Noble Research Lab Department of Genome Sciences University of Washington Our research group develops and applies computational techniques for modeling and understanding biological processes at the molecular level. Our research emphasizes the application of statistical and machine learning techniques, such as hidden Markov models and support vector machines. We apply these techniques to various types of biological data, including DNA and protein sequence data, as well as gene expression data from microarray experiments. We are currently developing methods for analyzing shotgun proteomics data, for characterizing protein function, structure and interactions, and for understanding the structure and regulatory influence of chromatin. Sheila Reynolds, Aaron Klammer, Charles Grant, Jian Qiu, Bill Noble, Xiaoyu Chen, Oliver Serang, Lukas Kall, Bob Thurman. Click here for older pictures. Lab members William Stafford Noble, Associate Professor, Genome Sciences Bob Thurman, Associate Research Scientist, Division of Medical Genetics Much of my work has been part of the ENCODE project, which is a large-scale international program to catalog all of the functional elements of the human genome. My work has focused on computational approaches to understanding two things. First, chromatin structure. This is the structure in which DNA is packaged in order to fit in the cell. I work closely with the Stam lab in the analysis of their DNaseI data, which measures the "openness" or accessiblity of chromatin at each point in the genome. Another, related field of interest is epigenetics, or the modifications to DNA that do not involve changes to the underlying DNA sequence. Some epigenetic phenomena, such as histone modifications, have been shown to be related to chromatin structure. Much of my work in both of these areas involves mathematical and signal processing techniques such as wavelets and Fourier analysis. Lukas Kall, Postdoctoral fellow, Genome Sciences I examine the behavior of different machine learning techniques on mass spectrometry data. In particular, I investigate the possibility of using MS/MS data as an aid in transmembrane topology prediction. Merja Oja, Postdoctoral fellow, Genome Sciences   Oliver Serang, Ph.D. student, Genome Sciences Imagine you built some cool things with Legos and then a friend took them apart, leaving small chunks together. Then later you're trying to remember what you built. You browse through the Lego manual and find several pieces in chunks together that should only be there if you built the X-Wing fighter and a chunk that could have come from Robinhood's castle or from the X-Wing, but you don't find any other pieces that could have come from Robinhood's castle. And so you guess that you built the X-wing. This is essentially how mass spectrometry-based proteomics works. I'm making algorithms that intelligently decide what proteins are in a sample by looking at pieces of these proteins and putting them back together. Charles Grant, senior programmer, Genome Sciences I work on MEME and Meta-MEME, software packages for discovering and searching for motifs. Motifs are short, distinctive sequences of protein or DNA which play critical roles in protein structure and the regulation of DNA transcription. Sheila Reynolds, graduate student, Department of Electrial Engineering Xiaoyu Chen, graduate student, Department of Computer Science and Engineering Publications Software ChargeCzar: classification of tandem mass spectra by charge state Crux: tandem mass spectrometry analysis Gist: support vector machine classification and for kernel principal components analysis. HyFi: identification of thermodynamically stable binding sites for a short DNA sequence matrix2png: matrix visualization software and web server. The MEME Suite: motif-based sequence analysis tools. Percolator: semi-supervised analysis of shotgun proteomics database search results. qvality: nonparametric estimation of posterior error probabilities Prism: microarray visualization server Rankprop: protein ranking by network diffusion SVM-fold: SCOP fold and superfamily prediction from a given protein sequence svmvia: support vector machine optimization using the full regularization path algorithm Former lab members Asa Ben-Hur, Assistant Professor, Department of Computer Science, Colorado State University, Fort Collins Paul Pavlidis, Assistant Professor of Psychiatry, University of British Columbia Eleazar Eskin, Assistant Professor, Department of Computer Science, Department of Human Genetics, University of California, Los Angeles Li Liao, Assistant Professor, Department of Computer and Information Sciences, University of Delaware Hike to Heather Lake, May 2006 A party, July 2006 Annual picnic, August 2006 Hike to Lake 22, June 2007 Annual picnic, August 2007 The lab is located in Foege, room S220.