Curve matching is one instance of the fundamental correspondence problem. Our exible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves, and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive...

structure alignment

The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper, we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them and generates scores that are more effective in distinguishing true similarities from chance similarities. We evaluate our method and compare it to other known methods using the Gene Ontology (GO) knowledge resource as a benchmark. Various performance measures, including ROC analysis, indicate that the new model improves upon the state of the art. The program is available at biozon.org/ftp / and www.cs.technion.ac.il/∼itaish/lowcomp/. Key words: 1.

Various algorithms are in use in medical processes to improve the speed, sensitivity and accuracy of the computations and analyses involved in those experiments. The aim of this paper is to suggest three improvements, namely Multi Hit, Dropoff percentage and NCM-2 in the BLAST algorithm. BLAST (Basic Local Alignment Search Tool) is a popular tool used for determining the patterns in genomic sequences. As the data is increasing exponentially, the need for advanced and complex algorithms for improving the accuracy, speed and sensitivity of pattern discovery tools in bioinformatics is also increasing. First Improvement: The initialization of the word matches in a pairwise sequence alignment works either on single hit or two-hit algorithms. Instead, if we use a 3-hit or n-hit in general then the results improve in general and improve dramatically for some specific species and sequences. Second Improvement: BLAST is using a drop-off score to calculate the highest scoring pairs between two sequences. A change has been proposed to calculate the threshold score that determines the inclusion of the subsequence in the result. Instead of using a drop-off score, if we use a drop-off percentage, it gives better results for some sequences. Third Improvement: We propose an NCM-2 approach for normalizing BLAST values for simple regions. This approach is based upon the natural properties of the Amino acid sequences. The algorithms have been run on Linux ES platform with Compaq Presario 2GB RAM and compared to the original BLAST.

Abstract. We examine the distribution of the global maximum of an independent superadditive process with negative drift. We show that, under certain conditions, the distribution’s upper tail decays exponentially at a certain rate that can be characterized as the unique positive zero of some limiting logarithmic moment generating function. This limiting logarithmic moment generating function in some sense describes the asymptotic single step distribution of the process. The result extends in a natural way the corresponding result from the world of random walks. We apply our results to sequence alignments with gaps. Calculating p-values of optimal gapped alignment scores still is one of the most challenging mathematical problems in bioinformatics. Our results provide a better understanding of the tail of the optimal score’s distribution, especially on the level of large deviations. Although a mathematical satisfying description of the optimal score’s distribution is still far from reach, our results fit nicely into the common practice of statistical evaluation of optimal alignment results. 1.

Island method for estimating the statistical significance of profile-profile alignment scores

The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper, we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them and generates scores that are more effective in distinguishing true similarities from chance similarities. We evaluate our method and compare it to other known methods using the Gene Ontology (GO) knowledge resource as a benchmark. Various performance measures, including ROC analysis, indicate that the new model improves upon the state of the art. The program is available at biozon.org/ftp / and www.cs.technion.ac.il/∼itaish/lowcomp/.

We consider matching with shifts for Gibbsian sequences. We prove that the maximal overlap behaves as clog n, where c is explicitly identified in terms of the thermodynamic quantities (pressure) of the underlying potential. Our approach is based on the analysis of the first and second moment of the number of overlaps of a given size. We treat both the case of equal sequences (and nonzero shifts) and independent sequences. 1. Introduction. In