Summary Given an m‐length query string q, approximate dictionary lookup searches for strings in a string dictionary D at a distance of 1 to q under some distances. In biological retrieval systems, the distances in such queries are defined by evolution operations on genomes. We consider the approximate dictionary lookup with one genome evolution operation including reversal and transposition, which searches for strings in D that can be generated from q by one reversal or one transposition. When the length of the reversed substring is confined to a constant α>1, we propose an O(m)‐time approach which uses bits space, where the dictionary D has d strings with totally |D| symbols. If the lengths of the reversals are in a range [α,β], the time for query is , and the space is words for any constant ε, in which occ is the number of matched strings. For problems allowing one transposition, when the length of the transposition is fixed to α, the time for a dictionary lookup is , while using words. In the case that the two swapped substrings are of the same length, the time for answering the query is O(m), while the space is bits.

Gene order changes under rearrangement events such as inversions and transpositions have attracted increasing attention as a new type of data for phylogenetic analysis. Since these events are rare, they allow the reconstruction of evolutionary history far back in time. Many software have been developed for the inference of gene order phylogenies, including widely used maximum parsimony methods such as GRAPPA and MGR. However, these methods confronted great difficulties in dealing with emerging large nuclear genomes. In this study, we proposed three simple yet powerful maximum likelihood(ML) based methods for phylogenetic reconstruction by first encoding the gene orders into binary or multistate strings based on gene adjacency information presented in the given genomes and further converting these strings into molecular sequences. RAxML is at last used to compute the maximum likelihood phylogeny. We conducted extensive experiments using simulated datasets and found that although the multistate encoding is more complex and more time-consuming, it did not improve accuracy over the methods using simpler binary encodings. Among all methods tested in our experiments, MLBE is of the most accuracy in most cases and often returns phylogenies without errors. ML methods is also fast and in the most difficult case only takes up to three days to compute datasets with 40 genomes, making it very suitable for large scale analysis. We give three simple and robust phylogenetic reconstruction methods using different encodings based on maximum likelihood which has not been successfully applied for gene orderings before. Our development of these ML methods showed great potential in gene order analysis with respect to the high accuracy and stability, although formal mathematical and statistical analysis of these methods are much desired.

Because of the advent of high-throughput sequencing and the consequent reduction in cost of sequencing, many organisms have been completely sequenced and most of their genes identified; homologies among these genes are also getting established. It thus has become possible to represent whole genomes as ordered lists of gene identifiers and to study the evolution of these entities through computational means, in systematics as well as in comparative genomics. As a result, gene order data (also known as genome rearrangement data) has attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis. Methods for reconstructing phylogeny from genome rearrangements include distance-based methods, MCMC methods and direct optimization methods. The latter, pioneered by Sankoff and extended in the software packages of GRAPPA and MGR, is the most accurate approach for inversion phylogeny. However, due to the difficulty of computing the transposition distance, this type of methods has not been applied to datasets where transposition is the only or dominant event. In this paper, we present a heuristic transposition median solver and extend GRAPPA to handle transpositions. Our extensive testing using simulated datasets shows that this method (GRAPPA-TP) is very accurate in terms of ancestor genome inference and phylogenetic reconstruction. It also suggests that model match is critical in phylogenetic analysis, and a fast and accurate method for transposition distance computation is still very important. The new GRAPPA-TP is available from phylo.cse.sc.edu.

Simulation environments and approaches for evaluating real-time of IP traceback in different network scenarios and attacking patterns are very important. A comparison among some of the most promising PPM (Probabilistic Packet Marking) schemes is presented with several metrics, including the received packet number required for reconstructing the attacking path, computation complexity and false positive etc. We constructe a simulation environment via extending ns2, setting attacking topology and traffic, which can be used to evaluate and compare the effectiveness of different PPM schemes. The simulation approach also can be used to test the performing effects of different PPM schemes in large-scale DDoS attacks. Based on the simulation and evaluation results, several improvable aspects of PPM are proposed, which can increase real-time of IP traceback efficiently.