Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA.
Bioinformatics. 2013 Sep 15;29(18):2261-8. doi: 10.1093/bioinformatics/btt397. Epub 2013 Jul 10.
We present an integrated toolkit, BoBro2.0, for prediction and analysis of cis-regulatory motifs. This toolkit can (i) reliably identify statistically significant cis-regulatory motifs at a genome scale; (ii) accurately scan for all motif instances of a query motif in specified genomic regions using a novel method for P-value estimation; (iii) provide highly reliable comparisons and clustering of identified motifs, which takes into consideration the weak signals from the flanking regions of the motifs; and (iv) analyze co-occurring motifs in the regulatory regions.
We have carried out systematic comparisons between motif predictions using BoBro2.0 and the MEME package. The comparison results on Escherichia coli K12 genome and the human genome show that BoBro2.0 can identify the statistically significant motifs at a genome scale more efficiently, identify motif instances more accurately and get more reliable motif clusters than MEME. In addition, BoBro2.0 provides correlational analyses among the identified motifs to facilitate the inference of joint regulation relationships of transcription factors.
The source code of the program is freely available for noncommercial uses at http://code.google.com/p/bobro/.
Supplementary data are available at Bioinformatics online.
我们提出了一个集成的工具包 BoBro2.0，用于预测和分析顺式调控基序。这个工具包可以 (i) 在基因组范围内可靠地识别具有统计学意义的顺式调控基序；(ii) 使用一种新的 P 值估计方法，准确地扫描指定基因组区域中查询基序的所有基序实例；(iii) 提供高度可靠的比较和聚类已识别的基序，同时考虑基序侧翼区域的弱信号；以及 (iv) 分析调控区域中共同出现的基序。
我们已经对 BoBro2.0 和 MEME 包进行了基序预测的系统比较。对大肠杆菌 K12 基因组和人类基因组的比较结果表明，BoBro2.0 可以更有效地在基因组范围内识别具有统计学意义的基序，更准确地识别基序实例，并获得比 MEME 更可靠的基序聚类。此外，BoBro2.0 还提供了已识别基序之间的相关分析，以促进转录因子联合调控关系的推断。
程序的源代码可在非商业用途免费使用，网址为 http://code.google.com/p/bobro/。
补充数据可在 Bioinformatics 在线获取。
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