A computational framework for the analysis of peptide microarray antibody binding data with application to HIV vaccine profiling
We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding a...
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| Published in | Journal of immunological methods Vol. 395; no. 1-2; pp. 1 - 13 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
30.09.2013
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0022-1759 1872-7905 1872-7905 |
| DOI | 10.1016/j.jim.2013.06.001 |
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| Abstract | We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework. |
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| AbstractList | We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework.We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework. We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework. We present an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization. Current techniques for the normalization of such data sets do not account for non-specific binding activity. A novel normalization technique based on peptide sequence information quickly and effectively reduced systematic biases. We also employed a sliding mean window technique that borrows strength from peptides sharing similar sequences, resulting in reduced signal variability. A smoothed signal aided in the detection of weak antibody binding hotspots. A new principled FDR method of setting positivity thresholds struck a balance between sensitivity and specificity. In addition, we demonstrate the utility and importance of using baseline control measurements when making subject-specific positivity calls. Data sets from two human clinical trials of candidate HIV-1 vaccines were used to validate the effectiveness of our overall computational framework. |
| Author | Shen, Xiaoying Koup, Richard A. Sauteraud, Renan Gottardo, Raphael Bailer, Robert T. Montefiori, David C. Imholte, Greg C. Tomaras, Georgia D. Mascola, John R. Turk, Ellen T. Korber, Bette |
| AuthorAffiliation | 6 Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710 3 Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health Bethesda, MD 20892 4 Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico 5 Santa Fe Institute, Santa Fe, New Mexico 2 Department of Statistics, University of Washington Box 354322, Seattle, WA 98195-4322 1 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, PO Box 19024, Seattle, WA 98109-1024 |
| AuthorAffiliation_xml | – name: 3 Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health Bethesda, MD 20892 – name: 6 Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710 – name: 2 Department of Statistics, University of Washington Box 354322, Seattle, WA 98195-4322 – name: 5 Santa Fe Institute, Santa Fe, New Mexico – name: 1 Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, PO Box 19024, Seattle, WA 98109-1024 – name: 4 Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico |
| Author_xml | – sequence: 1 givenname: Greg C. surname: Imholte fullname: Imholte, Greg C. email: gimholte@uw.edu organization: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, PO Box 19024, Seattle, WA 98109-1024, United States – sequence: 2 givenname: Renan surname: Sauteraud fullname: Sauteraud, Renan organization: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, PO Box 19024, Seattle, WA 98109-1024, United States – sequence: 3 givenname: Bette surname: Korber fullname: Korber, Bette organization: Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, United States – sequence: 4 givenname: Robert T. surname: Bailer fullname: Bailer, Robert T. organization: Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, United States – sequence: 5 givenname: Ellen T. surname: Turk fullname: Turk, Ellen T. organization: Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, United States – sequence: 6 givenname: Xiaoying surname: Shen fullname: Shen, Xiaoying organization: Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710, United States – sequence: 7 givenname: Georgia D. surname: Tomaras fullname: Tomaras, Georgia D. organization: Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710, United States – sequence: 8 givenname: John R. surname: Mascola fullname: Mascola, John R. organization: Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, United States – sequence: 9 givenname: Richard A. surname: Koup fullname: Koup, Richard A. organization: Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, United States – sequence: 10 givenname: David C. surname: Montefiori fullname: Montefiori, David C. organization: Duke Human Vaccine Institute, Duke University Medical Center, Durham, NC 27710, United States – sequence: 11 givenname: Raphael surname: Gottardo fullname: Gottardo, Raphael email: rgottard@fhcrc.org organization: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-C200, PO Box 19024, Seattle, WA 98109-1024, United States |
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| Copyright | 2013 Elsevier B.V. Copyright © 2013 Elsevier B.V. All rights reserved. 2013 Elsevier B.V. All rights reserved. 2013 |
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| SubjectTerms | AIDS Vaccines - immunology analytical methods Antibodies antibody microarrays Antibody Specificity clinical trials Clinical Trials as Topic - statistics & numerical data data collection Data Interpretation, Statistical Epitope Mapping - statistics & numerical data Epitopes - metabolism HIV Antibodies - biosynthesis HIV Antibodies - metabolism HIV Antigens - metabolism HIV-1 - immunology Human immunodeficiency virus 1 Humans Immunologic Techniques - methods Immunologic Techniques - statistics & numerical data Normalization Peptide microarrays peptides Positivity calls Protein Array Analysis - methods Protein Array Analysis - statistics & numerical data Protein Interaction Mapping - statistics & numerical data ROC Curve Software vaccines Visualization |
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| Title | A computational framework for the analysis of peptide microarray antibody binding data with application to HIV vaccine profiling |
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