OSCAR: one-class SVM for accurate recognition of cis-elements.
BACKGROUND
Traditional methods to identify potential binding sites of known transcription factors still suffer from large number of false predictions. They mostly use sequence information in a position-specific manner and neglect other types of information hidden in the proximal promoter regions. Recent biological and computational researches, however, suggest that there exist not only locational preferences of binding, but also correlations between transcription factors.


RESULTS
In this article, we propose a novel approach, OSCAR, which utilizes one-class SVM algorithms, and incorporates multiple factors to aid the recognition of transcription factor binding sites. Using both synthetic and real data, we find that our method outperforms existing algorithms, especially in the high sensitivity region. The performance of our method can be further improved by taking into account locational preference of binding events. By testing on experimentally-verified binding sites of GATA and HNF transcription factor families, we show that our algorithm can infer the true co-occurring motif pairs accurately, and by considering the co-occurrences of correlated motifs, we not only filter out false predictions, but also increase the sensitivity.


BACKGROUND
An online server based on OSCAR is available at http://bioinfo.au.tsinghua.edu.cn/oscar.
Jiang
Bo
B
MOE Key Laboratory of Bioinformatics, Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China.
Zhang
Michael Q
MQ
Zhang
Xuegong
X
D000465:Algorithms; D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D001483:Base Sequence; D001665:Binding Sites; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011401:Promoter Regions, Genetic / Q000235:genetics*; D011485:Protein Binding; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors