Walter L. Ruzzo
Ph.D., University of California, Berkeley, 1978.
B.S., California Institute of Technology, Mathematics, 1968.
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 methods for finding noncoding RNA (ncRNA) genes. 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. Application to genome-scale analysis and discovery of other new ncRNA families are both underway. His group also has expertise in analysis of gene expression array data including classification and clustering, sequence analysis problems such as computational gene prediction, and analysis of chromatin immunoprecipitation and high throughput sequencing data. Students have been deeply involved in and critical to the success of all stages of all of these research projects.
Concepts and introduction to RNA bioinformatics.. Methods in molecular biology (Clifton, N.J.). 1097:1-31.. 2014.
De Novo Discovery of Structured ncRNA Motifs in Genomic Sequences.. Methods in molecular biology (Clifton, N.J.). 1097:303-18.. 2014.
Discriminative motif analysis of high-throughput dataset.. Bioinformatics (Oxford, England).. 2013.
Comparison of endogenous and overexpressed MyoD shows enhanced binding of physiologically bound sites.. Skeletal muscle. 3(1):8.. 2013.
A microbial profiling method for the human microbiota using high-throughput sequencing.. Metagenomics (Cairo, Egypt). 2:235646.. 2013.
Integration of 198 ChIP-seq Datasets Reveals Human cis-Regulatory Regions.. Journal of computational biology : a journal of computational molecular cell biology. 19(9):989-97.. 2012.
Transcripts with in silico predicted RNA structure are enriched everywhere in the mouse brain.. BMC genomics. 13(1):214.. 2012.
DUX4 Activates Germline Genes, Retroelements, and Immune Mediators: Implications for Facioscapulohumeral Dystrophy.. Developmental cell. 22(1):38-51.. 2012.
An integrative genomic approach identifies p73 and p63 as activators of miR-200 microRNA family transcription.. Nucleic acids research. 40(2):499-510.. 2012.
Compression of next-generation sequencing reads aided by highly efficient de novo assembly.. Nucleic acids research.. 2012.
Genetic and epigenetic determinants of neurogenesis and myogenesis.. Developmental cell. 22(4):721-35.. 2012.
A new approach to bias correction in RNA-Seq.. Bioinformatics (Oxford, England). 28(7):921-928.. 2012.
Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming.. Developmental cell. 18(4):662-74.. 2010.
MicroRNA discovery and profiling in human embryonic stem cells by deep sequencing of small RNA libraries.. Stem cells (Dayton, Ohio). 26(10):2496-505.. 2008.