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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.
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.
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William Stafford
Noble, Professor, Genome Sciences
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免费Ⅴpn安卓, Postdoctoral fellow, Genome Sciences
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Will Fondrie, Postdoctoral fellow, Genome Sciences
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Borislav (Bobby) Hristov, Postdoctoral fellow, Genome Sciences
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Dejun Lin, Postdoctoral fellow, Genome Sciences
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Yang Lu, Postdoctoral fellow, Genome Sciences
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Ran Zhang, Postdoctoral fellow, Genome Sciences
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Gurkan Yardimci, Postdoctoral fellow, Genome Sciences
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Gesine Cauer, Ph.D. student, Department of Genome Sciences
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vp下载苹果, Ph.D. student, Department of Genome Sciences
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Jacob
Schreiber,
Ph.D. student, Paul G. Allen School of Computer Science
and Engineering
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Robin Aguilar, Ph.D. student, Department of Genome Sciences
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Kianna Hales, Ph.D. student, Department of Genome Sciences
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Alan Min, Ph.D. student, Department of Statistics
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Mu Yang, Ph.D. student, Department of Biomedical Informatics
and Medical Education
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vp下载苹果, visiting student intern, Masters Program in
Machine Learning, Royal Institute of Technology, Sweden
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Charles Grant, Senior programmer, Department of Genome Sciences
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Rita Chupalov, Software Engineer, Department of Genome Sciences
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Brian Raiter, Software Engineer, Department of Genome Sciences
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Winston Chen, Undergraduate, Paul G. Allen School of Computer Science and Engineering
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vpn下, Undergraduate, Department of Computer Science,
Brown University
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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.
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The MEME Suite of motif-based
sequence analysis tools provides functionality for motif discovery,
scanning, and enrichment analysis.
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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.
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vp下载
predicts protein transmembrane topology and signal peptides.
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Crux analyzes
shotgun proteomics tandem mass spectra, associating peptides with
observed spectra.
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Genomedata provides efficient storage of
multiple tracks of numeric data anchored to a genome.
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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.
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Segtools provides exploratory data
analysis on genomic segmentations.
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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.
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Pastis infers
the three-dimensional structure of the genome on the basis of Hi-C
data.
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vp下载 uses
tensor decomposition to impute large-scale genomic data.
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DeepPINK is a
general method for doing feature selection with deep neural
networks.
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The vp下载苹果
is a tandem mass spectrometry search engine that uses a dynamic
Bayesian network model.
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ANN-SoLo is a
spectral library search tool for fast and accurate open modification
searching.
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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.
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Kiwano is a method
for prioritizing the order that epigenomic experiments should be performed
based on minimizing redundancy using submodular optimization.
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vp下载苹果 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.
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Ferhat
Ay, Institute Leadership Assistant Professor of Computational
Biology, La Jolla Institute for Allergy and Immunology
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vp下载,
Assistant Professor, Computer Enginering Department, Abdullah Gul
University, Kayseri, Turkey
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Asa Ben-Hur,
Professor, Department of Computer Science, Colorado State University,
Fort Collins
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Wout Bittremieux,
Postdoctoral fellow fellow with Prof. Pieter Dorrestein, Skaggs School
of Pharmacy and Pharmaceutical Sciences, UC San Diego
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Xiaoyu Chen, Illumina
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Timothy Durham, Bioinformatics Specialist I, Broad Institute
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Eleazar Eskin,
Professor, Department of Computer Science, Department of Human
Genetics, University of California, Los Angeles
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Michael Hoffman,
Scientist, Princess Margaret Cancer Centre, Toronto, Canada;
Assistant Professor, Department of Medical Biophysics, University of Toronto
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Victoria
Haghighi, Associate Professor, Department of Psychiatry,
Columbia University.
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Lukas Käll,
Professor, Applied Systems Biology, KTH - Royal Institute of
Technology, Sweden
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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.
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Aaron Klammer, Pacific Biosciences
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Jie
Liu, Assistant Professor, Department of Computational Medicine and
Bioinformatics, University of Michigan
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Li Liao,
Associate Professor, Department of Computer and Information Sciences,
University of Delaware
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Max
Libbrecht, Assistant Professor, Department of Computing
Science, Simon Fraser University
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Wenxiu Ma, Assistant
Professor, Department of Statistics, UC Riverside
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vp下载苹果,
Computational Immunologist, Adaptive Biotechnologies Corporation
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Tobias Mann, Director of Software Engineering, Bioinformatics,
Adaptive Biotechnologies Corporation
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Sean
McIlwain, Assistant Scientist, Department of Biostatistics &
Medical Informatics, University of Wisconsin
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Merja Oja, VTT Technical Research Centre of Finland
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Paul Pavlidis,
Professor of Psychiatry, University of British Columbia
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Lindsay Pino, Postdoctoral Fellow, Ben Garcia, U Pennsylvani.
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Sheila
Reynolds, Senior Research Scientist, Institute for Systems
Biology
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vp下载苹果, Assistant
Professor, Department of Computer Science, University of Montana
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Ritambhara Singh, Assistant
Professor, Department of Computer Science, Brown University
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Ilan Wapinski,
Systems Biology Fellow, Department of Systems Biology, Harvard
University
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Habil Zare, Assistant
Professor, Department of Cell Systems & Anatomy, University of Texas
Health Science Center at San Antonio
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