Busch Anke, Richter Andreas S, Backofen Rolf
Bioinformatics Group, Albert-Ludwigs-University Freiburg, Georges-Koehler-Allee 106, Freiburg D-79110, Germany.
Bioinformatics. 2008 Dec 15;24(24):2849-56. doi: 10.1093/bioinformatics/btn544. Epub 2008 Oct 21.
During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches.
We introduce INTARNA, a new general and fast approach to the prediction of RNA-RNA interactions incorporating accessibility of target sites as well as the existence of a user-definable seed. We successfully applied INTARNA to the prediction of bacterial sRNA targets and determined the exact locations of the interactions with a higher accuracy than competing programs.
在过去几年中，细菌中发现了几种新的小调控RNA（sRNA）。它们中的大多数通过与靶mRNA碱基配对发挥转录后调控作用，导致翻译抑制或激活，或mRNA降解。已经鉴定出许多sRNA，但经过实验验证的靶标的数量要少得多。因此，对计算靶标预测的需求很大。许多现有的靶标预测程序忽略了靶位点的可及性和种子序列的存在，而其他方法要么专门针对某些类型的RNA，要么对于全基因组搜索来说太慢。
我们引入了INTARNA，这是一种新的通用且快速的预测RNA-RNA相互作用的方法，该方法纳入了靶位点的可及性以及用户可定义的种子序列的存在。我们成功地将INTARNA应用于细菌sRNA靶标的预测，并以比竞争程序更高的准确性确定了相互作用的确切位置。
