Accelerating Smith Waterman (SW) Algorithm on Altera Cyclone II Field Programmable Gate Array

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1 Accelerating Smith Waterman (SW) Algorithm on Altera yclone II Field Programmable Gate Array NUR DALILAH AHMAD SABRI, NUR FARAH AIN SALIMAN, SYED ABDUL MUALIB AL JUNID, ABDUL KARIMI HALIM Faculty Electrical Engineering Universiti eknologi Mara 445, Shah Alam MALAYSIA Abstract: - onstructing a technique to overcome the ever increasing volume of data in Bioinformatics database is an important problem in the system biology. When exploring the DNA sequences that may have a very large number of sequences because of the increasing human population every day, the algorithm turns out to be computationally costly. his paper work emphasis on accelerate in determining the runtime of the Smith Waterman matrix. his design of FPGA accelerated hardware offers a guaranteed path to searching calculation progress of the genomic database searching using custom instruction modules. Design construction which covers code development, compilation and simulation is carried out under experimental design for both of the proposed designs using Altera Quartus version 12 EDA tools and targeted to yclone EP4E115F297N FPGA at 5MHz clock cycle runtime. he final S-W algorithm is programmed in the FPGA as custom instruction through Nios II Eclipse. Key-Words: - Bioinformatics, Smith Waterman algorithm, FPGA, DNA, sequence alignment, local alignment 1 Introduction In Bioinformatics, the sequence alignment system may be defined as a comparison method for pair or more DNA sequences [1]. he purpose of DNA sequence alignment is to align and trace back path. DNA is the acronym for Deoxyribonucleic Acid [2]. Sequence alignment is aimed at identifying regions of similarity between DNA or protein sequences. It is similar to string matching in the context of activities in Bioinformatics. Moreover, the particular similarity may form valuable information for experimentation on the newly found sequences. here are two major alignment types in DNA sequence alignment system (global and local) [3]. his method is to ensure the identifying of the similar region. he idea of the global alignment method is by searching the similarity region from end to end of the sequences. Meanwhile, local alignment only searches the most similar region. Within both methods there are many algorithms that implement as global and local alignment such as dot plot, Needleman-Wunsch (N-W) algorithm [4], Smith-Waterman (S-W) algorithm [5], FASA [6] and BLAS [7]. Under global alignment, there are two algorithms conquer which are dot plot and N-W. Dot plot is known as the earliest of comparing the two sequences. In order to illustrate the dot plot, a dot will be placed whenever there are similar bases between the two sequences. he advantage of the dot plot is fast, however the sensitivity of the dot plot is limited. hen a few years later, N-W is introduced to improve the sensitivity of searching the optimal path. In contrast to the global alignment, local alignment consists of S-W, FASA and BLAS. S- W is an improvement from the N-W algorithm and the difference between both algorithms is the way to find the optimal path. Other methods available to perform and accelerate sequence alignments in local alignment activities are FASA, BLAS. In these algorithms are based on heuristic approach and provide efficient performance, however their alignment quality decreases dramatically. With the ever increasing collection of data in the Bioinformatics databases, the time taken to comparing the subject and query sequence in the databases also is always increasing. For this particular reason, to achieve both performance, which is increasing speed and an optimal alignment, it is necessary to accelerate the S-W in hardware. From the paperwork it is organized into five sections. his section is purposely for the overview of the general manner. In section two tells about an ISBN:

2 overview about dynamic programming. Meanwhile, the concept of using S-W algorithm issues will be detailed elaborated in section three. Section four is the result and discussion while the conclusion is in section five. 2 Dynamic programming Dynamic Programming is motivated as evolutionary algorithmic which solving problem by tackling the smallest problem first and subsequently the answer to tackle the larger ones [4]. he S-W algorithm is one of the methods to identify the query sequence and the database sequence based on dynamic programming. Although the local search (S-W) Algorithm is declining in aligning a long sequence, this method can find an optimal path guarantee between the two DNA or protein sequences [9]. he procedure for the custom instruction will be well elaborated in the following subsections: 2.1 Smith-Waterman Development Many approaches have been used to accelerate the S-W algorithm in hardware [11]. his project proposes an accelerator which is the custom instruction for S-W algorithm using the FPGA platform and eventually compares the performance with other acceleration S-W in hardwareimplementation. When computing the similarity for the S-W algorithm, a basic recurrent matrix element H (i, j) is used. he used of H (i,j) matrix is to keep the track of similarity degree. In order to calculate the sequence between two characters by compared in the S-W matrix, it can calculate through such equation (1). Where S (i,j) is called as a similarity score or known as substitution matrix. he function of the similarity score is to key in the match and mismatch score of the S-W algorithm. If there are matches bases between two bases, then it will choose the match score, otherwise it chooses the mismatch score. hen, d is the gap penalty of the S-W algorithm. he used of gap penalty is to ensure the direction of the gap. Ns1 Ns2 Ns3... Nsm Nq1 Nq2 Nq3 Fig.1: Size of matrix length... Nqn Figure 1 shows on how the H(i,j) matrix size with the length of M x N. M and N = the subject (Ns) and the query (Nq) length of the sequences to be aligned. his Figure is to ensure the position of the cell which is the arrangement of query sequence (Nq) and subject sequence (Ns) length. Moreover, Nqn is the number for query length while Nsm is the number of subject length of the sequence to be aligned.. In addition, the S-W algorithm can be divided into three sub modules such as Initialization module, Fill matrix module and race back module. (1) Initialization Mode Firstly, the matrix will be in the initialization module. In the initialization module, need to initialize the first row and column of matrix with (H (,) = H (I, ) = H (, j) = ). he occupied zero in the first row and the first column of the H (i, j) matrix process is called an initialization module as shown in following Figure 2. ISBN:

3 Nq1 Nq2 Nq3 Nq4 Nq race Back Module Lastly, in trace back step is to trace back the optimal alignment. his process starts by tracing the highest score and persists up to the cell until the scores declined into a predefined threshold. Ns1 Ns2 Ns3 H1,1 H2,1 H3,1 H1,2 H2,2 H3,2 H1,3 H2,3 H3,3 H1,4 H2,4 H3,4 H1,5 H2,5 H3,5 A Ns4 H4,1 H4,2 H4,3 H4,4 H4,5 Ns5 H5,1 H5,2 H5,3 H5,4 H5,5 Fig.2: Initialization mode G Fill Matrix Module After the initialization module, secondly is a score fill in the dynamic programming matrix. In this module the calculation of scoring can be determined through equation (1). For an example, calculate the score at position H (2,2). Since that the bases from each sequence are same, thus the S (2,2) will be 2. hen, the gap penalty will be -1. Finally, the maximum score that calculate based on equation (1) is 2. G A A Fig.4: race back stage 3 Result and Discussion With the increases number of databases, it slows the time taken. hus, this paper proposed a custom instruction of a S-W algorithm to accelerate the system. Since to scoring calculating is repeated in FPGA, it gives good routine for user to implement this method. hus, in order to construct the custom instruction of the S-W algorithm need to convert the only scoring equation into Verilog HDL writing. he used of this code is to program into FPGA platform. he advantage of custom instruction usage is sallow multiple input and output in a single clock. hus, it gives an accelerate S-W algorithm design than the software version. In addition, when compares the custom instruction with software version finds that the instruction set of software version is limited. 5 A Fig.3: Fill in matrix ISBN:

4 he way to come out with custom instruction, there are format need to be followed. he format for this custom instruction is following the combinational custom instruction format. his format will have two inputs with 32 bits for each input. However, for the purpose of the design is require to have 4 inputs. hus, in order to fit the inputs need to make some arrangement as in Figure 5. he input will recall an upper left, upper, left and gap input. hen, the arrangement of the output is by divided into two portions as gap and score. Upper Input (Hi,j-1) Gap Gap INPU OUPU Fig. 5 Left Input (Hi-1,j) Upper Left Input (Hi-1,j-1) Score (Hi,j) Lastly, after the custom instruction works well, the custom instruction needs to be added into the Nios II processor for calculating the S-W algorithm score. 3.1 esting Result able 1: ime onsumed Number of 1xSM (ms) Run/cell (ms) cells 2x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x As shown in able 1, the identical length that is being tested in this paper is at ranges 1 to 64 basepair. he able 1 also shows the performance of custom instruction through yclone 4 board. he time taken for each cell is average with.24 ms/cell. As reported in able 1, the table shows that the increasing for the full run time is reduced by 3.9 to hus, even though the length of the test is large, however the runtime has still remained average from.2 to.3. 4 onclusion he single cell module has a repetitive behavior in the calculation. Since it is duplicative, the behavior it has an excellent for FPGA based hardware acceleration applicant. he advantage of ustom Instruction is to accelerate in FPGA. hen, when comparing the custom with the software version based FPGA, the acceleration in hardware version gives a better performance. he performance time taken to complete the system is reduced proportionally from 3.9 till 1.65 References: [1] Y. Liu, K. Benkrid, A. Benkrid, and S. Kasap, An FPGA-Based Web Server for High Performance Biological Sequence Alignment, pp , 29. [2] G. ochrane,. E. ook, and E. Birney, he future of DNA sequence archiving, GigaScience, vol. 1, no. 1, p. 2, 212. [3]. D. S. Morony and E. D. Moreno, FPGA-Based Implementation and ISBN:

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