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Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data
Motivation: Due to the high mutation rate of human immunodeficiency virus (HIV), drug-resistant-variants emerge frequently. Therefore, researchers are constantly searching for new ways to attack the virus. One new class of anti-HIV drugs is the class of coreceptor antagonists that block cell entry b...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436800/ https://www.ncbi.nlm.nih.gov/pubmed/22962486 http://dx.doi.org/10.1093/bioinformatics/bts373 |
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author | Pfeifer, Nico Lengauer, Thomas |
author_facet | Pfeifer, Nico Lengauer, Thomas |
author_sort | Pfeifer, Nico |
collection | PubMed |
description | Motivation: Due to the high mutation rate of human immunodeficiency virus (HIV), drug-resistant-variants emerge frequently. Therefore, researchers are constantly searching for new ways to attack the virus. One new class of anti-HIV drugs is the class of coreceptor antagonists that block cell entry by occupying a coreceptor on CD4 cells. This type of drug just has an effect on the subset of HIVs that use the inhibited coreceptor. A good prediction of whether the viral population inside a patient is susceptible to the treatment is hence very important for therapy decisions and pre-requisite to administering the respective drug. The first prediction models were based on data from Sanger sequencing of the V3 loop of HIV. Recently, a method based on next-generation sequencing (NGS) data was introduced that predicts labels for each read separately and decides on the patient label through a percentage threshold for the resistant viral minority. Results: We model the prediction problem on the patient level taking the information of all reads from NGS data jointly into account. This enables us to improve prediction performance for NGS data, but we can also use the trained model to improve predictions based on Sanger sequencing data. Therefore, also laboratories without NGS capabilities can benefit from the improvements. Furthermore, we show which amino acids at which position are important for prediction success, giving clues on how the interaction mechanism between the V3 loop and the particular coreceptors might be influenced. Availability: A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de. Contact: nico.pfeifer@mpi-inf.mpg.de |
format | Online Article Text |
id | pubmed-3436800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-34368002012-12-12 Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data Pfeifer, Nico Lengauer, Thomas Bioinformatics Original Papers Motivation: Due to the high mutation rate of human immunodeficiency virus (HIV), drug-resistant-variants emerge frequently. Therefore, researchers are constantly searching for new ways to attack the virus. One new class of anti-HIV drugs is the class of coreceptor antagonists that block cell entry by occupying a coreceptor on CD4 cells. This type of drug just has an effect on the subset of HIVs that use the inhibited coreceptor. A good prediction of whether the viral population inside a patient is susceptible to the treatment is hence very important for therapy decisions and pre-requisite to administering the respective drug. The first prediction models were based on data from Sanger sequencing of the V3 loop of HIV. Recently, a method based on next-generation sequencing (NGS) data was introduced that predicts labels for each read separately and decides on the patient label through a percentage threshold for the resistant viral minority. Results: We model the prediction problem on the patient level taking the information of all reads from NGS data jointly into account. This enables us to improve prediction performance for NGS data, but we can also use the trained model to improve predictions based on Sanger sequencing data. Therefore, also laboratories without NGS capabilities can benefit from the improvements. Furthermore, we show which amino acids at which position are important for prediction success, giving clues on how the interaction mechanism between the V3 loop and the particular coreceptors might be influenced. Availability: A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de. Contact: nico.pfeifer@mpi-inf.mpg.de Oxford University Press 2012-09-15 2012-09-03 /pmc/articles/PMC3436800/ /pubmed/22962486 http://dx.doi.org/10.1093/bioinformatics/bts373 Text en © The Author(s) (2012). Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Pfeifer, Nico Lengauer, Thomas Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title | Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title_full | Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title_fullStr | Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title_full_unstemmed | Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title_short | Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
title_sort | improving hiv coreceptor usage prediction in the clinic using hints from next-generation sequencing data |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436800/ https://www.ncbi.nlm.nih.gov/pubmed/22962486 http://dx.doi.org/10.1093/bioinformatics/bts373 |
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