tailieunhanh - Báo cáo sinh học: "EXMOTIF: efficient structured motif extraction"

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: EXMOTIF: efficient structured motif extraction. | Algorithms for Molecular Biology BioMed Central Research EXMOTIF efficient structured motif extraction Yongqiang Zhang and Mohammed J Zaki Address Department of Computer Science Rensselaer Polytechnic Institute Troy New York 12180 USA Email Yongqiang Zhang - zhangy0@ Mohammed J Zaki - zaki@ Corresponding author Open Access Published 16 November 2006 Received 23 July 2006 J Accepted 16 November 2006 Algorithms for Molecular Biology 2006 1 21 doi 1748-7188-1-21 This article is available from http content 1 1 21 2006 Zhang and Zaki 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 Extracting motifs from sequences is a mainstay of bioinformatics. We look at the problem of mining structured motifs which allow variable length gaps between simple motif components. We propose an efficient algorithm called EXMOTIF that given some sequence s and a structured motif template extracts all frequent structured motifs that have quorum q. Potential applications of our method include the extraction of single composite regulatory binding sites in DNA sequences. Results EXMOTIF is efficient in terms of both time and space and is shown empirically to outperform RISO a state-of-the-art algorithm. It is also successful in finding potential single composite transcription factor binding sites. Conclusion EXMOTIF is a useful and efficient tool in discovering structured motifs especially in DNA sequences. The algorithm is available as open-source at http zaki software exMotif . Introduction Analyzing and interpreting sequence data is an important task in bioinformatics. One critical aspect of such interpretation is to extract important motifs patterns from sequences. The .