Acceleration of Biological Sequence Alignment using Recursive Variable Expansion

A Novel Approach for Accelerating the Smith-Waterman Algorithm using Recursive Variable Expansion

In this paper we implement in hardware, a novel approach for accelerating the S-W algorithm using Recursive Variable Expansion (RVE) technique, which enhances inherent parallelism and exposes extra parallelism as compared to any other technique. The results demonstrate that applying recursive variable expansion technique speeds up the performance by a factor of 3.83, as compared to traditional ...

Optimal pipeline design for Recursive Variable Expansion

This paper presents an automated flexible pipeline design algorithm for our unique acceleration technique called Recursive Variable Expansion. The preliminary experimental results on a kernel of real life application shows comparable performance to hand optimized implementation in reduced design time. This make it a good choice for generating high performance code for kernels, for which hand op...

A generalization of Profile Hidden Markov Model (PHMM) using one-by-one dependency between sequences

The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on th...

An Overview of Hardware-Based Acceleration of Biological Sequence Alignment

Efficient biological sequence (proteins or DNA) alignment is an important and challenging task in bioinformatics. It is similar to string matching in the context of biological data and is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment can provide valuable information for experimentation on the newly found sequences. It is indispensab...

On the Rational Recursive Sequence X_{N+1}=ƔX_{N-K}+(AX_N+BX_{N-K})⁄(CX_N-DX_{N-K})