TY - JOUR
T1 - A modified bacterial one-hybrid system yields improved quantitative models of transcription factor specificity
AU - Christensen, Ryan G.
AU - Gupta, Ankit
AU - Zuo, Zheng
AU - Schriefer, Lawrence A.
AU - Wolfe, Scot A.
AU - Stormo, Gary D.
N1 - Funding Information:
Funding for open access charge: National Institutes of Health (R24GM078369, R01HG004744 and R01HG00249).
PY - 2011/7
Y1 - 2011/7
N2 - We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis-GRaMS (Growth Rate Modeling of Specificity)-that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C2H2 zinc-finger TF on both a 28bp randomized library for the standard B1H method and on 6bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media.
AB - We examine the use of high-throughput sequencing on binding sites recovered using a bacterial one-hybrid (B1H) system and find that improved models of transcription factor (TF) binding specificity can be obtained compared to standard methods of sequencing a small subset of the selected clones. We can obtain even more accurate binding models using a modified version of B1H selection method with constrained variation (CV-B1H). However, achieving these improved models using CV-B1H data required the development of a new method of analysis-GRaMS (Growth Rate Modeling of Specificity)-that estimates bacterial growth rates as a function of the quality of the recognition sequence. We benchmark these different methods of motif discovery using Zif268, a well-characterized C2H2 zinc-finger TF on both a 28bp randomized library for the standard B1H method and on 6bp randomized library for the CV-B1H method for which 45 different experimental conditions were tested: five time points and three different IPTG and 3-AT concentrations. We find that GRaMS analysis is robust to the different experimental parameters whereas other analysis methods give widely varying results depending on the conditions of the experiment. Finally, we demonstrate that the CV-B1H assay can be performed in liquid media, which produces recognition models that are similar in quality to sequences recovered from selection on solid media.
UR - http://www.scopus.com/inward/record.url?scp=79960225382&partnerID=8YFLogxK
U2 - 10.1093/nar/gkr239
DO - 10.1093/nar/gkr239
M3 - Article
C2 - 21507886
AN - SCOPUS:79960225382
SN - 0305-1048
VL - 39
SP - e83
JO - Nucleic acids research
JF - Nucleic acids research
IS - 12
ER -