OSCAR:: One-class SVM for accurate recognition of cis-elements
OSCAR:: One-class SVM for accurate recognition of cis-elements
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DOI：
10.1093/bioinformatics/btm473
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发表时间：
2007-11-01
期刊：
影响因子：
5.8
通讯作者：
Zhang, Xuegong
中科院分区：
文献类型：
--
作者：
Jiang, Bo;Zhang, Michael Q.;Zhang, Xuegong
动机：识别已知转录因子的潜在结合位点的传统方法仍然遭受大量错误预测。它们大多以位置特异性的方式使用序列信息，而忽略了隐藏在近端启动子区域中的其他类型的信息。然而，最近的生物学和计算研究表明，不仅存在位置偏好的结合，但也转录factors.Results之间的相关性：在这篇文章中，我们提出了一种新的方法，OSCAR，它利用一类SVM算法，并结合了多个因素，以帮助识别转录因子结合位点。使用合成和真实的数据，我们发现，我们的方法优于现有的算法，特别是在高灵敏度区域。我们的方法的性能可以进一步提高，考虑到结合事件的位置偏好。通过对实验验证的加塔和HNF转录因子家族结合位点的测试，我们表明我们的算法可以准确地推断出真正的共现模体对，并且通过考虑相关模体的共现，我们不仅过滤了错误的预测，而且提高了灵敏度。
Motivation: 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.