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Manke, T.* ; Heinig, M.* ; Vingron, M.*

Quantifying the effect of sequence variation on regulatory interactions.

Hum. Mutat. 31, 477-483 (2010)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
The increasing amount of sequence data provides new opportunities and challenges to derive mechanistic models that can link sequence variations to phenotypic diversity. Here we introduce a new computational framework to suggest possible consequences of sequence variations on regulatory networks. Our method, called sTRAP (strap.molgen.mpg.de), analyses variations in the DNA sequence and predicts quantitative changes to the binding strength of any transcription factor for which there is a binding model. We have tested the method against a set of known associations between SNPs and their regulatory consequences. Our predictions are robust with respect to different parameters and model assumptions. Importantly we set an objective and quantifiable benchmark against which future improvements can be compared. Given the good performance of our method, we developed a publicly available tool that can serve as an important starting point for routine analysis of disease-associated sequence regions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2010
HGF-Berichtsjahr 0
ISSN (print) / ISBN 1059-7794
e-ISSN 1098-1004
Zeitschrift Human Mutation
Quellenangaben Band: 31, Heft: 4, Seiten: 477-483 Artikelnummer: , Supplement: ,
Verlag Wiley
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-553500-001
PubMed ID 20127973
DOI 10.1002/humu.21209
Erfassungsdatum 2010-12-31
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