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Author, Editor(s)

Author(s):

Pfeifer, Nico
Lengauer, Thomas

dblp
dblp



BibTeX cite key*:

Pfeifer2012

Title

Title*:

Improving HIV coreceptor usage prediction in the clinic using hints from next-generation sequencing data

Journal

Journal Title*:

Bioinformatics

Journal's URL:

http://bioinformatics.oxfordjournals.org/

Download URL
for the article:

http://bioinformatics.oxfordjournals.org/content/28/18/i589.full.pdf

Language:

English

Publisher

Publisher's
Name:

Oxford University Press

Publisher's URL:

http://global.oup.com/?cc=de

Publisher's
Address:

Oxford, UK

ISSN:

14602059

Vol, No, pp, Date

Volume*:

28

Number:

18

Publishing Date:

September 2012

Pages*:

i589-i595

Number of
VG Pages:


Page Start:


Page End:


Sequence Number:


DOI:

10.1093/bioinformatics/bts373

Note, Abstract, ©

Note:


(LaTeX) Abstract:

\section{Motivation:}
Due to the high mutation rate of 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 prerequisite 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 via a percentage threshold for the resistant viral minority.

\section{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 next generation sequencing 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.

\section{Availability:}
A webserver is available at http://coreceptor.bioinf.mpi-inf.mpg.de.
\href{http://coreceptor.bioinf.mpi-inf.mpg.de/}{http://coreceptor.bioinf.mpi-inf.mpg.de/}.

\section{Contact:} \href{nico.pfeifer@mpi-inf.mpg.de}{nico.pfeifer@mpi-inf.mpg.de}

URL for the Abstract:

http://dx.doi.org/10.1093/bioinformatics/bts373

Categories,
Keywords:

Computational Biology, HIV, Coreceptor Usage, Machine Learning, kernels, next-generation sequencing

HyperLinks / References / URLs:

http://dx.doi.org/10.1093/bioinformatics/bts373

Copyright Message:

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.

Personal Comments:


Download
Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computational Biology and Applied Algorithmics

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort


BibTeX Entry:

@ARTICLE{Pfeifer2012,
AUTHOR = {Pfeifer, Nico and Lengauer, Thomas},
TITLE = {Improving {HIV} coreceptor usage prediction in the clinic using hints from next-generation sequencing data},
JOURNAL = {Bioinformatics},
PUBLISHER = {Oxford University Press},
YEAR = {2012},
NUMBER = {18},
VOLUME = {28},
PAGES = {i589--i595},
ADDRESS = {Oxford, UK},
MONTH = {September},
ISBN = {14602059},
DOI = {10.1093/bioinformatics/bts373},
}


Entry last modified by Anja Becker, 02/14/2013
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Editor(s)
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Created
12/12/2012 04:28:55 PM
Revisions
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Editor(s)
Anja Becker
Anja Becker
Anja Becker
Ruth Schneppen-Christmann
Ruth Schneppen-Christmann
Edit Dates
14.02.2013 11:37:54
14.02.2013 11:25:33
14.02.2013 11:25:26
10.01.2013 11:36:18
01/07/2013 04:24:03 PM