research and Science
Precision Oncology

Stephanie Georges

Software Developer
Precision Oncology / iobio

Anders Pitman

Software and Backend Developer

Szabolcs Tarapcsak

Postdoctoral Fellow
Precision Oncology

Niki Williams

PhD Student
Somatic Mosaicism in Haematopoiesis

John Chamberlin

DBMI PhD Student
Single-Cell Bioinformatics

Gage Black

PhD Student
Precision Oncology

Isabelle Cooperstein

PhD Student
Genotype:Phenotype Correlations

Nancy Benson

Administrative Manager
Department of Human Genetics

Lab Alumni

Corin Thummel

Business Data Analyst
FLSmidth & Co.

Matthew Bailey

Assistant Professor
Brigham Young University

Preetida Bhetariya

Bioinformatics Analyst / RA
Harvard School of Public Health

Erik Garrison

Postdoctoral Research Fellow
University of California, Santa Cruz

Meet Us!

We regularly attend meetings and conferences. In the coming months you can find us at:

American College of Medical Genetics (ACMG) Annual Clinical Genetics Meeting, Virtual, April 13-16 2021
- Poster #eP161: "bAyesCMG: a high throughput computational tool for applying ACMG/AMP criteria and generating Bayesian pathogenicity probabilities"

Cold Spring Harbor Laboratory (CSHL) Biology of Genomes, Virtual, May 11-14 2021


Our lab is part of the Utah Center for Genetic Discovery (UCGD) and the Department of Human Genetics at the University of Utah


15 N 2030 E, Salt Lake City, UT

Phone Number

(801) 581-4422