Welcome to the Yandell Lab located in the Eccles Institute of Human Genetics on the Campus of the University of Utah's Health Sciences Center.

Current Research in the Yandell Lab:

Sequenced genomes contain a treasure trove of information about how genes function and evolve. Getting at this information, however, is challenging and requires novel approaches that combine computer science and experimental molecular biology. My lab works at the intersection of both domains, and research in our group can be summarized as follows: generate hypotheses concerning gene function and evolution by computational means, and then test these hypotheses at the bench. This is easier said than done, as serious barriers still exist to using sequenced genomes and their annotations as starting points for experimental work. Some of these barriers lie in the computational domain, others in the experimental. Though challenging, overcoming these barriers offers exciting training opportunities in both computer science and molecular genetics, especially for those seeking a future at the intersection of both fields. Ongoing projects in the lab are centered on genome annotation and comparative genomics; exploring the relationships between sequence variation and human disease; and high-throughput biological image analysis.

More About Research Interests...

Recent Publications:

Kapusta A, Kronenberg Z, Lynch VJ, Zhuo X, Ramsay L, Bourque G, Yandell M, Feschotte C.
PLoS Genet. 2013 Apr;9(4) Epub
Shapiro MD, Kronenberg Z, Li C, Domyan ET, Pan H, Campbell M, Tan H, Huff CD, Hu H, Vickrey AI, Nielsen SCA, Stringham SA, Hu H, Willerslev E, Gilbert MTP, Yandell M, Zhang G, Wang J.
Science. 2013 Mar 1;339(6123):1063-7
Vieler A, Wu G, Tsai CH, Bullard B, Cornish AJ, Harvey C, Reca IB, Thornburg C, Achawanantakun R, Buehl CJ, Campbell MS, Cavalier D, Childs KL, Clark TJ, Deshpande R, Erickson E, Armenia Ferguson A, Handee W, Kong Q, Li X, Liu B, Lundback S, Peng C, Roston RL, Sanjaya, Simpson JP, Terbush A, Warakanont J, Zauner S, Farre EM, Hegg EL, Jiang N, Kuo MH, Lu Y, Niyogi KK, Ohlrogge J, Osteryoung KW, Shachar-Hill Y, Sears BB, Sun Y, Takahashi H, Yandell M, Shiu SH, Benning C.
PLoS Genet. 2012;8(11)
Smith JJ, Kuraku S, Holt C, Sauka-Spengler T, Jiang N, Campbell MS, Yandell M, Manousaki T, Meyer A, Bloom OE, Morgan JR, Buxbaum JD, Sachidanandam R, Sims C, Garruss AS, Cook M, Krumlauf R, Wiedemann LM, Sower SA, Decatur WA, Hall JA, Amemiya CT, Saha NR, Buckley KM, Rast JP, Das S, Hirano M, McCurley N, Guo P, Rohner N, Tabin CJ, Piccinelli P, Elgar G, Ruffier M, Aken BL, Searle SM, Muffato M, Pignatelli M, Herrero J, Jones M, Brown CT, Chung-Davidson YW, Nanlohy KG, Libants SV, Yeh CY, McCauley DW, Langeland JA, Pancer Z, Fritzsch B, de Jong PJ, Zhu B, Fulton LL, Theising B, Flicek P, Bronner ME, Warren WC, Clifton SW, Wilson RK, Li W.
Nat Genet. 2013 Feb 24.
Yandell M, Ence D.
Nat Rev Gen. 2012 May
Yandell M, Huff CD, Hu H, Singleton M, Moore B, Xing J, Jorde L, Reese MG.
Genome Res. 2011 Sep;21(9):1529-42
Rope AF, Wang K, Evjenth R, Xing J, Johnston JJ, Swensen JJ, Johnson WJ, Moore B, Huff CD, Bird LM, Carey JC, Opitz JM, Stevens CA, Jiang T, Schank C, Fain HD, Robison R, Dalley B, Chin S, South ST, Pysher TJ, Jorde LB, Hakonarson H, Lillehaug JR, Biesecker LG, Yandell M, Arnesen T, Lyon GJ.
Am J Hum Genet. 2011 Jul 15;89(1):28-43
Moore B, Hu H, Singleton M, De La Vega FM, Reese MG, Yandell M.
Genet Med. 2011 Mar;13(3):210-7
Suen G, Teiling C, Li L, Holt C, Abouheif E, Bornberg-Bauer E, Bouffard P, Caldera EJ, Cash E, Cavanaugh A, Denas O, Elhaik E, Fave M-J, Gadau J, Gibson JD, Graur D, Grubbs KJ, Hagen DE, Harkins TT, Helmkampf M, Hu H, Johnson BR, Kim J, Marsh SE, Moeller JA, Munoz-Torres MC, Murphy MC, Naughton MC, Nigam S, Overson R, Rajakumar R, Reese JT, Scott JJ, Smith CR, Tao S, Tsutsui ND, Viljakainen L, Wissler L, Yandell M, Zimmer F, Taylor J, Slater SC, Clifton SW, Warren WC, Elsik CG, Smith CD, Weinstock GM, Gerardo NM, Currie CR.
PLoS Genet. 2011 Feb 10;7(2)
Smith C R, Smith C D, Robertson H M, Helmkampf M, Zimin A, Yandell M, Holt C, Hu H, Abouheif E, Benton R, Cash E, Croset V, Currie C R, Elhaik E, Elsik C G, Fave M-J, Fernandes V, Gibson J D, Graur D, Gronenberg W, Grubbs K J, Hagen D E, Viniegra A S I, Johnson B R, Johnson R M, Khila A, Kim J W, Mathis K A, Munoz-Torres M C, Murphy M C, Mustard J A, Nakamura R, Niehuis O, Nigam S, Overson R P, Placek J E, Rajakumar R, Reese J T, Suen G, Tao S, Torres C W, Tsutsui N D, Viljakainen L, Wolschin F, Gadau J.
Proc Natl Acad Sci U S A. 2011 Jan 31
Smith C D, Zimin A, Holt C, Abouheif E, Benton R, Cash E, Croset, V, Currie C R, Elhaik E, Elsik C G, Fave M-J, Fernandes V, Gadau J, Gibson J D, Graur D, Grubbs K J, Hagen D E, Helmkampf M, Holley J-A, Hu H, Viniegra A S I, Johnson B R, Johnson R M, Khila A, Kim J W, Laird J, Mathis K A, Moeller J A, Munoz-Torres M C, Murphy M C, Nakamura R, Nigam S, Overson R P, Placek J E, Rajakumar R, Reese J T, Robertsonl H M, Smith C R, Suarez A V, Suen G, Suhr E L, Tao S, Torres C W, Wilgenburg E van, Viljakainen L, Walden K K O, Wild A L, Yandell M, Yorke J A, Tsutsui N D.
Proc Natl Acad Sci U S A. 2011 Jan 31
Hu H, Bandyopadhyay P, Olivera B, Yandell M.
BMC Genomics. 2011; 12: 60

More Publications...



VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds upon existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood-framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and non-coding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology.


MAKER is a portable and easily configurable genome annotation pipeline. It's purpose is to allow smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER is also easily trainable: outputs of preliminary runs can be used to automatically retrain its gene prediction algorithm, producing higher quality gene-models on seusequent runs. MAKER's inputs are minimal and its ouputs can be directly loaded into a GMOD database. They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER should prove especially useful for emerging model organism projects with minimal bioinformatics expertise and computer resources.


More Software...