Stability selection for regression-based models of transcription factor-DNA binding specificity.

Mordelet, Fantine

Horton, John

Hartemink, Alexander J

Engelhardt, Barbara E

Gordân, Raluca

England

2017-08-01T20:48:48Z

2017-08-01T20:48:48Z

2013-07-01

MOTIVATION: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. RESULTS: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein-binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max and Mad2) in their native genomic context. These high-throughput quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar position weight matrices, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step toward better sequence-based models of individual TF-DNA binding specificity. AVAILABILITY: Our code is available at http://genome.duke.edu/labs/gordan/ISMB2013. The PBM data used in this article are available in the Gene Expression Omnibus under accession number GSE47026.

https://www.ncbi.nlm.nih.gov/pubmed/23812975

btt221

1367-4811

https://hdl.handle.net/10161/15155

eng

Oxford University Press (OUP)

Bioinformatics

10.1093/bioinformatics/btt221

Algorithms

Binding Sites

DNA

Genome

Humans

Linear Models

Protein Array Analysis

Protein Binding

Saccharomyces cerevisiae Proteins

Support Vector Machine

Transcription Factors

Stability selection for regression-based models of transcription factor-DNA binding specificity.

Journal article

Hartemink, Alexander J|0000-0002-1292-2606

https://www.ncbi.nlm.nih.gov/pubmed/23812975

i117

i125

13

Basic Science Departments

Biostatistics & Bioinformatics

Computer Science

Duke

Faculty

Molecular Genetics and Microbiology

School of Medicine

Trinity College of Arts & Sciences

Published

29