English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features

Döring, M., Kreer, C., Lehnen, N., Klein, F., & Pfeifer, N. (2019). Modeling the Amplification of Immunoglobulins through Machine Learning on Sequence-Specific Features. Scientific Reports, 9: 10748. doi:10.1038/s41598-019-47173-w.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0004-5F56-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0004-830F-8
Genre: Journal Article

Files

show Files
hide Files
:
s41598-019-47173-w.pdf (Publisher version), 3MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-0004-715E-4
Name:
s41598-019-47173-w.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
© The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per- mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
License:
http://creativecommons.org/licenses/by/4.0/

Locators

show

Creators

show
hide
 Creators:
Döring, Matthias1, Author           
Kreer, Christoph2, Author
Lehnen, Nathalie2, Author
Klein, Florian2, Author
Pfeifer, Nico1, Author           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: Successful primer design for polymerase chain reaction (PCR) hinges on the ability to identify primers
that efciently amplify template sequences. Here, we generated a novel Taq PCR data set that reports
the amplifcation status for pairs of primers and templates from a reference set of 47 immunoglobulin
heavy chain variable sequences and 20 primers. Using logistic regression, we developed TMM, a model
for predicting whether a primer amplifes a template given their nucleotide sequences. The model
suggests that the free energy of annealing, ΔG, is the key driver of amplifcation (p=7.35e-12) and that
3′ mismatches should be considered in dependence on ΔG and the mismatch closest to the 3′ terminus
(p=1.67e-05). We validated TMM by comparing its estimates with those from the thermodynamic
model of DECIPHER (DE) and a model based solely on the free energy of annealing (FE). TMM
outperformed the other approaches in terms of the area under the receiver operating characteristic
curve (TMM: 0.953, FE: 0.941, DE: 0.896). TMM can improve primer design and is freely available via
openPrimeR (http://openPrimeR.mpi-inf.mpg.de).

Details

show
hide
Language(s): eng - English
 Dates: 2018-11-292019-06-102019-07-24
 Publication Status: Published online
 Pages: 11 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: DöringPfeiferModel
DOI: 10.1038/s41598-019-47173-w
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: London, UK : Nature Publishing Group
Pages: 11 p. Volume / Issue: 9 Sequence Number: 10748 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322