We combine computational and genomic techniques to explore genome biology and the genetic basis of traits.

News

We are grateful to have been awarded funding from the Chan-Zuckerberg Initiative to further develop our bedtools and Go Get Data (GGD) projects as part of their Essential Open Source Software for Science program. Details about our efforts can be found here.
Tom Sasani's manuscript on germline mutation in large human pedigrees is out in eLife. Using sequencing data from 33 large, three-generation CEPH families, Tom found significant variability in parental age effects on DNM counts across families. He also discovered that nearly 10% of DNMs that would typically be attributed to the germline, are, in fact, post-zygotic. Read the manuscript and check out our open science repository of the code and data used in this study. Lastly, Tom gives an insightful interview in the eLife Podcast (min 7:11) if you are interested in learning more.
Our manuscript identifying constrained coding regions in the human genome was just published in Nature Genetics, and it made the cover! We encourage you to read our blog post describing the motivation and key results. In addition, there is a nice writeup of our work and a short video below providing a high level overview.
Our research into the genetic basis of rare human diseases is featured in a recent Scope Radio interview: "Backed by Computer Power, Scientists Are Finding the Causes of Mysterious Diseases"
We are always looking to add motivated, talented graduate students and postdoctoral scientist to our team. If interested, please email Aaron Quinlan articulating your research experience and career goals.

Research

The research in our laboratory is focused on the application of computational methods to develop a deeper understanding of genetic variation in diverse contexts. Modern experimental methods allow us to examine entire genomes with exquisite detail. Perhaps not surprisingly, staggering complexity is revealed as we look more closely at how genetic variation (both inherited and somatic) contributes to phenotypes. Modern genomic technologies necessitate efficient approaches for exploring, manipulating and comparing large genomic datasets. We develop such methods so that we and others may apply them to experiments investigating the impact of genetic variation on human disease, evolution, and somatic differentiation. Genome research is difficult - we strive to develop computational means that make it easier.

  • Rare disease genetics

    We develop and apply new software for identifying causal genetic variants in studies of rare familial disease. The University of Utah has a long history of expertise in this area and we work closely with many clinical collaborators to solve rare disease. Our GEMINI software is central to these efforts, and our laboratory collaborates with other members of the Utah Center for Genetic Discovery to study familial disease among the large pedigrees in the Utah Genome Project.

  • Structural variation

    Human chromosomes harbor hundreds of structural differences including deletions, insertions, duplications, inversions, and translocations. Collectively, these differences are known as "structural variation" (or, "SV"). Any two humans differ by thousands of structural variants which vary greatly in size and phenotypic consequence. However, we are just beginning to understand the contribution of SV to evolution, development, and complex disease. Our laboratory continues to develop new methods such as LUMPY for detecting and understanding structural variation using modern DNA sequencing techniques.

  • Cancer genomics and genome evolution

    Massively parallel DNA sequencing has yielded detailed maps of clonal variation in human cancer, through an inference of clonal substructure by analysis of variant allele frequencies in bulk tumor cell populations and direct sequencing of single cells. Dynamic changes in clonal structure over time and under the selective pressure of treatment have been extensively studied in hematologic malignancies, but are less well characterized in solid cancers. Our understanding of the dynamics of clonal change and its role in therapeutic response and the emergence of resistance is in its infancy. However, deeper insight is accessible via significant advances in sequencing and new algorithms. We are developing new methods to identify genomic changes that are responsible for clonal evolution, chemoresistance, and relapse.

  • Algorithm and Software Development

    Broadly speaking, the research in my laboratory marries genetics with genomics technologies, computer science, and machine learning techniques to develop new strategies for gaining insight into genome biology. We try to tackle challenging problems with practical importance to understanding genome variation in the context of human disease. We actively maintain a broad range of widely used tools for genome research including: BEDTOOLS, GEMINI, LUMPY, VCFANNO, PEDDY, and GQT.

Software

We strive to develop innovative, well-tested, and well-documented tools for genome research.

STRling

Detect novel (and reference) STR expansions from short-read sequencing data

indexcov

indexcov - crazy fast genome coverage estimates

bedtools

a swiss-army knife for genome intervals

lumpy

a probabilistic framework for SV discovery

gemini

a flexible framework for exploring genome variation

gqt

Genotype Query Tools

poretools

A toolkit for working with nanopore sequencing data from Oxford Nanopore.

speedseq

ultra-fast personal genome analysis and interpretation

vcfanno

A fast, flexible toolset for annotating VCF files.

peddy

Detect sample mixups in family based studies of disease.

Giggle

A scalable, multi-file index for fast queries of genomic intervals.

Publications

Preprints

Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches

Brent S Pedersen, Preeti J Bhetariya, Joe Brown, Gabor Marth, Randy L Jensen, Mary P Bronner, Hunter R Underhill, Aaron R. Quinlan.

https://www.biorxiv.org/content/10.1101/839944v2

Lower germline mutation rates in young adults predict longer lives and longer reproductive lifespans

Richard M. Cawthon, Huong D. Meeks, Thomas A. Sasani, Ken R. Smith, Richard A. Kerber, Elizabeth O'Brien, Aaron R. Quinlan, Lynn B. Jorde.

https://www.medrxiv.org/content/10.1101/19004184v2

2019

Large, three-generation CEPH families reveal post-zygotic mosaicism and variability in germline mutation accumulation

Thomas. A Sasani, Brent S. Pedersen, Ziyue Gao, Lisa Baird, Molly Przeworski, Lynn B. Jorde, Aaron R. Quinlan.

eLife, https://elifesciences.org/articles/46922

Duphold: scalable, depth-based annotation and curation of high-confidence structural variant calls.

Brent S. Pedersen, Aaron R. Quinlan.

GigaScience, https://doi.org/10.1093/gigascience/giz040

Overlooked roles of DNA damage and maternal age in generating human germline mutations.

Ziyue Gao, Priya Moorjani, Thomas A. Sasani, Brent S. Pedersen, Aaron R. Quinlan, Lynn B. Jorde, Guy Amster, Molly Przeworski.

PNAS, https://doi.org/10.1073/pnas.1901259116

Coexpression patterns define epigenetic regulators associated with neurological dysfunction.

Leandros Boukas, James M. Havrilla, Peter F. Hickey, Aaron R. Quinlan, Hans T. Bjornsson, Kasper D. Hansen.

Genome Research, https://doi.org/10.1101/gr.239442.118p>

2018

A map of constrained coding regions in the human genome.

James M. Havrilla, Brent S. Pedersen, Ryan M. Layer, Aaron R. Quinlan

Nature Genetics, https://doi.org/10.1038/s41588-018-0294-6

Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder.

An JY, Lin K, Zhu L, Werling DM, Dong S, Brand H, Wang HZ, Zhao X, Schwartz GB, Collins RL, Currall BB, Dastmalchi C, Dea J, Duhn C, Gilson MC, Klei L, Liang L, Markenscoff-Papadimitriou E, Pochareddy S, Ahituv N, Buxbaum JD, Coon H, Daly MJ, Kim YS, Marth GT, Neale BM, Quinlan AR, Rubenstein JL, Sestan N, State MW, Willsey AJ, Talkowski ME, Devlin B, Roeder K, Sanders SJ.>

Science, doi: 10.1126/science.aat6576

Long read sequencing reveals poxvirus evolution through rapid homogenization of gene arrays.

Sasani TA, Cone KR, Quinlan AR, Elde NC.

eLife, doi: 10.7554/eLife.35453

Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy.

Ostrander BEP, Butterfield RJ, Pedersen BS, Farrell AJ, Layer RM, Ward A, Miller C, DiSera T, Filloux FM, Candee MS, Newcomb T, Bonkowsky JL, Marth GT, Quinlan AR

Nature Genomic Medicine, doi: 10.1038/s41525-018-0061-8

Coloc-stats: a unified web interface to perform colocalization analysis of genomic features.

Simovski B, Kanduri C, Gundersen S, Titov D, Domanska D, Bock C, Bossini-Castillo L, Chikina M, Favorov A, Layer RM, Mironov AA, Quinlan AR, Sheffield NC, Trynka G, Sandve GK.

Nucleic Acids Research, doi: 10.1093/nar/gky474

SV-plaudit: A cloud-based framework for manually curating thousands of structural variants.

Belyeu JR, Nicholas TJ, Pedersen BS, Sasani TA, Havrilla JM, Kravitz SN, Conway ME, Lohman BK, Quinlan AR, Layer RM.

Gigascience, doi: 10.1093/gigascience/giy064

hts-nim: scripting high-performance genomic analyses.

Pedersen BS, Quinlan AR

Bioinformatics, doi: 10.1093/bioinformatics/bty358

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder.

Donna M Werling, Harrison Brand, Joon-Yong An, Matthew R Stone, Joseph T Glessner, Lingxue Zhu, Ryan L Collins, Shan Dong, Ryan M Layer, Eiriene-Chloe Markenscoff-Papadimitriou, Andrew Farrell, Grace B Schwartz, Benjamin B Currall, Jeanselle Dea, Clif Duhn, Carolyn Erdman, Michael Gilson, Robert E Handsaker, Seva Kashin, Lambertus Klei, Jeffrey D Mandell, Tomasz J Nowakowski, Yuwen Liu, Sirisha Pochareddy, Louw Smith, Michael F Walker, Harold Z Wang, Mathew J Waterman, Xin He, Arnold R Kriegstein, John L Rubenstein, Nenad Sestan, Steven A McCarroll, Ben M Neale, Hilary Coon, A. Jeremy Willsey, Joseph D Buxbaum, Mark J Daly, Matthew W State, Aaron Quinlan, Gabor T Marth, Kathryn Roeder, Bernie Devlin, Michael E Talkowski, Stephan J Sanders

Nature Genetics, DOI: 10.1038/s41588-018-0107-y

Nanopore sequencing and assembly of a human genome with ultra-long reads

Miten Jain, Sergey Koren, Josh Quick, Arthur C Rand, Thomas A Sasani, John R Tyson, Andrew D Beggs, Alexander T Dilthey, Ian T Fiddes, Sunir Malla, Hannah Marriott, Karen H Miga, Tom Nieto, Justin O'Grady, Hugh E Olsen, Brent S Pedersen, Arang Rhie, Hollian Richardson, Aaron Quinlan, Terrance P Snutch, Louise Tee, Benedict Paten, Adam M. Phillippy, Jared T Simpson, Nicholas James Loman, View ORCID ProfileMatthew Loose

Nature Biotechnology, DOI: 10.1038/nbt.4060

GIGGLE: a search engine for large-scale integrated genome analysis

Ryan M. Layer, Brent S. Pedersen, Tonya DiSera, Gabor T. Marth, Jason Gertz, Aaron R. Quinlan

Nature Methods, doi: 10.1038/nmeth.4556

mosdepth: quick coverage calculation for genomes and exomes

Brent S. Pedersen and Aaron Quinlan

Bioinformatics doi.org/10.1093/bioinformatics/btx699

2017

Indexcov: fast coverage quality control for whole-genome sequencing.

Brent S. Pedersen, Ryan L Collins, Michael E Talkowski, Aaron Quinlan

GigaScience doi.org/10.1093/gigascience/gix090

Settling the score: variant prioritization and Mendelian disease.

Karen Eilbeck*, Aaron Quinlan*, Mark Yandell

Nature Reviews Genetics doi:10.1038/nrg.2017.52

Combating subclonal evolution of resistant cancer phenotypes.

Andrea Bild, Samuel Brady, Jasmine McQuerry, Yi Qiao, Stephen Piccolo, Gajendra Shrestha, Ryan Layer, Brent Pedersen, David Jenkins, Ryan Miller, Amanda Esch, Sara Selitsky, Joel Parker, Layla Anderson, Chakravarthy Reddy, Jonathan Boltax, Dean Li, Philip Moos, Joe Gray, Laura Heiser, W. Evan Johnson, Saundra Buys, Adam Cohen, Quinlan AR, Gabor Marth, Theresa Werner, Brian Dalley, and Rachel Factor

Nature Communications, doi:10.1038/s41467-017-01174-3

Identification of ATIC as a novel target for chemoradiosensitization.

Xiangfei Liu, Uma Devi Paila, Sharon N. Teraoka, Jocyndra A. Wright, Xin Huang, Quinlan AR, Richard A. Gatti and Patrick Concannon

International Journal of Radiation Oncology, doi:10.1016/j.ijrobp.2017.08.033

Who’s Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

Pedersen BS, Quinlan AR†

AJHG doi: 10.1016/j.ajhg.2017.01.017

cyvcf2: fast, flexible variant analysis with Python.

Pedersen BS, Quinlan AR†

Bioinformatics doi: 10.1093/bioinformatics/btx057

2016

Vcfanno: fast, flexible annotation of genetic variants.

Pedersen BS, Layer RM, Quinlan AR†

Genome Biol. doi: 10.1186/s13059-016-0973-5

Targeted Deep Sequencing in Multiple-Affected Sibships of European Ancestry Identifies Rare Deleterious Variants in PTPN22 that Confer Risk for Type 1 Diabetes.

Ge Y, Onengut-Gumuscu S, Quinlan AR, Mackey AJ, Wright JA, Buckner JH, Habib T, Rich SS, Concannon P.

Diabetes. pii: db150322

2015

Efficient genotype compression and analysis of large genetic-variation data sets.

Layer RM, Kindlon N, Karczewski K, Exome Aggregation Consortium, Quinlan AR†

Nature Methods. doi:10.1038/nmeth.3654

A parallel algorithm for N-way interval set intersection.

Layer RM, Quinlan AR†

IEEE Proceedings.

Speedseq: Ultra-fast personal genome analysis and interpretation.

Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, Marth GT, Quinlan AR, Hall IM.

Nature Methods. doi:10.1038/nmeth.3505

Rare and Coding Region Genetic Variants Associated With Risk of Ischemic Stroke: The NHLBI Exome Sequence Project.

Auer PL, et al.

JAMA Neurology. doi:10.1001/jamaneurol.2015.0582

Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers.

Onengut-Gumuscu S, Chen WM, Burren O, Cooper NJ, Quinlan AR, et al.

Nature Genetics. doi:10.1038/ng.3245

Population-based structural variation discovery with Hydra-Multi.

Lindberg MR, Hall IM, Quinlan AR†, et al.

Bioinformatics. doi:10.1093/bioinformatics/btu771

Extending reference assembly models.

Church DM, Schneider VA, Steinberg KM, Schatz MC, Quinlan AR, Chin CS, Kitts PA, Aken B, Marth GT, Hoffman MM, Herrero J, Mendoza ML, Durbin R, Flicek P.

Genome Biology. doi:10.1186/s13059-015-0587-3.

Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction.

Do R, et al.

Nature. doi:10.1038/nature13917

2014

Genetics of Systemic Lupus Erythematosus: Immune Responses and End Organ Resistance to Damage.

Dai C, Deng Y, Quinlan AR, Gaskin F, Tsao B, Fu SM.

Current Opinion in Immunology. doi:10.1016/j.coi.2014.10.004

A reference bacterial genome dataset generated on the MinIONTM portable single-molecule nanopore sequencer.

Quick J, Quinlan AR, Loman N.

GigaScience. doi: 10.1186/2047-217X-3-22

PORETOOLS: a toolkit for working with nanopore sequencing data from Oxford Nanopore

Loman N, Quinlan AR†, Loman N.

Bioinformatics. doi:10.1093/bioinformatics/btu555

SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization.

Qiao Y, Quinlan AR, Jazaeri A, Verhaak R, Wheeler D, Marth G.

Genome Biology. doi:10.1186/s13059-014-0443-x

BEDTools: the Swiss-army tool for genome interval arithmetic.

Quinlan AR†

Current Protocols in Bioinformatics. doi: 10.1002/0471250953.bi1112s47

LUMPY: A probabilistic framework for sensitive detec- tion of chromosomal rearrangements.

Layer RM, Quinlan AR†, Hall IM.

Genome Biology. doi:10.1186/gb-2014-15-6-r84

Homozygous mutation of MTPAP causes cellular radiosensitivity and persistent DNA double strand breaks.

Martin N, Nakamura K, Paila U, Woo J, Brown C, Wright J, Teraoka S, Haghayegh S, Mc- Curdy D, Schneider M, Hu H, Quinlan AR, Gatti R, and Concannon P.

Cell Death Dis. doi: 10.1038/cddis.2014.99

A Novel IFITM5 Mutation in Severe Osteogenesis Imperfecta Decreases PEDF Secretion by Osteoblasts.

Farber CR, Reich A, Barnes AM, Becerra P, Rauch F, Cabral WA, Bae A, Quinlan AR, Glorieux FH, Clemens TL, and Marini JC.

J Bone Miner Res. doi: 10.1002/jbmr.2173

Pathogenic variants for Mendelian and complex traits in exomes of 6,517 European and African Ameri- cans: implications for the return of incidental results.

Tabor HK, Auer PL, Jamal SM, Chong JX, Yu JH, Gordon AS, Graubert TA, O’Donnell CJ, Rich SS, Nickerson DA; NHLBI Exome Sequencing Project, Bamshad MJ.

Am J Hum Genet. doi: 10.1016/j.ajhg.2014.07.006

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.

Lange LA, Hu Y, Zhang H, NHLBI Grand Opportunity Exome Sequencing Project, et al.

Am J Hum Genet. doi: 10.1016/j.ajhg.2014.01.010

Quantifying rare, deleterious variation in 12 human cytochrome P450 drug-metabolism genes in a large-scale exome dataset.

Gordon AS, Tabor HK, Johnson AD, Snively BM, NHLBI GO Exome Sequencing Project, et al.

Hum Mol Genet, doi: 10.1093/hmg/ddt588

2013

GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations.

Paila U, Chapman BA, Kirchner R, Quinlan AR†.

PLoS Comput Biol. doi:10.1371/journal.pcbi.1003153

Joint linkage and association analysis with exome sequence data implicates SLC25A40 in hypertriglyceridemia.

Rosenthal EA, Ranchalis J, Crosslin DR, Burt A, Brunzell JD, Motulsky AG, Nickerson DA; NHLBI GO Exome Sequencing Project, Wijsman EM, Jarvik GP.

Am J Hum Genet., doi: 10.1016/j.ajhg.2013.10.019

Recurrent gain-of-function mutation in PRKG1 causes thoracic aortic aneurysms and acute aortic dissections.

Guo DC, Regalado E, NHLBI Grand Opportunity Exome Sequencing Project, et al.

Am J Hum Genet., doi: 10.1016/j.ajhg.2013.06.019

Fine-scale patterns of population stratification confound rare variant association tests.

O’Connor TD, Kiezun A, Bamshad M, Rich SS, Smith JD, Turner E; NHLBIGO Exome Sequencing Project; ESP Population Genetics, Statistical Analysis Working Group, Leal SM, Akey JM.

PLoS One. doi:10.1371/journal.pone.0065834

Common and rare von Willebrand factor (VWF) coding variants, VWF levels, and factor VIII levels in African Americans: the NHLBI Exome Sequencing Project.

Johnsen JM, Auer PL, Morrison AC, Jiao S, Wei P, Haessler J, Fox K, McGee SR, Smith JD, Carlson CS, Smith N, Boerwinkle E, Kooperberg C, Nickerson DA, Rich SS, Green D, Peters U, Cushman M, Reiner AP; NHLBI Exome Sequencing Project.

Blood. doi:10.1182/blood-2013-02-485094

Exome sequencing and genome-wide linkage analysis in 17 families illustrate the complex contribution of TTN truncating variants to dilated cardiomy- opathy.

Norton N, Li D, Rampersaud E, Morales A, Martin ER, Zuchner S, Guo S, Gonzalez M, Hedges DJ, Robertson PD, Krumm N, Nickerson DA, Hershberger RE; National Heart, Lung, and Blood Institute GO Exome Sequencing Project and the Exome Sequencing Project Family Studies Project Team.

Circ Cardiovasc Genet. doi:10.1161/CIRCGENETICS.111.000062

Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms.

Malhotra A, Lindberg M, Leibowitz M, Clark R, Faust G, Layer R, Quinlan AR†, and Hall IM†.

Genome Research, doi:10.1101/gr.143677.112

Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants

Fu W, O’Connor TD, Jun G, Kang HM, Abecasis G, Leal SM, Gabriel S, Rieder MJ, Altshuler D, Shendure J, Nickerson DA, Bamshad MJ; NHLBI Exome Sequencing Project, Akey JM.

Nature. doi:10.1038/nature11690

Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections.

Layer R, Robins G, Skadron K, Quinlan AR†

Bioinformatics. doi: 10.1093/bioinformatics/bts652

TGFB2 mutations cause familial thoracic aortic aneurysms and dissections associated with mild systemic features of Marfan syndrome.

Boileau C, Guo DC, Hanna N, Regalado ES, D, NHLBI Go Exome Sequencing Project, et al.

Nature Genetics. doi:10.1038/ng.2348

Exome sequencing of extreme phenotypes identifies DCTN4 as a modifier of chronic Pseudomonas aeruginosa infection in cystic fibrosis.

Emond MJ, Louie T, Emerson J, Zhao W, NHLBI Exome Sequencing Project; Lung GO, Gibson RL, Bamshad MJ.

Nature Genetics. doi:10.1038/ng.2344

2007-2012

Copy number variation detection and genotyping from exome sequence data.

Krumm N, Sudmant PH, Ko A, O‘Roak BJ, NHLBI Exome Sequencing Project, Quinlan AR, Nickerson DA, Eichler EE.

Genome Research. doi: 10.1101/gr.138115.112

Characterizing complex structural variation in germline and somatic genomes.

Quinlan AR and Hall IM.

Trends in Genetics. doi: http://dx.doi.org/10.1016/j.tig.2011.10.002

Detection and interpretation of genomic structural variation in mammals.

Quinlan AR and Hall IM.

Methods in Molecular Biology

Genome sequencing of mouse induced pluripotent stem cells reveals retroelement stability and infrequent DNA rearrangement during reprogramming.

Quinlan AR, Boland MJ, Leibowitz ML, Shumilina S, Pehrson SM, Baldwin KK, Hall IM.

Cell Stem Cell. doi: 10.1016/j.stem.2011.07.018

Evidence for two independent associations with type 1 diabetes at the 12q13 locus.

Keene KL, Quinlan AR, Hou X, Hall IM, Mychaleckyj, Onengut-Gumuscu S, Concannon P.

Genes and Immunity. doi: 10.1038/gene.2011.56

Pybedtools: a flexible Python library for manipulating genomic datasets and annotations.

Dale R, Pedersen B, Quinlan AR†.

Bioinformatics. doi: 10.1093/bioinformatics/btr539

BamTools: a C++ API and toolkit for analyzing and managing BAM files.

Barnett D, Garrison E, Quinlan AR, Stromberg M, Marth G.

Bioinformatics. doi: 10.1093/bioinformatics/btr174

A map of human genome variation from population-scale sequencing.

1000 Genomes Project Consortium.

Nature. doi: 10.1038/nature09534

BEDTools: A flexible framework for comparing genomic features.

Quinlan AR and Hall IM.

Bioinformatics. doi: 10.1093/bioinformatics/btq033

Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome.

Quinlan AR, Clark RA, Sokolova, S, Leibowitx ML, Zhang Y, Hurles ME, Mell JC, Hall IM.

Genome Research. doi: 10.1101/gr.102970.109

Population Genomic Inferences from Sparse High-Throughput Sequencing of Two Populations of Drosophila melanogaster.

Sackton, TB, Kulathinal RJ, Bergman CM, Quinlan AR, Dopman E, Marth GT, Hartl DL, Clark AG.

Genome Biol Evol. doi: 10.1093/gbe/evp048

Rapid whole-genome mutational profiling using next-generation sequencing technologies.

Smith D, Quinlan AR, Peckham HR, et al.

Genome Research

Whole Genome Sequencing and SNP Discovery for C. elegans using massively parallel sequencing-by-synthesis.

Hillier LW, Marth GT, Quinlan AR, et al.

Nature Methods. doi: 10.1101/gr.077776.108

PyroBayes: Accurate quality scores for 454 Life Science pyrosequences.

Quinlan AR, Stewart D, Stromberg M, Marth GT

Nature Methods. doi:10.1038/nmeth.1172

Primer-site SNPs mask mutations.

Quinlan AR and Marth GT.

Nature Methods. doi:10.1038/nmeth0307-192

The Lab

We are a hard working group of geneticists and computational biologists at the University of Utah in the Departments of Human Genetics and Biomedical Informatics. We're committed to developing and applying cutting-edge methods to the understanding of genome biology and the genetic basis of disease. Working in our lab is a unique opportunity to apply and learn large-scale genomics methodologies and to make an impact on our understanding of human diseases. Please contact us if you are passionate about future work in this area. To learn more about Salt Lake City and the incredible research and quality of life in Utah, please visit the "Why Utah" resource.

Aaron Quinlan

Principal Investigator

Brent Pedersen

Senior Programmer

Amelia Wallace

Postdoctoral Research Associate

Meenal Gupta

Sr. Research Scientist

Tom Nicholas

Sr. Research Scientist

Peter McHale

Sr. Analyst/Programmer

Hao Hou

Programmer / Staff Scientist

Joe Brown

Programmer / Staff Scientist

Tom Sasani

Graduate Student

Stephanie Kravitz

Graduate Student

Jonathan Belyeu

Graduate Student

Michael Cormier

Graduate Student

Harriet Dashnow

Postdoctoral Scientist

Simone Longo

Graduate Student

Alumni

Brian Lohman

Postdoctoral Research Associate

Ryan Layer

Assistant Professor at CU-Boulder

Jim Havrilla

Graduate Student

Uma Paila

Postdoctoral Scientist

Neil Kindlon

Programmer

John Kubinski

Undergraduate Researcher

Phanwadee Sinthong

Undergraduate Researcher

Nathan Wilkinson

Undergraduate Student

Teaching

Salt Lake Learners of Biostats (SLLOBS)

SLLOBS - Lecture 03 - Data Frames and crude RNA-seq analysis

SLLOBS - Lecture 05 - Data Visualization with ggplot2

SLLOBS - Lecture 08 - Intro to Probability with Coin Tosses

SLLOBS - Lecture 11 - Bayes's Rule, variance, prob. distributions, expectation, covariance

SLLOBS - Lecture 12 - Poisson distributions in biology

SLLOBS - Lecture 13 - Gaussian Processes and QQ Plots

SLLOBS - Lecture 14 - t-statistics, t-distribution, t-tests, and p-values

SLLOBS - Lecture 16 - Intro to regression and model interpretation.

Applied Computational Genomics

Tutorials

Lectures

Lab.blog

Funding

We are very grateful to receive generous funding for our research from the National Human Genome Research Institute, the National Cancer Institute, USTAR, the Simons Foundation, and the Margolis Foundation.

Contact Us

Aaron Quinlan is an Associate Professor in the Department of Human Genetics and the Department of Biomedical Informatics at the University of Utah. Our lab is located on the 7th floor of The Eccles Institute for Human Genetics at The University of Utah. We are a part of the Utah Center for Genetic Discovery.

Eccles Institute for Human Genetics
15 North 2030 East, Room 7160B

aquinlan at genetics dot utah dot edu