Larry Ruzzo, Professor, received a B.S. (in Mathematics) from the California Institute of Technology in 1968, his Ph.D. (Computer Science) from the University of California at Berkeley in 1978, and has been with the University of Washington since 1977.
His research is focused on development of computational methods and tools applicable to practical problems in molecular biology, an increasingly data-rich discipline. Recent work has focused on analysis of high throughput sequencing data, such as chromatin immunoprecipitation (ChIPseq) and transcriptomic (RNAseq) data, including development of new methods for mapping, assembly, bias correction, isoform quantitation, and motif discovery. New methods for finding noncoding RNA (ncRNA) genes are also being actively developed. A rush of discoveries in the last few years has greatly broadened appreciation of the biological diversity and importance of these genes, but analysis has been hampered by a lack of sensitive, specific and/or fast computational tools. His group has developed new techniques for inference of and sequence searching with covariance models, a leading approach for modeling ncRNA gene families. The inferred models show high sensitivity and specificity and the search tools typically accelerate searches by 100 fold or more with (provably) no loss in accuracy. These tools have been instrumental in the discovery of many new families of riboswitches. Students have been deeply involved in and critical to the success of all stages of all of these research projects.