TAILIEUCHUNG - Báo cáo sinh học: "DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment"

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: DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment. | Algorithms for Molecular Biology BioMed Central Research DIALIGN-TX greedy and progressive approaches for segment-based multiple sequence alignment Amarendran R Subramanian 1 Michael Kaufmann1 and Burkhard Morgenstern2 Open Access Address University of Tubingen Wilhelm-Schickard-Institut fur Informatik Sand 13 72076 Tubingen Germany and 2University of Gottingen Institute of Microbiology and Genetics Goldschmidtstr. 1 37077 Gottingen Germany Email Amarendran R Subramanian - subraman@ Michael Kaufmann - mk@ Burkhard Morgenstern - bmorgen@ Corresponding author Published 27 May 2008 Received 25 March 2008 Algorithms for Molecular Biology 2008 3 6 doi 1748-7188-3-6 Accepted 27 May 2008 This article is available from http content 3 1 6 2008 Subramanian 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 DIALIGN-T is a reimplementation of the multiple-alignment program DIALIGN. Due to several algorithmic improvements it produces significantly better alignments on locally and globally related sequence sets than previous versions of DIALIGN. However like the original implementation of the program DIALIGN-T uses a a straight-forward greedy approach to assemble multiple alignments from local pairwise sequence similarities. Such greedy approaches may be vulnerable to spurious random similarities and can therefore lead to suboptimal results. In this paper we present DIALIGN-TX a substantial improvement of DIALIGN-T that combines our previous greedy algorithm with a progressive alignment approach. Results Our new heuristic produces significantly better alignments especially on globally related sequences without increasing the CPU time

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