Abstract
How to identify true transcription factor binding sites on the basis of sequence motif information (e.g., motif pattern, location, combination, etc.) is an important question in bioinformatics. We present “PeakRegressor,” a system that identifies binding motifs by combining DNA-sequence data and ChIP-Seq data. PeakRegressor uses L1-norm log linear regression in order to predict peak values from binding motif candidates. Our approach successfully predicts the peak values of STAT1 and RNA Polymerase II with correlation coefficients as high as 0.65 and 0.66, respectively. Using PeakRegressor, we could identify composite motifs for STAT1, as well as potential regulatory SNPs (rSNPs) involved in the regulation of transcription levels of neighboring genes. In addition, we show that among five regression methods, L1-norm log linear regression achieves the best performance with respect to binding motif identification, biological interpretability and computational efficiency.
Highlights
The experimental identification of cis-regulatory sites based on transcription factor binding motifs (TFBMs) is a difficult and timeconsuming task
In PeakRegressor, we choose to use the L1-norm log linear regression to solve this problem. This approach favors sparse solutions and we argue that it is more suitable for the TFBM identification problem
We considered the following regression methods for TFBM identification: L1-norm log linear regression, linear least squares regression, ridge regression, partial least squares regression, and principal component regression
Summary
The experimental identification of cis-regulatory sites based on transcription factor binding motifs (TFBMs) is a difficult and timeconsuming task. With the RNA Polymerase II dataset (Table 5), linear least squares regression is able to identify the initiator site and the downstream promoter element.
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