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The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data
BACKGROUND: In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances an...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634160/ https://www.ncbi.nlm.nih.gov/pubmed/26538192 http://dx.doi.org/10.1186/s12859-015-0791-x |
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author | Vrbik, Irene Stephens, David A. Roger, Michel Brenner, Bluma G. |
author_facet | Vrbik, Irene Stephens, David A. Roger, Michel Brenner, Bluma G. |
author_sort | Vrbik, Irene |
collection | PubMed |
description | BACKGROUND: In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application. RESULTS: This paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis. CONCLUSIONS: Apart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0791-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4634160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46341602015-11-06 The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data Vrbik, Irene Stephens, David A. Roger, Michel Brenner, Bluma G. BMC Bioinformatics Methodology BACKGROUND: In the context of infectious disease, sequence clustering can be used to provide important insights into the dynamics of transmission. Cluster analysis is usually performed using a phylogenetic approach whereby clusters are assigned on the basis of sufficiently small genetic distances and high bootstrap support (or posterior probabilities). The computational burden involved in this phylogenetic threshold approach is a major drawback, especially when a large number of sequences are being considered. In addition, this method requires a skilled user to specify the appropriate threshold values which may vary widely depending on the application. RESULTS: This paper presents the Gap Procedure, a distance-based clustering algorithm for the classification of DNA sequences sampled from individuals infected with the human immunodeficiency virus type 1 (HIV-1). Our heuristic algorithm bypasses the need for phylogenetic reconstruction, thereby supporting the quick analysis of large genetic data sets. Moreover, this fully automated procedure relies on data-driven gaps in sorted pairwise distances to infer clusters, thus no user-specified threshold values are required. The clustering results obtained by the Gap Procedure on both real and simulated data, closely agree with those found using the threshold approach, while only requiring a fraction of the time to complete the analysis. CONCLUSIONS: Apart from the dramatic gains in computational time, the Gap Procedure is highly effective in finding distinct groups of genetically similar sequences and obviates the need for subjective user-specified values. The clusters of genetically similar sequences returned by this procedure can be used to detect patterns in HIV-1 transmission and thereby aid in the prevention, treatment and containment of the disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0791-x) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-04 /pmc/articles/PMC4634160/ /pubmed/26538192 http://dx.doi.org/10.1186/s12859-015-0791-x Text en © Vrbik et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Vrbik, Irene Stephens, David A. Roger, Michel Brenner, Bluma G. The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title | The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title_full | The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title_fullStr | The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title_full_unstemmed | The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title_short | The Gap Procedure: for the identification of phylogenetic clusters in HIV-1 sequence data |
title_sort | gap procedure: for the identification of phylogenetic clusters in hiv-1 sequence data |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634160/ https://www.ncbi.nlm.nih.gov/pubmed/26538192 http://dx.doi.org/10.1186/s12859-015-0791-x |
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