Li Lang, Cheng Alfred S L, Jin Victor X, Paik Henry H, Fan Meiyun, Li Xiaoman, Zhang Wei, Robarge Jason, Balch Curtis, Davuluri Ramana V, Kim Sun, Huang Tim H-M, Nephew Kenneth P
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine Indianapolis, IN 47405, USA.
Bioinformatics. 2006 Sep 15;22(18):2210-6. doi: 10.1093/bioinformatics/btl329. Epub 2006 Jun 29.
To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-alpha (ERalpha), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs.
Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERalpha target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERalpha target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods.
The software is available on request from the authors.
Supplementary data are available at Bioinformatics online.
为了检测和选择能够区分由雌激素受体α（ERα）直接调控的基因的转录因子结合位点（TFBS）模式，我们开发了一种基于创新混合模型的判别分析方法来识别有序的TFBS对。
从生物学角度来看，我们提出的新算法清楚地表明TFBS并非随机分布在ERα靶基因启动子内（P值<0.001）。上调的靶基因显著（P值<0.01）拥有TFBS对，如（DBP，MYC）、（DBP，MYC/MAX异源二聚体）、（DBP，USF2）和（DBP，肌细胞生成素）；而下调的ERα靶基因显著（P值<0.01）拥有TFBS对，例如（DBP，c-ETS1-68）、（DBP，USF2）和（DBP，肌细胞生成素）。从统计学角度而言，我们提出的基于混合模型的判别分析能够同时进行TFBS模式识别、TFBS模式选择以及靶基因类别预测；当前方法无法实现这种综合能力。
可向作者索取该软件。
补充数据可在《生物信息学》在线获取。
