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Development of a novel clustering tool for linear peptide sequences
Epitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capa...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187223/ https://www.ncbi.nlm.nih.gov/pubmed/30014462 http://dx.doi.org/10.1111/imm.12984 |
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author | Dhanda, Sandeep K. Vaughan, Kerrie Schulten, Veronique Grifoni, Alba Weiskopf, Daniela Sidney, John Peters, Bjoern Sette, Alessandro |
author_facet | Dhanda, Sandeep K. Vaughan, Kerrie Schulten, Veronique Grifoni, Alba Weiskopf, Daniela Sidney, John Peters, Bjoern Sette, Alessandro |
author_sort | Dhanda, Sandeep K. |
collection | PubMed |
description | Epitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the ‘clique method’, in which all members of the cluster must share the same minimal level of identity with each other, and the ‘connected graph method’, in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel ‘cluster‐breaking algorithm’ for consensus sequence driven sub‐clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the ‘clique method’, (ii) sets of allergen‐derived peptides from related species for the ‘connected graph method’ and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T‐cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed ‘cluster‐breaking algorithm’. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/. |
format | Online Article Text |
id | pubmed-6187223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61872232018-10-22 Development of a novel clustering tool for linear peptide sequences Dhanda, Sandeep K. Vaughan, Kerrie Schulten, Veronique Grifoni, Alba Weiskopf, Daniela Sidney, John Peters, Bjoern Sette, Alessandro Immunology Original Articles Epitopes identified in large‐scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope‐related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the ‘clique method’, in which all members of the cluster must share the same minimal level of identity with each other, and the ‘connected graph method’, in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel ‘cluster‐breaking algorithm’ for consensus sequence driven sub‐clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the ‘clique method’, (ii) sets of allergen‐derived peptides from related species for the ‘connected graph method’ and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T‐cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed ‘cluster‐breaking algorithm’. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/. John Wiley and Sons Inc. 2018-08-06 2018-11 /pmc/articles/PMC6187223/ /pubmed/30014462 http://dx.doi.org/10.1111/imm.12984 Text en © 2018 The Authors. Immunology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Articles Dhanda, Sandeep K. Vaughan, Kerrie Schulten, Veronique Grifoni, Alba Weiskopf, Daniela Sidney, John Peters, Bjoern Sette, Alessandro Development of a novel clustering tool for linear peptide sequences |
title | Development of a novel clustering tool for linear peptide sequences |
title_full | Development of a novel clustering tool for linear peptide sequences |
title_fullStr | Development of a novel clustering tool for linear peptide sequences |
title_full_unstemmed | Development of a novel clustering tool for linear peptide sequences |
title_short | Development of a novel clustering tool for linear peptide sequences |
title_sort | development of a novel clustering tool for linear peptide sequences |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6187223/ https://www.ncbi.nlm.nih.gov/pubmed/30014462 http://dx.doi.org/10.1111/imm.12984 |
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