Department of Integrative Structural and Computational Biology
Faculty, Graduate Program
Co-Executive Editor, GENE
Scientific Advisory Board, The Gene Ontology Consortium
Our lab is interested biomedical discovery using quantitative methods. We apply the tools of computer science and statistics to a variety of projects spanning mouse genetics, transcriptional regulation, cancer biology, and immunology.
Our current research activities generally fall into three groups.
First, we build computational tools to enable and accelerate biomedical discovery. These online tools are typically targeted toward genetics and genomics researchers. Currently, we develop and maintain BioGPS and the Gene Wiki, both of which embrace the principle of “community intelligence”. These tools harness the collective intelligence of the biological community to systematically improve the organization and access to gene annotation information.
Second, we directly engage in biomedical discovery through large-scale data mining. Most recently, we developed a method to utilize dense genotype maps in a diverse panel of inbred mice to do genome-wide association scans. We also extended this method to examine the genetic basis of gene expression variation, called expression QTL (or eQTL) analysis. We have used these approaches to identify, and in some cases validate, novel regulators of biological pathways and processes.
Third, we actively seek out new collaborations with experimental scientists to explore novel data sets and technology platforms. The rapid rate of biological data generation has only accelerated in recent years, and bioinformatics plays a critical role in the warehousing, analysis, and visualization of these data. We collaborate with many research groups both locally and globally, and focus on efficiently translating large data sets into testable hypotheses.
Please see our lab website or full publication list for more details on all these research activities in our group.
Ph.D., Chemistry, The Scripps Research Institute, 2002
B.A., Chemistry, Computing and Information Systems, Integrated Science, Northwestern University, 1998
2011-2017 Associate Professor, Molecular and Experimental Medicine (MEM), The Scripps Research Institute
2010-2011 Associate Director, Bioinformatics, Genomics Institute of the Novartis Research Foundation
2002-2010 Group Leader, Senior Research Investigator, Bioinformatics, Genomics Institute of the Novartis Research Foundation
Good BM, Su AI. (2013) Crowdsourcing for Bioinformatics. Bioinformatics. 15;29(16):1925-33.
Loguercio S, Good BM, Su AI. (2013) Dizeez: an online game for human gene-disease annotation. PLoS One. 8(8):e71171.
Clarke EL, Loguercio S, Good BM, Su AI. (2013) A task-based approach for Gene Ontology evaluation. J Biomed Semantics. 4 Suppl 1:S4.
Choi NM, Loguercio S, Verma-Gaur J, Degner SC, Torkamani A, Su AI, Oltz EM, Artyomov M, Feeney AJ. (2013) Deep sequencing of the murine igh repertoire reveals complex regulation of nonrandom v gene rearrangement frequencies. J Immunol. 191(5):2393-402.
Good BM, Howe DG, Lin SM, Kibbe WA, Su AI. (2011) Mining the Gene Wiki for functional genomic knowledge. BMC Genomics. 12:603.
Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J, Huss JW, 3rd and Su AI (2009) BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol, 10:R130.
Huss JW, 3rd, Orozco C, Goodale J, Wu C, Batalov S, Vickers TJ, Valafar F and Su AI (2008) A gene wiki for community annotation of gene function. PLoS Biol, 6:e175.
Wu C, Delano DL, Mitro N, Su SV, Janes J, McClurg P, Batalov S, Welch GL, Zhang J, Orth AP, Walker JR, Glynne RJ, Cooke MP, Takahashi JS, Shimomura K, Kohsaka A, Bass J, Saez E, Wiltshire T and Su AI (2008) Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. PLoS Genet, 4:e1000070.
McClurg P, Janes J, Wu C, Delano DL, Walker JR, Batalov S, Takahashi JS, Shimomura K, Kohsaka A, Bass J, Wiltshire T and Su AI (2007) Genomewide association analysis in diverse inbred mice: power and population structure. Genetics, 176:675-83.
Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, Cooke MP, Walker JR and Hogenesch JB (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A, 101:6062-7.