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:

Hu H Roach JC Coon H Guthery SL Voelkerding KV Margraf RL Durtschi JD Tavtigian SV Shankaracharya Wu W Scheet P Wang S Xing J Glusman G Hubley R Li H Garg V Moore B Hood L Galas DJ Srivastava D Reese MG Jorde LB Yandell M Huff CD
Nature Biotech - In Press
Kennedy B Kronenberg Z Hu H Moore B Flygare S Reese MG Jorde LB Yandell M Huff C
Current Protocols in Human Genetics. 2014 Apr 24;81:6.14.1-6.14.25
Manuck TA Watkins WS Moore B Esplin MS Varner MW Jackson GM Yandell M Jorde L
Am J Obstet Gynecol. 2014 Apr;210(4)
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
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.

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Software:

GPAT

The application of population genomics to non-model organisms is greatly facilitated by the low cost of next generation sequencing (NGS). Barriers, however, exist for using NGS data for population level analyses. Traditional population genetic metrics, such as Fst, are not robust to the genotyping errors inherent in noisy NGS data. Additionally, many older software tools were never designed to handle the volume of data produced by NGS pipelines. To overcome these limitations we have developed a flexible software library designed specifically for large and noisy NGS datasets. The Genotype Phenotype Association Toolkit (GPAT) implements both traditional and novel population genetic methods in a single user-friendly framework. GPAT consists of a suite of compiled tools and a Perl API that programmers can use to develop new applications. To date GPAT has been used successfully to identity genotype-phenotype associations in several real-world datasets including: domestic pigeons, Pox virus and pine rust fungus. GPAT is open source and freely available for academic use.

GPA++ is a C++ extension of The Genotype Phenotype Association Toolkit. The perl implementation of GPA has more bells and whistles than GPA++, but lacks speed.

VAAST

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

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.

Features

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