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 protein and DNA sequences, data from high-throughput genomic assays such as ChIP-seq and Hi-C, and tandem mass spectrometry. 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.

Inclusion statement

    Back row: Bill Noble, Gurkan Yardimci, Tim Durham, Ritambhara Singh, Yang Lu, Lindsay Pino, Andy Lin. Middle row: Ella Fondrie, Nicole Fondrie, Will Fondrie, Giancarlo Bonora, Charles Grant. Front row: Enzo Bonora, Dejun Lin, Rita Chupalov.

    Click here for older pictures.

    Lab members

    Brief bios of current and former lab members

    • William Stafford Noble, Professor, Genome Sciences

    • Kris Alavattam, Postdoctoral fellow, Genome Sciences / Institute for Stem Cell and Regenerative Medicine

    • Giancarlo Bonora, Postdoctoral fellow, Genome Sciences

    • Will Fondrie, Postdoctoral fellow, Genome Sciences

    • Borislav (Bobby) Hristov, Postdoctoral fellow, Genome Sciences

    • Anupama Jha, Postdoctoral fellow, Genome Sciences

    • Dejun Lin, Postdoctoral fellow, Genome Sciences

    • Yang Lu, Postdoctoral fellow, Genome Sciences

    • Ran Zhang, Postdoctoral fellow, Genome Sciences

    • Ayse Dincer, Ph.D. student, Paul G. Allen School of Computer Science and Engineering

    • Gesine Cauer, Ph.D. student, Department of Genome Sciences

    • Robin Aguilar, Ph.D. student, Department of Genome Sciences

    • Kianna Hales, Ph.D. student, Department of Genome Sciences

    • Alan Min, Ph.D. student, Department of Statistics

    • Mu Yang, Ph.D. student, Department of Biomedical Informatics and Medical Education

    • Melih Yilmaz, Ph.D. student, Paul G. Allen School of Computer Science and Engineering

    • Charles Grant, Senior programmer, Department of Genome Sciences

    • Rita Chupalov, Software Engineer, Department of Genome Sciences

    • Brian Raiter, Software Engineer, Department of Genome Sciences

    • Winston Chen, Undergraduate, Paul G. Allen School of Computer Science and Engineering

    • Mozes Jacobs, Undergraduate, Paul G. Allen School of Computer Science and Engineering

    • Justin Sanders, Undergraduate, Department of Computer Science, Brown University

    • Donavan See, Undergraduate, Paul G. Allen School of Computer Science and Engineering



    The following list includes actively maintained software packages. Many additional released packages are linked from our publications page. All of the software listed below is available with source code at the URLs specified. When indicated, some of the software is augmented with a free web server.

    1. The MEME Suite of motif-based sequence analysis tools provides functionality for motif discovery, scanning, and enrichment analysis.
    2. Percolator post-processes the results of a shotgun proteomics database search program, re-ranking peptide-spectrum matches so that the top of the list is enriched for correct matches.
    3. Philius predicts protein transmembrane topology and signal peptides.
    4. Crux analyzes shotgun proteomics tandem mass spectra, associating peptides with observed spectra.
    5. Genomedata provides efficient storage of multiple tracks of numeric data anchored to a genome.
    6. Segway performs simultaneous segmentation and clustering of genomic signal data such as those from ChIP-seq and DNase-seq, finding recurring patterns in these data.
    7. Segtools provides exploratory data analysis on genomic segmentations.
    8. Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C.
    9. PASTIS infers the three-dimensional structure of the genome on the basis of Hi-C data.
    10. PREDICTD uses tensor decomposition to impute large-scale genomic data.
    11. DeepPINK is a general method for doing feature selection with deep neural networks.
    12. The DRIP Toolkit is a tandem mass spectrometry search engine that uses a dynamic Bayesian network model.
    13. ANN-SoLo is a spectral library search tool for fast and accurate open modification searching.
    14. Avocado compresses large compendia of genome-wide epigenomic measurements into low dimensional latent representations that can be used to impute experiments that have not yet been performed.
    15. Kiwano is a method for prioritizing the order that epigenomic experiments should be performed based on minimizing redundancy using submodular optimization.
    16. Apricot is a Python toolkit that implements submodular optimization. While the methods are general to many applications, the package focuses on the application of selecting a diverse set of training set examples for machine learning methods.

    Former lab members

    • Ferhat Ay, Institute Leadership Assistant Professor of Computational Biology, La Jolla Institute for Allergy and Immunology
    • Zafer Aydin, Assistant Professor, Computer Enginering Department, Abdullah Gul University, Kayseri, Turkey
    • Asa Ben-Hur, Professor, Department of Computer Science, Colorado State University, Fort Collins
    • Wout Bittremieux, Postdoctoral fellow fellow with Prof. Pieter Dorrestein, Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego
    • Xiaoyu Chen, Principal Computational Biologist, Adaptive Biotechnology
    • Timothy Durham, Bioinformatics Specialist I, Broad Institute
    • Eleazar Eskin, Professor, Department of Computer Science, Department of Human Genetics, University of California, Los Angeles
    • Michael Hoffman, Scientist, Princess Margaret Cancer Centre, Toronto, Canada; Assistant Professor, Department of Medical Biophysics, University of Toronto
    • Victoria Haghighi, Professor, Department of Neuroscience, Icahn School of Medicine at Mount Sinai.
    • Lukas Käll, Professor, Applied Systems Biology, KTH - Royal Institute of Technology, Sweden
    • Attila Kertesz-Farkas, Assistant Professor, School of Data Analysis and Artificial Intelligence, the Faculty of Informatics, National Research University Higher School of Economics in Moscow, Russian Federation.
    • Aaron Klammer, Pacific Biosciences
    • Andy Lin, Linus Pauling Distinguished Postdoctoral Fellow, Pacific Northwest National Labs
    • Jie Liu, Assistant Professor, Department of Computational Medicine and Bioinformatics, University of Michigan
    • Li Liao, Associate Professor, Department of Computer and Information Sciences, University of Delaware
    • Max Libbrecht, Assistant Professor, Department of Computing Science, Simon Fraser University
    • Wenxiu Ma, Assistant Professor, Department of Statistics, UC Riverside
    • Damon May, Computational Immunologist, Adaptive Biotechnologies Corporation
    • Tobias Mann, Director of Software Engineering, Bioinformatics, Adaptive Biotechnologies Corporation
    • Sean McIlwain, Assistant Scientist, Department of Biostatistics & Medical Informatics, University of Wisconsin
    • Merja Oja, VTT Technical Research Centre of Finland
    • Paul Pavlidis, Professor of Psychiatry, University of British Columbia
    • Lindsay Pino, Postdoctoral Fellow, Ben Garcia, U Pennsylvania.
    • Sheila Reynolds, Senior Research Scientist, Institute for Systems Biology
    • Jacob Schreiber, Postdoctoral Fellow with Prof. Anshul Kundaje, Department of Genetics, Stanford University
    • Oliver Serang, Assistant Professor, Department of Computer Science, University of Montana
    • Ritambhara Singh, Assistant Professor, Department of Computer Science, Brown University
    • Ilan Wapinski, Systems Biology Fellow, Department of Systems Biology, Harvard University
    • Gurkan Yardimci, Assistant Professor, Oncological Sciences Division, Oregon Health Sciences University
    • Habil Zare, Assistant Professor, Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio


Hike to Heather Lake, May 2006. A party, July 2006. Annual picnic, August 2006. Hike to Lake 22, June 2007. Annual picnic, August 2007. Hike to Wallace Falls, May 2008. Annual picnic, October 2008. Hike to Heather Lake, June 2009. Hike to Gold Creek, June 2010. Annual picnic, August 2010. Goodbye party for Michael Mathews, December 2010. Hike to Boulder River, May 2011. Hike to Talapus Lake, June 2012. Hike to Bridal Veil Falls, July 2013. Goodbye party for Habil Zare, June 2014. Hike to Annette Lake, June 2014. Hike to Snow Lake, July 2015. Hike to Denny Creek, August 2016. Hike to Rattlesnake Ledge, July 2017. Hike to Heather Lake, July 2018. Hike to Dirty Harry's Balcony, July 2019.

The lab is located in Foege, room S340.

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