Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: A scoring matrix approach to detecting miRNA target sites. | Algorithms for Molecular Biology BioMed Central Open Access A scoring matrix approach to detecting miRNA target sites Simon Moxon Vincent Moulton and Jan T Kim Address School of Computing Sciences University of East Anglia Norwich NR4 7TJ UK Email Simon Moxon - simonm@ Vincent Moulton - Jan T Kim - jtk@ Corresponding author Published 31 March 2008 Received 5 October 2007 Algorithms for Molecular Biology 2008 3 3 doi 1748-71 88-3-3 Accepted 31 March 2008 This article is available from http content 3 l 3 2008 Moxon et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Experimental identification of microRNA miRNA targets is a difficult and time consuming process. As a consequence several computational prediction methods have been devised in order to predict targets for follow up experimental validation. Current computational target prediction methods use only the miRNA sequence as input. With an increasing number of experimentally validated targets becoming available utilising this additional information in the search for further targets may help to improve the specificity of computational methods for target site prediction. Results We introduce a generic target prediction method the Stacking Binding Matrix SBM that uses both information about the miRNA as well as experimentally validated target sequences in the search for candidate target sequences. We demonstrate the utility of our method by applying it to both animal and plant data sets and compare it with miRanda a commonly used target prediction method. Conclusion We show that SBM can be applied to target prediction in both plants and animals and performs well in terms of .
