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Using Computational Genomics to Investigate Human Disease
Research
The Marth Laboratory at the University of Utah Medical School is developing computational tools for biomedical data analysis in rare-disease genomics, precision oncology, and somatic mosaicism discovery. Our team is part of the Utah Center for Genetic Discovery, a community of doctoral and postdoctoral trainees, data engineers, statisticians, and faculty, all engaged in computational genomics research.
Clinical Genomics
Computational tools for rare-disease diagnostics.
As part of the NIH-funded Undiagnosed Diseases Network (UDN) Data Management and Coordination Center, the Marth Laboratory is building a comprehensive rare-disease patient data management platform and a large set of diagnostic tools for improving diagnostic rates in the UDN patient cohort. We are developing machine learning approaches for phenotype-driven patient-matching that leverage the UDN patients and additional large disease cohorts to identify groups of patients with similar phenotypes to identify shared causative genes within the group.
Precision Oncology
Machine learning algorithms for anticancer therapy selection
We are building novel deep-learning algorithms for personalizing treatment for each cancer patient based on the omic and pharmacologic characteristics of the patient’s tumor. In collaboration with cancer biologists, pharmacologists, and oncologists at the University of Utah and Huntsman Cancer Institute, we are implementing and testing these approaches in the clinic, currently for the treatment of advanced/metastatic breast cancers and primary/recurrent brain cancers. We are funded by the National Cancer Institute to utilize our learning approaches for identifying efficacious combination therapies for the treatment of breast cancer.
Somatic Mosaicism
Computational algorithms for somatic mosaicism discovery
The Marth Laboratory is a funded participant of the Somatic Mosaicism across Human Tissues (SMaHT) Network, an NIH initiative to transform our understanding of how somatic mosaicism in organs, tissues, and cells influences human biology and disease (more about SMaHT). Our main contribution is the development of reference-free computational algorithms for detecting somatically acquired (rather than inherited) mutations from voluminous, high-throughput DNA sequencing data generated by the SMaHT Consortium for the construction of a comprehensive Healthy Human Somatic Mosaicism Atlas across multiple tissues in hundreds of donors.
Visually-driven Genomics
Interactive tools for biomedical data analysis
We are developing highly visual, intuitive tools for real-time genome data browsing, disease variant prioritization, and metagenomics. Our approach prioritizes intuitive and interactive visual presentations of complex genomic data, making our software broadly accessible, regardless of computational expertise.
Software
iobio
Realtime genomic data visualization and analysis web tools
- gene.iobio - interactive visual variant annotation and prioritization
- clin.iobio - comprehensive clinical variant review workflow
- genepanel.iobio - phenotype and disorder associated gene lists
- bam.iobio - alignment file quality control
- vcf.iobio - variant calling file quality control
- oncogene.iobio - variant annotation and tracking in oncology studies
- taxonomer.iobio - ultra-fast metagenomic analysis
RUFUS
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
freebayes
Bayesian haplotype-based variant calling
superseeker
Computational reconstruction of tumor clones
bayescmg
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
ped_draw
Pedigree drawing with ease
- quick and easy pedigree visualization
- simple one-liner syntax
- no dependencies
bamtools
C++ API & command-line toolkit for working with BAM data
Tangram
Fast structural variation detection toolbox
Publications
Some of our recent and featured publications are shown below, but you can find all of our publications on PubMed and Google Scholar
Apply!
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!
Members
Lab Alumni
Corin Thummel
Business Data AnalystFLSmidth & Co.
Matthew Bailey
Assistant ProfessorBrigham Young University
Preetida Bhetariya
Bioinformatics Analyst / RAHarvard School of Public Health
Erik Garrison
Postdoctoral Research FellowUniversity of California, Santa Cruz
Contact
Our lab is part of the Utah Center for Genetic Discovery (UCGD) and the Department of Human Genetics at the University of Utah