PeakRegressor identifies composite sequence motifs responsible for STAT1 binding sites and their potential rSNPs.
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.
Pessiot
Jean-François
JF
Computational Biology Research Center, Advanced Industrial Science and Technology, Tokyo, Japan.
Chiba
Hirokazu
H
Hyakkoku
Hiroto
H
Taniguchi
Takeaki
T
Fujibuchi
Wataru
W
D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology; D016014:Linear Models; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025341:Principal Component Analysis; D012319:RNA Polymerase II / Q000378:metabolism; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D012091:Repetitive Sequences, Nucleic Acid / Q000235:genetics*; D050794:STAT1 Transcription Factor / Q000378:metabolism*
D050794:STAT1 Transcription Factor; D012319:RNA Polymerase II
Bioinformatics. 2006 Jul 15;22(14):e141-9
16873464
PLoS Comput Biol. 2009 Jan;5(1):e1000269
19180174
Nat Biotechnol. 2009 Jan;27(1):66-75
19122651
Proc Natl Acad Sci U S A. 2003 Mar 18;100(6):3339-44
12626739
Nat Methods. 2007 Aug;4(8):651-7
17558387
BMC Bioinformatics. 2004 Mar 18;5:31
15113405
Genes Dev. 2002 Oct 15;16(20):2583-92
12381658
Nucleic Acids Res. 2009 Jul;37(12):e85
19451166