CSI-Tree: a regression tree approach for modeling binding properties of DNA-binding molecules based on cognate site identification (CSI) data.
The identification and characterization of binding sites of DNA-binding molecules, including transcription factors (TFs), is a critical problem at the interface of chemistry, biology and molecular medicine. The Cognate Site Identification (CSI) array is a high-throughput microarray platform for measuring comprehensive recognition profiles of DNA-binding molecules. This technique produces datasets that are useful not only for identifying binding sites of previously uncharacterized TFs but also for elucidating dependencies, both local and nonlocal, between the nucleotides at different positions of the recognition sites. We have developed a regression tree technique, CSI-Tree, for exploring the spectrum of binding sites of DNA-binding molecules. Our approach constructs regression trees utilizing the CSI data of unaligned sequences. The resulting model partitions the binding spectrum into homogeneous regions of position specific nucleotide effects. Each homogeneous partition is then summarized by a position weight matrix (PWM). Hence, the final outcome is a binding intensity rank-ordered collection of PWMs each of which spans a different region in the binding spectrum. Nodes of the regression tree depict the critical position/nucleotide combinations. We analyze the CSI data of the eukaryotic TF Nkx-2.5 and two engineered small molecule DNA ligands and obtain unique insights into their binding properties. The CSI tree for Nkx-2.5 reveals an interaction between two positions of the binding profile and elucidates how different nucleotide combinations at these two positions lead to different binding affinities. The CSI trees for the engineered DNA ligands exhibit a common preference for the dinucleotide AA in the first two positions, which is consistent with preference for a narrow and relatively flat minor groove. We carry out a reanalysis of these data with a mixture of PWMs approach. This approach is an advancement over the simple PWM model and accommodates position dependencies based on only sequence data. Our analysis indicates that the dependencies revealed by the CSI-Tree are challenging to discover without the actual binding intensities. Moreover, such a mixture model is highly sensitive to the number and length of the sequences analyzed. In contrast, CSI-Tree provides interpretable and concise summaries of the complete recognition profiles of DNA-binding molecules by utilizing binding affinities.
Keleş
Sündüz
S
Department of Statistics, University of Wisconsin, Madison WI, USA.
Warren
Christopher L
CL
Carlson
Clayton D
CD
Ansari
Aseem Z
AZ
D000465:Algorithms; D000704:Analysis of Variance; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D004247:DNA / Q000737:chemistry* / Q000378:metabolism; D018398:Homeodomain Proteins / Q000737:chemistry / Q000378:metabolism; D016014:Linear Models; D015233:Models, Statistical; D009757:Nylons / Q000737:chemistry; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism
D018398:Homeodomain Proteins; D009757:Nylons; D014157:Transcription Factors; D004247:DNA