Designing good or optimal seeds is a key factor for local homology search in bioinformatics.Continuous seeds have existed for nearly 20 years used by BLAST series programs.Recently,spaced seeds,which were introduced by PattenHunter program,were shown to be more sensitive and faster than continuous seeds under the same similarity level.However,there are 2 main disadvantages for space seeds:(i) It assumes that only matches and mismatches occur within seed alignments,but not insertions and deletions(indels);(ii) calculating optimal spaced seeds is an NP-hard problem.Introduction for indel seeds solved the first problem,but the second is getting much harder because of its higher exponential level.In this paper,we introduce an efficient way of designing good(even optimal) indel seeds under "indel overlap complexity" model,and it can be calculated in polynomial time.We calculate indel seeds from weight of 11 to 15.The result shows that indel seeds have higher sensitivi-ties than spaced ones and our algorithm finds good indel seeds very quickly.
A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node's location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization (Ext-MCL) is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.
