Finding large deletions in genome sequences have become increasingly more useful in bioinformatics, such as in clinical research and diagnosis. Several partial alignment approaches based on the Smith-Waterman (SW) algorithm has been proposed for alignment with large gaps. However, in the literature, no detailed comparisons of these three SW-based methods were given in terms of the runtimes and errors in estimated position of the start of the deletion in the query sequences. Our comparative simulations show that BinaryPartialAlign has the lowest error and very high speed.

MOTIVATION: Gene therapy aims at using viral vectors for attaching helpful genetic code to target genes. Therefore, it is of great importance to develop methods that can discover significant patterns around viral integration sites. Canonical correlation analysis is an unsupervised statistical tool that is used to describe the relations between two related views of the same semantic object, which fits well for identifying such salient patterns. RESULTS: Proposed method is demonstrated on a sequence dataset obtained from a study on HIV-1 preferred integration regions. The subsequences on the left and right sides of the integration points are given to the method as the two views, and statistically significant relations are found between sequence-driven features derived from these two views, which suggest that the viral preference must be the factor responsible for this correlation. We found that there are significant correlations at x=5 indicating a palindromic behavior surrounding the viral integration site, which complies with the previously reported results. AVAILABILITY: Developed software tool is available at http://ce.istanbul.edu.tr/bioinformatics/hiv1/.