Aaron R. Quinlan
- PhD, Boston College
- Assistant Professor, Public Health Sciences
- Email: firstname.lastname@example.org
Bioinformatics and computational genomics; Variation in genome structure; Somatic genome evolution; Cancer genome biology
The research in my laboratory is largely focused on developing computational methods towards the 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 genome structure and the landscape of genetic variation. The throughput of modern genomic technologies (such as next-generation DNA sequencing) necessitates efficient approaches for manipulating and comparing large genomic datasets and for characterizing the genetic variation therein. Our laboratory develops 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. We are actively involved in several collaborations including: exome sequencing in multiple disease phenotypes, screens for rare mutations and copy-number variation in Type 1 diabetes, characterization of hundreds of cancer genomes, and the genomics of induced pluripotent stem (iPS) cells.
Genomics is now an intensely computational field of research. A pressing challenge is the fact that we can sequence entire genomes much more rapidly than we can interpret them. Consequently, many geneticists struggle to apply modern molecular techniques to their specific area of expertise. Our laboratory develops robust and intuitive computer software for manipulating and interpreting massive genomics datasets. We currently develop software in three main areas: (1) genome arithmetic; that is, comparing and contrasting large sets of genomic features, (2) discovery, annotation, and interpretation of genetic variation, and (3) rapid comparison of complete genome sequences.
Variation in genome structure
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 that vary greatly in size and phenotypic consequence. They are formed by diverse mechanisms including non-allelelic homologous recombination (NAHR), non-homologous end joining (NHEJ), retrotransposition, and other mechanisms mediated by DNA replication. Thanks to experimental advances and large-scale studies such as the 1000 Genomes Project, our understanding of the prevalence of SV and the contribution of each mechanism to SV in the germline has increased dramatically. However, we are just beginning to understand the contribution of SV to evolution, development, and complex disease. We also have a very limited understanding of why certain regions of our genome are prone to recurrent mutation. Our laboratory continues to develop new methods for detecting and understanding structural variation using modern DNA sequencing techniques.
Somatic genome evolution and cancer
Over the lifetime of an individual, the genomes of somatic cell lineages acquire many structural changes that rearrange their genetic material. Unlike point mutations such as single-nucleotide polymorphisms (SNP), structural lesions have the potential to amplify, remove, or reconstruct entire genes. The number and impact of these mutations depends on the age and genetic background of the individual, the cell type in question, and exposure to mutagens. Our laboratory is interested in several questions related to somatically-acquired mutation. First, we want to understand whether specific regions of our genome are “hotspots” for rearrangement; that is, are they more prone to breakage and rearrangement then others? Second, are structural rearrangements so-called “driver” mutations in cancer? Lastly, the structural integrity of founding somatic lineages is likely to have direct consequences on the development of stable, patient-specific, induced pluripotent stem (iPS) cell lineages for therapeutic applications.
- Hall I, Quinlan A. Detection and interpretation of genomic structural variation in mammals. Methods in molecular biology (Clifton, N.J.). 2012;838 225-48. PMID: 22228015
- Keene K, Quinlan A, Hou X, Hall I, Mychaleckyj J, Onengut-Gumuscu S, Concannon P. Evidence for two independent associations with type 1 diabetes at the 12q13 locus. Genes and immunity. 2011;13(1): 66-70. PMID: 21850031 | PMCID: PMC3285513
- Quinlan A, Boland M, Leibowitz M, Shumilina S, Pehrson S, Baldwin K, Hall I. Genome sequencing of mouse induced pluripotent stem cells reveals retroelement stability and infrequent DNA rearrangement during reprogramming. Cell stem cell. 2011;9(4): 366-73. PMID: 21982236 | PMCID: PMC3975295
- Quinlan A, Hall I. Characterizing complex structural variation in germline and somatic genomes. Trends in genetics : TIG. 2011;28(1): 43-53. PMID: 22094265 | PMCID: PMC3249479
- Quinlan A, Clark R, Sokolova S, Leibowitz M, Zhang Y, Hurles M, Mell J, Hall I. Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome. Genome research. 2010;20(5): 623-35. PMID: 20308636 | PMCID: PMC2860164
- Quinlan A, Hall I. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England). 2010;26(6): 841-2. PMID: 20110278 | PMCID: PMC2832824