TMO: time and memory optimized algorithm applicable for more accurate alignment of trinucleotide repeat disorders associated genes

Abstract

In this study, time and memory optimized (TMO) algorithm is presented. Compared with SmithÀWaterman's algorithm, TMO is applicable for a more accurate detection of continuous insertion/deletions (indels) in genes' fragments, associated with disorders caused by overrepetition of a certain codon. The improvement comes from the tendency to pinpoint indels in the least preserved nucleotide pairs. All nucleotide pairs that occur less frequently are classified as less preserved and they are considered as mutated codons whose mid-nucleotides were deleted. Other benefit of the proposed algorithm is its general tendency to maximize the number of matching nucleotides included per alignment, regardless of any specific alignment metrics. Since the structure of the solution, when applying SmithÀWaterman, depends on the adjustment of the alignment parameters and, therefore, an incomplete (shortened) solution may be derived, our algorithm does not reject any of the consistent matching nucleotides that can be included in the final solution. In terms of computational aspects, our algorithm runs faster than SmithÀWaterman for very similar DNA and requires less memory than the most memory efficient dynamic programming algorithms. The speed up comes from the reduced number of nucleotide comparisons that have to be performed, without having to imperil the completeness of the solution. Due to the fact that four integers (16 Bytes) are required for tracking matching fragment, regardless its length, our algorithm requires less memory than Huang's algorithm.

A novel method for predicting RNA-interacting residues in proteins using a combination of feature-based and sequence template-based methods

“…The evolutionary information feature. The evolutionary information has been proved effective in RNAinteracting protein residues prediction and other bioinformatics problems [35]. PSSM is a common representation for evolutionary information and Kumar et al [23] have shown its effectiveness to improve the prediction performance.…”