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Series GSE130833 Query DataSets for GSE130833
Status Public on May 08, 2019
Title QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants III
Platform organism synthetic construct
Sample organism Homo sapiens
Experiment type Other
Summary Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a p-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation data sets in several formats, and it allows post-processing of the results through a user- friendly web interface. QBiC-Pred is freely available at
Overall design Universal protein-binding microarray (PBM) experiments were performed for recombinant, epitope-tagged transcription factors from human, mouse, and A. thaliana. Briefly, universal PBMs involved binding of GST-tagged, His-tagged, or Flag-tagged transcription factors to double-stranded Agilent microarrays containing a DNA library designed to cover all possible 10-bp sequences, with every 8-mer occurring in at least 16 different spots on the array. This design allows comprehensive and unbiased characterization of the binding specificity of transcription factors for all possible 8-bp sequences.
Contributor(s) Gordan R
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Submission date May 07, 2019
Last update date May 09, 2019
Contact name Raluca Gordan
Organization name Duke University
Department Center for Genomic and Computational Biology
Street address 101 Science Dr, CIEMAS 2179
City Durham
State/province NC
ZIP/Postal code 27708
Country USA
Platforms (1)
GPL26634 Duke/RG_uPBM_4x44k_with_GR_probes
Samples (1)
GSM3754997 NR3C1 at 800 nM concentration
This SubSeries is part of SuperSeries:
GSE130837 QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants
BioProject PRJNA541543

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE130833_RAW.tar 1.6 Mb (http)(custom) TAR (of TXT)
Processed data provided as supplementary file

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