Joel S Bader

Summary

Affiliation: Johns Hopkins University
Country: USA

Publications

  1. ncbi request reprint Global synthetic-lethality analysis and yeast functional profiling
    Siew Loon Ooi
    High Throughput Biology Center, Institute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University School of Medicine, 339 Broadway Research Building, 733 North Broadway, Baltimore, MD 21205, USA
    Trends Genet 22:56-63. 2006
  2. pmc Molecular immunologic correlates of spontaneous latency in a rabbit model of pulmonary tuberculosis
    Selvakumar Subbian
    Laboratory of Mycobacterial Immunity and Pathogenesis, The Public Health Research Institute PHRI Center at the University of Medicine and Dentistry of New Jersey UMDNJ, 225 Warren Street, 07103, Newark, NJ, USA
    Cell Commun Signal 11:16. 2013
  3. pmc Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast
    Ping Ye
    The Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
    BMC Bioinformatics 6:270. 2005
  4. pmc New connections, new components, real dynamics
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA
    Sci Signal 2:pe48. 2009
  5. ncbi request reprint Systems biology. When proteomes collide
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21215, USA
    Science 311:187-8. 2006
  6. pmc Grand network convergence
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA
    Genome Biol 12:306. 2011
  7. ncbi request reprint Gaining confidence in high-throughput protein interaction networks
    Joel S Bader
    Department of Biomedical Engineering, 201C Clark Hall, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, USA
    Nat Biotechnol 22:78-85. 2004
  8. pmc Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
    Yan Qi
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
    Genome Res 18:1991-2004. 2008
  9. pmc A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation
    Yu yi Lin
    High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
    Genes Dev 22:2062-74. 2008
  10. pmc How networks change with time
    Yongjin Park
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    Bioinformatics 28:i40-8. 2012

Detail Information

Publications52

  1. ncbi request reprint Global synthetic-lethality analysis and yeast functional profiling
    Siew Loon Ooi
    High Throughput Biology Center, Institute of Genetic Medicine, Department of Biostatistics, Johns Hopkins University School of Medicine, 339 Broadway Research Building, 733 North Broadway, Baltimore, MD 21205, USA
    Trends Genet 22:56-63. 2006
    ....
  2. pmc Molecular immunologic correlates of spontaneous latency in a rabbit model of pulmonary tuberculosis
    Selvakumar Subbian
    Laboratory of Mycobacterial Immunity and Pathogenesis, The Public Health Research Institute PHRI Center at the University of Medicine and Dentistry of New Jersey UMDNJ, 225 Warren Street, 07103, Newark, NJ, USA
    Cell Commun Signal 11:16. 2013
    ..However, besides nonhuman primates, rabbits are the only animal model that fully recapitulates the pathological features of human TB, including progressive disease with necrosis and cavitation or establishment of spontaneous latency...
  3. pmc Commensurate distances and similar motifs in genetic congruence and protein interaction networks in yeast
    Ping Ye
    The Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
    BMC Bioinformatics 6:270. 2005
    ....
  4. pmc New connections, new components, real dynamics
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA
    Sci Signal 2:pe48. 2009
    ..Together, these technologies are revealing the design choices made by evolution, and they provide a framework for building new biological circuits to order...
  5. ncbi request reprint Systems biology. When proteomes collide
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21215, USA
    Science 311:187-8. 2006
  6. pmc Grand network convergence
    Joel S Bader
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, MD 21218, USA
    Genome Biol 12:306. 2011
    ..Three major themes arose during the 2011 meeting: technological drivers and data generation, algorithmic advances, and convergence on biological applications with context-sensitive networks...
  7. ncbi request reprint Gaining confidence in high-throughput protein interaction networks
    Joel S Bader
    Department of Biomedical Engineering, 201C Clark Hall, Johns Hopkins University, 3400 N Charles St, Baltimore, Maryland 21218, USA
    Nat Biotechnol 22:78-85. 2004
    ..The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans...
  8. pmc Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions
    Yan Qi
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
    Genome Res 18:1991-2004. 2008
    ..The kernels show significant improvement over previous best methods for predicting genetic interactions and protein co-complex membership from genetic interaction data...
  9. pmc A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation
    Yu yi Lin
    High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
    Genes Dev 22:2062-74. 2008
    ..These new characterizations of the HDA and NuA4 complexes demonstrate how systematic analyses of genetic interactions may help illuminate the mechanisms of intricate cellular processes...
  10. pmc How networks change with time
    Yongjin Park
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    Bioinformatics 28:i40-8. 2012
    ..Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals...
  11. doi request reprint Assembling large DNA segments in yeast
    Héloïse Muller
    Department of Environmental Health Sciences, Johns Hopkins University School of Public Health, Baltimore, MD, USA
    Methods Mol Biol 852:133-50. 2012
    ..In this chapter, we describe the assembly of 3-kb fragments with an overlap of one building block (∼750 base pairs) into a 40-kb DNA piece...
  12. pmc HistoneHits: a database for histone mutations and their phenotypes
    Hailiang Huang
    Department of Biomedical Engineering, Johns Hopkins University and School of Medicine, Baltimore, Maryland 21218, USA
    Genome Res 19:674-81. 2009
    ..All data sets are freely available for download...
  13. pmc Resolving the structure of interactomes with hierarchical agglomerative clustering
    Yongjin Park
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    BMC Bioinformatics 12:S44. 2011
    ....
  14. pmc Probing nucleosome function: a highly versatile library of synthetic histone H3 and H4 mutants
    Junbiao Dai
    High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
    Cell 134:1066-78. 2008
    ....
  15. doi request reprint Design-A-Gene with GeneDesign
    Sarah M Richardson
    High Throughput Biology Center, Johns Hopkins University School of Public Health, Baltimore, MD, USA
    Methods Mol Biol 852:235-47. 2012
    ..GeneDesign is a set of modules that automate batch nucleotide manipulation. Here, we explain the installation, configuration, and use of GeneDesign as part of a synthetic design workflow...
  16. doi request reprint The Build-a-Genome course
    Eric M Cooper
    High Throughput Biology Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
    Methods Mol Biol 852:273-83. 2012
    ..In this chapter, we describe the organization of the course and provide advice for anyone interested in starting a similar course at their own institution...
  17. pmc Synthetic chromosome arms function in yeast and generate phenotypic diversity by design
    Jessica S Dymond
    High Throughput Biology Center, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, Maryland 21205, USA
    Nature 477:471-6. 2011
    ..When complete, the fully synthetic genome will allow massive restructuring of the yeast genome, and may open the door to a new type of combinatorial genetics based entirely on variations in gene content and copy number...
  18. pmc Predicting functional associations from metabolism using bi-partite network algorithms
    Balaji Veeramani
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD21218, USA
    BMC Syst Biol 4:95. 2010
    ....
  19. pmc CLONEQC: lightweight sequence verification for synthetic biology
    Pablo A Lee
    Department of Computer Science, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21215, USA
    Nucleic Acids Res 38:2617-23. 2010
    ..This software will be useful to laboratories conducting in-house DNA synthesis and is available at http://cloneqc.thruhere.net/ and as Berkeley Software Distribution (BSD) licensed source...
  20. pmc Gene function prediction from congruent synthetic lethal interactions in yeast
    Ping Ye
    Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
    Mol Syst Biol 1:2005.0026. 2005
    ..These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans...
  21. pmc Constructing the angiome: a global angiogenesis protein interaction network
    Liang Hui Chu
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, USA
    Physiol Genomics 44:915-24. 2012
    ..The results of this analysis can be used to identify genes and proteins in different disease conditions and putative targets for therapeutic interventions as high-ranked candidates for experimental validation...
  22. pmc Comprehensive evaluation of imputation performance in African Americans
    Pritam Chanda
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
    J Hum Genet 57:411-21. 2012
    ....
  23. pmc Novel peptide-specific quantitative structure-activity relationship (QSAR) analysis applied to collagen IV peptides with antiangiogenic activity
    Corban G Rivera
    Department of Biomedical Engineering, 613 Traylor Building, Johns Hopkins University, 720 Rutland Avenue, Baltimore, Maryland 21205, United States
    J Med Chem 54:6492-500. 2011
    ..We found that the models produced quantitatively accurate predictions of activity and provided insight into collagen IV derived peptide structure-activity relationships...
  24. doi request reprint Structure-based ab initio prediction of transcription factor-binding sites
    L Angela Liu
    Department of Biomedical Engineering and Institute for Multiscale Modeling of Biological Interactions, John Hopkins University, Baltimore, MD, USA
    Methods Mol Biol 541:23-41. 2009
    ..The additive approximation is not strictly necessary, and more detailed computations could be used to investigate non-additive effects...
  25. pmc Temporal profiling of the secretome during adipogenesis in humans
    Jun Zhong
    McKusick Nathans Institute of Genetic Medicine, Departments of Biological Chemistry, Oncology, Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
    J Proteome Res 9:5228-38. 2010
    ..This is the first large-scale quantitative proteomic study that combines two platforms, mass spectrometry and antibody arrays, to analyze the changes in the secretome during the course of adipogenesis in humans...
  26. pmc NeMo: Network Module identification in Cytoscape
    Corban G Rivera
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
    BMC Bioinformatics 11:S61. 2010
    ..In this work, we present a novel method to identify densely connected and bipartite network modules based on a log odds score for shared neighbours...
  27. pmc Precision and recall estimates for two-hybrid screens
    Hailiang Huang
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, MD, USA
    Bioinformatics 25:372-8. 2009
    ..Previously, we reported a capture-recapture estimator for bait-specific precision and recall. Here, we present an improved method that better accounts for heterogeneity in bait-specific error rates...
  28. pmc GeneDesign 3.0 is an updated synthetic biology toolkit
    Sarah M Richardson
    McKusick Nathans Institute of Genetic Medicine, High Throughput Biology Center, Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA
    Nucleic Acids Res 38:2603-6. 2010
    ....
  29. ncbi request reprint Greedily building protein networks with confidence
    Joel S Bader
    CuraGen Corporation, 555 Long Wharf Drive, New Haven, CT 06511, USA
    Bioinformatics 19:1869-74. 2003
    ..We focus on the problem of building pathways starting from known proteins of interest...
  30. pmc Fast association tests for genes with FAST
    Pritam Chanda
    Department of Biomedical Engineering and Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
    PLoS ONE 8:e68585. 2013
    ..Availability: https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home. ..
  31. pmc Gene-based tests of association
    Hailiang Huang
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
    PLoS Genet 7:e1002177. 2011
    ..This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis...
  32. ncbi request reprint A DNA integrity network in the yeast Saccharomyces cerevisiae
    Xuewen Pan
    Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, 733 North Broadway, Baltimore, MD 21205, USA
    Cell 124:1069-81. 2006
    ..This network will guide more detailed characterization of mechanisms governing DNA integrity in yeast and other organisms...
  33. pmc Teaching synthetic biology, bioinformatics and engineering to undergraduates: the interdisciplinary Build-a-Genome course
    Jessica S Dymond
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, USA
    Genetics 181:13-21. 2009
    ..Regular "lab meeting" sessions help prepare them for future roles in laboratory science...
  34. pmc HapZipper: sharing HapMap populations just got easier
    Pritam Chanda
    Department of Biomedical Engineering, Johns Hopkins University, High Throughput Biology Center, Johns Hopkins University School of Medicine, McKusick Nathans Institute of Genetic Medicine, Baltimore, MD 21205, USA
    Nucleic Acids Res 40:e159. 2012
    ..We demonstrate the usefulness of HapZipper by compressing HapMap 3 populations to <5% of their original sizes. HapZipper is freely downloadable from https://bitbucket.org/pchanda/hapzipper/downloads/HapZipper.tar.bz2...
  35. pmc Metabolic flux correlations, genetic interactions, and disease
    Balaji Veeramani
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
    J Comput Biol 16:291-302. 2009
    ..These methods will be useful in predicting genetic interactions in model organisms and understanding the combinatorial effects of genetic variations in humans...
  36. pmc Dynamic networks from hierarchical bayesian graph clustering
    Yongjin Park
    Department of Biomedical Engineering and High Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA
    PLoS ONE 5:e8118. 2010
    ....
  37. pmc Genetic Interaction Motif Finding by expectation maximization--a novel statistical model for inferring gene modules from synthetic lethality
    Yan Qi
    Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD 21218, USA
    BMC Bioinformatics 6:288. 2005
    ..Probabilistic algorithms that identify gene modules based on motif discovery are highly appropriate for the analysis of synthetic lethal genetic interaction data and have great potential in integrative analysis of heterogeneous datasets...
  38. pmc Analysis of VEGF--a regulated gene expression in endothelial cells to identify genes linked to angiogenesis
    Corban G Rivera
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
    PLoS ONE 6:e24887. 2011
    ..The analysis showed differential regulation of HIF-1α and HIF-2α. The data also provided additional evidence for the role of endothelial cells in Alzheimer's disease...
  39. ncbi request reprint A robust toolkit for functional profiling of the yeast genome
    Xuewen Pan
    Department of Molecular Biology and Genetics, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
    Mol Cell 16:487-96. 2004
    ..Direct comparison revealed that these techniques are more robust than existing methods in functional profiling of the yeast genome. Widespread application of these tools will elucidate a comprehensive yeast genetic network...
  40. doi request reprint RNA-Seq optimization with eQTL gold standards
    Shannon E Ellis
    McKusick Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, USA
    BMC Genomics 14:892. 2013
    ..Further, a method to assess normalization and adjustment measures imposed on the data is lacking...
  41. doi request reprint Modeling intercellular transfer of biomolecules through tunneling nanotubes
    Yasir Suhail
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
    Bull Math Biol 75:1400-16. 2013
    ..The model described makes certain predictions and opens a number of questions to be explored experimentally. ..
  42. pmc Analytical approximations for the amplitude and period of a relaxation oscillator
    Carmen Kut
    Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
    BMC Syst Biol 3:6. 2009
    ..Analytical approximations have identified criteria for sustained oscillations, but have not linked the observed period and phase to compact formulas involving underlying molecular parameters...
  43. pmc Angiogenesis-associated crosstalk between collagens, CXC chemokines, and thrombospondin domain-containing proteins
    Corban G Rivera
    Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
    Ann Biomed Eng 39:2213-22. 2011
    ..We identified six proteins at the center of the angiogenesis-associated network including three syndecans, MMP9, CD44, and versican. These findings shed light on the complex signaling networks that govern angiogenesis phenomena...
  44. pmc Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps
    Hailiang Huang
    Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
    PLoS Comput Biol 3:e214. 2007
    ..All software and datasets are available in and , -, and -, and are also available from our Web site, http://www.baderzone.org...
  45. pmc Phosphoproteomic analysis of Her2/neu signaling and inhibition
    Ron Bose
    Department of Pharmacology, McKusick Nathans Institute for Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
    Proc Natl Acad Sci U S A 103:9773-8. 2006
    ..Network modeling, which combined phosphoproteomic results with literature-curated protein-protein interaction data, was used to suggest roles for some of the previously unidentified Her2 signaling proteins...
  46. ncbi request reprint Probabilistic inference of molecular networks from noisy data sources
    Ivan Iossifov
    Department of Medical Informatics, Columbia University, New York, NY 10032, USA
    Bioinformatics 20:1205-13. 2004
    ..We further explore the prediction limits, given experimental data that cover only part of the underlying protein networks. This approach can be extended naturally to include other types of biological data sources...
  47. doi request reprint Human Proteinpedia enables sharing of human protein data
    Suresh Mathivanan
    Nat Biotechnol 26:164-7. 2008
  48. ncbi request reprint Optimal selection strategies for QTL mapping using pooled DNA samples
    Ansar Jawaid
    Department of Psychological Medicine, Institute of Psychiatry, King s College London, London SE5 8AF, UK
    Eur J Hum Genet 10:125-32. 2002
    ..Our results emphasize the importance of minimising experimental errors and suggest a pooling fraction of around 20%...
  49. ncbi request reprint Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets
    T K B Gandhi
    Institute of Bioinformatics, International Technology Park, Bangalore 560 066, India
    Nat Genet 38:285-93. 2006
    ..The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states...
  50. pmc Genome sequencing in microfabricated high-density picolitre reactors
    Marcel Margulies
    454 Life Sciences Corp, 20 Commercial Street, Branford, Connecticut 06405, USA
    Nature 437:376-80. 2005
    ..Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine...
  51. pmc Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS
    Udayan Guha
    Program in Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
    Proc Natl Acad Sci U S A 105:14112-7. 2008
    ..Bayesian network analysis of these and other datasets revealed that PTRF might be a potentially important component of the ERBB signaling network...
  52. doi request reprint Systems approaches for pharmacogenetics and pharmacogenomics
    Joel S Bader
    Pharmacogenomics 9:257-62. 2008

Research Grants3

  1. Structural, Functional & Evolutionary Genomics Gordon Conference
    Joel Bader; Fiscal Year: 2007
    ..In that regard, this GRC stands a chance to be particularly fruitful because it will be the first meeting at a new GRC site, Hinxton (UK), a major center of Genomics, Systems Biology, and Bioinformatics for the UK and Europe. ..
  2. Mapping disease-specific human protein networks
    Joel Bader; Fiscal Year: 2005
    ..Success in Phase I of this project will result in improved methods for mapping disease-relevant human biological networks. Success in Phase II will result in targets for therapeutic intervention. ..
  3. Comparative systematic genetics for cardiovascular disease gene identification
    Joel Bader; Fiscal Year: 2006
    ..The comparative systematic genetics platform developed will be broadly applicable to identifying gene modules relevant to other human diseases and will extend to systematic genetics data collected for a growing set of model organisms. ..