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.
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.
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.
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.
Search, and install genomic data packages. Build and check new ggd data packages.
ggd provides easy access to processed genomic data. It removes the difficulties and complexities with finding and processing the data sets and annotations germane to your experiments and/or analyses. You can quickly and easily search and install data package using ggd. ggd also offers tools to easily create and contribute data packages to ggd.
The D4 Quantatative Data Format. We sought to improve on existing formats such as BigWig and compressed BED files by creating the Dense Depth Data Dump (D4) format and tool suite. The D4 format is adaptive in that it profiles a random sample of aligned sequence depth from the input BAM or CRAM file to determine an optimal encoding that minimizes file size, while also enabling fast data access. We show that D4 uses less disk space for both RNA-Seq and whole-genome sequencing and offers 3 to 440 fold speed improvements over existing formats for random access, aggregation and summarization for scalable downstream analyses that would be otherwise intractable.
seqcover is a tool for viewing and evaluating depth-of-coverage with the following aims...
OncoGEMINI is an adaptation of GEMINI intended for the improved identification of biologically and clincally relevant tumor variants from multi-sample and longitudinal tumor sequencing data. Using a GEMINI-compatible database (generated from an annotated VCF file), OncoGEMINI is able to filter tumor variants based on included genomic annotations and various allele frequency signatures.
Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.
mosdepth can output...
jigv --open-browser --region chr1:34566-34999 *.bam /path/to/some.cram my.vcf.gz
Uphold your DUP and DEL calls. SV callers like lumpy look at split-reads and pair distances to find structural variants. This tool is a fast way to add depth information to those calls. This can be used as additional information for filtering variants; for example we will be skeptical of deletion calls that do not have lower than average coverage compared to regions with similar gc-content.
STRling (pronounced like "sterling") is a method to detect large STR expansions from short-read sequencing data. It is capable of detecting novel STR expansions, that is expansions where there is no STR in the reference genome at that position (or a different repeat unit from what is in the reference). It can also detect STR expansions that are annotated in the reference genome. STRling uses kmer counting to recover mis-mapped STR reads. It then uses soft-clipped reads to precisely discover the position of the STR expansion in the reference genome.
Crazy fast genome coverage estimates! The BAM and CRAM formats provide a supplementary linear index that facilitates rapid access to sequence alignments in arbitrary genomic regions. Comparing consecutive entries in a BAM or CRAM index allows one to infer the number of alignment records per genomic region for use as an effective proxy of sequence depth in each genomic region. Based on these properties, we have developed indexcov, an efficient estimator of whole-genome sequencing coverage to rapidly identify samples with aberrant coverage profiles, reveal large scale chromosomal anomalies, recognize potential batch effects, and infer the sex of a sample.
Collectively, the bedtools utilities are a swiss-army knife of tools for a wide-range of genomics analysis tasks. The most widely-used tools enable genome arithmetic. That is, set theory on the genome. For example, bedtools allows one to intersect, merge, count, complement, and shuffle genomic intervals from multiple files in widely-used genomic file formats such as BAM, BED, GFF, VCF. While each individual tool is designed to do a relatively simple task (e.g., intersect two interval files), quite sophisticated analyses can be conducted by combining multiple bedtools operations on the UNIX command line.
LUMPY is a novel and general probabilistic SV discovery framework that naturally integrates multiple SV detection signals, including those generated from read alignments or prior evidence, and that can readily adapt to any additional source of evidence that may become available with future technological advances.
GEMINI (GEnome MINIng) is a flexible framework for exploring genetic variation in the context of the wealth of genome annotations available for the human genome. By placing genetic variants, sample phenotypes and genotypes, as well as genome annotations into an integrated database framework, GEMINI provides a simple, flexible, and powerful system for exploring genetic variation for rare disease and population genetics.
Genotype Query Tools (GQT) is command line software and a C API for indexing and querying large-scale genotype data sets like those produced by 1000 Genomes, the UK100K, and forthcoming datasets involving millions of genomes. GQT represents genotypes as compressed bitmap indices, which reduce computational burden of variant queries based on sample genotypes, phenotypes, and relationships by orders of magnitude over standard "variant-centric" indexing strategies. This index can significantly expand the capabilities of population-scale analyses by providing interactive-speed queries to data sets with millions of individuals.
Poretools is a flexible toolkit for exploring datasets generated by nanopore sequencing devices from MinION for the purposes of quality control and downstream analysis. Poretools operates directly on the native FAST5 (an application of the HDF5 standard) file format produced by ONT and provides a wealth of format conversion utilities and data exploration and visualization tools.
SpeedSeq is an open-source genome analysis platform that accomplishes alignment, variant detection and functional annotation of a 50× human genome in 13 h on a low-cost server and alleviates a bioinformatics bottleneck that typically demands weeks of computation with extensive hands-on expert involvement. SpeedSeq offers performance competitive with or superior to current methods for detecting germline and somatic single-nucleotide variants, structural variants, insertions and deletions, and it includes novel functionality for streamlined interpretation.
Stephen A. Goldstein, Joe Brown, Brent S Pedersen, Aaron R. Quinlan, Nels C. Elde
Sarah M. Fixsen, Kelsey R. Cone, Stephen A. Goldstein, Thomas A. Sasani, Aaron R. Quinlan, Stefan Rothenburg, Nels C. Elde.
Meenal Gupta, Xiangfei Liu, Sharon N. Teraoka, Jocyndra A. Wright, Richard A. Gatti, Aaron R. Quinlan, Patrick Concannon.
Brent S. Pedersen, Joseph Brown, Harriet Dashnow, Amelia D. Wallace, Matt Velinder, Tatiana Tvrdik, Rong Mao, D. Hunter Best, Pinar Bayrak-Toydemir, Aaron R. Quinlan.
Michael J. Cormier, Jonathan R. Belyeu, Brent S. Pedersen, Joseph Brown, Johannes Koster, Aaron R. Quinlan.
Jonathan R. Belyeu, Murad Chowdhury, Joseph Brown, Brent S. Pedersen, Michael J. Cormier, Aaron R. Quinlan, Ryan M. Layer.
Jonathan R. Belyeu, Harrison Brand, Harold Wang, Xuefang Zhao, Brent S. Pedersen, Julie Feusier, Meenal Gupta, Thomas J. Nicholas, Lisa Baird, Bernie Devlin, Stephan J. Sanders, Lynn B. Jorde, Michael E. Talkowski, Aaron R. Quinlan.
Thomas J. Nicholas, Michael J. Cormier, Xiaomeng Huang, Yi Qiao, Gabor T. Marth, Aaron R. Quinlan.
Jonathan R Belyeu, Thomas A Sasani, Brent S Pedersen, Aaron R Quinlan
Hao Hou, Brent Pedersen, Aaron Quinlan.
Amelia Wallace, Thomas A. Sasani, Jordan Swanier, Brooke L. Gates, Jeff Greenland, Brent S. Pedersen, K-T Varley, Aaron R. Quinlan.
Brent S. Pedersen, Preetida J. Bhetariya, Joe Brown, Stephanie N. Kravitz, Gabor Marth, Randy L. Jensen, Mary P. Bronner, Hunter R. Underhill, Aaron R. Quinlan.
Genome Medicine, https://doi.org/10.1186/s13073-020-00761-2
Richard M. Cawthon, Huong D. Meeks, Thomas A. Sasani, Ken R. Smith, Richard A. Kerber, Elizabeth O’Brien, Lisa Baird, Melissa M. Dixon, Andreas P. Peiffer, Mark F. Leppert, Aaron R. Quinlan, Lynn B. Jorde.
Scientific Reports, https://doi.org/10.1038/s41598-020-66867-0
Jordan A. Berg, Jonathan R. Belyeu, Jeffrey T. Morgan, Yeyun Ouyang, Alex J. Bott, Aaron R. Quinlan, Jason Gertz, Jared Rutter.
PLoS computational biology, https://doi.org/10.1371/journal.pcbi.1007625
Thomas. A Sasani, Brent S. Pedersen, Ziyue Gao, Lisa Baird, Molly Przeworski, Lynn B. Jorde, Aaron R. Quinlan.
Brent S. Pedersen, Aaron R. Quinlan.
Ziyue Gao, Priya Moorjani, Thomas A. Sasani, Brent S. Pedersen, Aaron R. Quinlan, Lynn B. Jorde, Guy Amster, Molly Przeworski.
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.239442.118
James M. Havrilla, Brent S. Pedersen, Ryan M. Layer, Aaron R. Quinlan
Nature Genetics, https://doi.org/10.1038/s41588-018-0294-6
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
Sasani TA, Cone KR, Quinlan AR, Elde NC.
eLife, doi: 10.7554/eLife.35453
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
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
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
Pedersen BS, Quinlan AR
Bioinformatics, doi: 10.1093/bioinformatics/bty358
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
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, Matthew Loose
Nature Biotechnology, DOI: 10.1038/nbt.4060
Ryan M. Layer, Brent S. Pedersen, Tonya DiSera, Gabor T. Marth, Jason Gertz, Aaron R. Quinlan
Nature Methods, doi: 10.1038/nmeth.4556
Brent S. Pedersen and Aaron Quinlan
Brent S. Pedersen, Ryan L Collins, Michael E Talkowski, Aaron Quinlan
Karen Eilbeck*, Aaron Quinlan*, Mark Yandell
Nature Reviews Genetics doi:10.1038/nrg.2017.52
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
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
Pedersen BS, Quinlan AR†
AJHG doi: 10.1016/j.ajhg.2017.01.017
Pedersen BS, Quinlan AR†
Bioinformatics doi: 10.1093/bioinformatics/btx057
Pedersen BS, Layer RM, Quinlan AR†
Genome Biol. doi: 10.1186/s13059-016-0973-5
Ge Y, Onengut-Gumuscu S, Quinlan AR, Mackey AJ, Wright JA, Buckner JH, Habib T, Rich SS, Concannon P.
Diabetes. pii: db150322
Layer RM, Kindlon N, Karczewski K, Exome Aggregation Consortium, Quinlan AR†
Nature Methods. doi:10.1038/nmeth.3654
Layer RM, Quinlan AR†
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
Auer PL, et al.
JAMA Neurology. doi:10.1001/jamaneurol.2015.0582
Onengut-Gumuscu S, Chen WM, Burren O, Cooper NJ, Quinlan AR, et al.
Nature Genetics. doi:10.1038/ng.3245
Lindberg MR, Hall IM, Quinlan AR†, et al.
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.
Do R, et al.
Dai C, Deng Y, Quinlan AR, Gaskin F, Tsao B, Fu SM.
Current Opinion in Immunology. doi:10.1016/j.coi.2014.10.004
Quick J, Quinlan AR, Loman N.
GigaScience. doi: 10.1186/2047-217X-3-22
Loman N, Quinlan AR†, Loman N.
Qiao Y, Quinlan AR, Jazaeri A, Verhaak R, Wheeler D, Marth G.
Genome Biology. doi:10.1186/s13059-014-0443-x
Current Protocols in Bioinformatics. doi: 10.1002/0471250953.bi1112s47
Layer RM, Quinlan AR†, Hall IM.
Genome Biology. doi:10.1186/gb-2014-15-6-r84
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
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
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
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
Gordon AS, Tabor HK, Johnson AD, Snively BM, NHLBI GO Exome Sequencing Project, et al.
Hum Mol Genet, doi: 10.1093/hmg/ddt588
Paila U, Chapman BA, Kirchner R, Quinlan AR†.
PLoS Comput Biol. doi:10.1371/journal.pcbi.1003153
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
Guo DC, Regalado E, NHLBI Grand Opportunity Exome Sequencing Project, et al.
Am J Hum Genet., doi: 10.1016/j.ajhg.2013.06.019
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
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.
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
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
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.
Layer R, Robins G, Skadron K, Quinlan AR†
Bioinformatics. doi: 10.1093/bioinformatics/bts652
Boileau C, Guo DC, Hanna N, Regalado ES, D, NHLBI Go Exome Sequencing Project, et al.
Nature Genetics. doi:10.1038/ng.2348
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
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
Quinlan AR and Hall IM.
Trends in Genetics. doi: http://dx.doi.org/10.1016/j.tig.2011.10.002
Quinlan AR and Hall IM.
Methods in Molecular Biology
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
Keene KL, Quinlan AR, Hou X, Hall IM, Mychaleckyj, Onengut-Gumuscu S, Concannon P.
Genes and Immunity. doi: 10.1038/gene.2011.56
Dale R, Pedersen B, Quinlan AR†.
Bioinformatics. doi: 10.1093/bioinformatics/btr539
Barnett D, Garrison E, Quinlan AR, Stromberg M, Marth G.
Bioinformatics. doi: 10.1093/bioinformatics/btr174
1000 Genomes Project Consortium.
Nature. doi: 10.1038/nature09534
Quinlan AR and Hall IM.
Bioinformatics. doi: 10.1093/bioinformatics/btq033
Quinlan AR, Clark RA, Sokolova, S, Leibowitx ML, Zhang Y, Hurles ME, Mell JC, Hall IM.
Genome Research. doi: 10.1101/gr.102970.109
Sackton, TB, Kulathinal RJ, Bergman CM, Quinlan AR, Dopman E, Marth GT, Hartl DL, Clark AG.
Genome Biol Evol. doi: 10.1093/gbe/evp048
Smith D, Quinlan AR, Peckham HR, et al.
Hillier LW, Marth GT, Quinlan AR, et al.
Nature Methods. doi: 10.1101/gr.077776.108
Quinlan AR, Stewart D, Stromberg M, Marth GT
Nature Methods. doi:10.1038/nmeth.1172
Quinlan AR and Marth GT.
Nature Methods. doi:10.1038/nmeth0307-192
Sr. Research Scientist
Programmer / Staff Scientist
Data Engineer / Staff Scientist
Programmer / Analyst
University of Utrecht
University of Utah
Assistant Professor at CU-Boulder
Children's Hospital of Philadelphia