Software Implementation of Smith-Waterman Algorithm in FPGA

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1 Software Implementation of Smith-Waterman lgorithm in FP NUR FRH IN SLIMN, NUR DLILH HMD SBRI, SYED BDUL MULIB L JUNID, ZULKIFLI BD MJID, BDUL KRIMI HLIM Faculty of Electrical Engineering Universiti eknologi MR 445, Shah lam MLYSI ain_saliman@yahoo.com bstract: - his paper proposed a software version of Smith-Waterman (SW) algorithm using Field Programmable ate rray (FP). he implementation was carried out using low cost EP4E115F297 FP. hirty two tests were conducted with average runtime for each cell recoded from.3492ms to.45ms per cell. herefore, the software implementation has direct dependencies over cell runtime due the iterative computational method used. Key-Words: - Bioinformatics, Sequence lignment, Local lignment, Smith-Waterman algorithm, FP 1 Introduction significant part of bioinformatics is the analysis of pair or more sequences. hus, most common system in bioinformatics that used for finding the similarity region based on comparing method is called as sequence alignment system. hrough this similarity region, it may be a consequence to find the degree of homolog of functional, structural or evolutionary relationship between sequences. In sequence alignment system, there are two available methods which are global and local alignment as reported in [1]. global method is aiming from end-to-end of the sequences and there are two methods functional as global method; Dot Plot and Needleman-Wunsch (NW) algorithm [2]. Meanwhile, another alignment also carried two methods which are local alignment method. he methods are known as an exact method like SW algorithm [3] and heuristic based approximate method like FS [4] and BLS [5]. In local alignment method, both methods are attempted to identify the most similar region between pair or more sequences. With the increasing volume of the Deoxyribonucleic cid (DN) databases, it causes the increasing of comparing runtime between two or more DN sequences. s an alternative, the used of FS and BLS as faster heuristic algorithm have been proposed. However, both algorithms cannot guarantee finding the optimal alignment after increased the speed due to the sensitivity issue. herefore, in order to achieve both target (speed and sensitivity); it is necessary to accelerate or optimize the SW algorithm. Various approaches have been made on accelerating the available method and some of the acceleration were implemented either the whole algorithm or some part in a hardware [6][7][8] [9][1][11][12]. In [7], L. Hasan et al presents a raphics Processing Units (PUs) accelerated S-W implementation for protein sequence alignment. he paper proposed a new sequence database organization and several optimizations to reduce the number of memory accesses. he implementation achieved a performance of 21.4 UPS and 1.13 times better than the implementation on an NVIDI X 275 graphics card. In [1], Z. Nawaz et al implemented two Recursive Variable Expansion (RVE) based techniques, which are proved to give better speedup at 2.29 times faster than any dataflow approach at the 2.82 times extra area. he paper is organized as follows: Section 1gives an introduction of problem and solution of this field. Section 2 gives a brief description of SW algorithm. Section 3 discusses the implementation of software version in FP. Section 4 results and some discussion on the significance in comparison. Section 5 provides a brief conclusion. ISBN:

2 2 he Smith-Waterman lgorithm In 1981, Smith and Waterman introduce a local method that called as the SW algorithm [3] which is commonly used to identify the optimal regions of similarity. his subsection introduces the SW algorithm, as well as the necessary description of SW algorithm process. Ns1 Nq1 Nq2 Nq3 Nq4 Nqm Ns2 2.1 SW Description We defined H i, j as a cell matrix for dynamic matrix where the calculation for the H i, j using the following equation: Ns3 Ns4 Nsn (1) Fig.2: Initialization Step hen, S i, j is denoted as similarity score between two sequences (query and subject). Meanwhile, d as penalty gap for a mismatch DN bases. he whole SW algorithm is divided into three following steps as shown in Figure 1. SW lgorithm In fill matrix step, each cell of the H i, j matrix is calculated according to the equation (1), where i and j of the H i, j matrix is assumed to be column and row number. In order to find the H i, j cell, it is important to know the H i, j matrix position as shown in Figure 3. Nq1 Nq2 Nq3 Nq4 Nqm Initialization Fill Matrix race Back Fig.1: SW algorithm flow process Ns1 Ns2 In dynamic matrix H, the local alignment method is to create H i, j matrix with N q +1 and N s +1 for query columns and subject rows. hus, in initialization step assumes there is no gap penalty by initialized the first row and first column with zeroes (H, j = and H i, =, for all i and j) as following in Figure 2. Normally those columns and rows can be considered as column and row. Ns3 Ns4 Nsn Hi-1,j-1 Hi-1,j Hi,j-1 Hi,j Fig.3: H i, j Matrix position ISBN:

3 ssume that, the pair sequences for SW algorithm as follows, (query sequence) and (subject sequence) as shown in Figure 4. hus, it gives that m and n length of SW sequences as 7 and 6. In trace back step, it starts from the highest score and continues until the minimum score as shown in Figure Fig.4: Initialization of Pair Sequences Fig.6: race Back Step simple scoring is assumed as followed: Based on this information, the position at cell H 1, 1 can be calculated. By comparing first base from query and first base in subject sequence, we find out that both bases are not equal. hus, the score of similarity S 1, 1 = -1 and the penalty gap d = 2. hen, the score of cell H 1, 1 = and the complete fill matrix step illustrated as in Figure Fig.5: Fill Matrix Step 3 Hardware Platforms here are various hardware platforms have been proposed to be used on accelerating the sequence alignment methods such as entral Processing Unit (PU), Field Programmable ate rray (FP) and raphic Processing Unit (PU). Following is a brief discussion on software implementation on FP platform used in this study. 3.1 Software implementation In order to build the SW algorithm software, there are several parameters required. he parameters that cover in this implementation are the input of H i, j matrix position, direction of gap and output of H i, j matrix score. he input parameter of H i, j matrix position is to control the neighboring cell for the matrix position of H i-1, j-1, H i-1, j and H i, j-1 (the position of diagonal, left and upper). hen, the implementation of direction gap (gap penalty) is to conduct the direction of gap which is defined as affine gap. he output parameter of SW algorithm is carried out the final score of the cell. ypically, this software version of SW algorithm involves iterative calculation of cells in a scoring matrix. he scheme that used to compute the score of a H i, j, H i-1, j-1, H i, j-1 and H i-1, j is determined as following: ISBN:

4 he upper left of H i, j, left of H i, j and upper of H i, j are the cell that represent the cell of H i-1, j-1, H i, j- 1 and H i-1, j. hose cells is represent as three neighboring cell. It is important for finding the score of cell H i, j as respectively shows in Figure 3. In addition, Figure 7 shows the example architecture for SW software version. he implementation of SW software version shows that the architecture is for comparing between two sequences. 3.2 FP Implementation for Software version he software was developed using language and targeted to FP platform EP4E115F297. Furthermore, the benchmark was carried out using Nios II Eclipse tool. he software version acceleration involves a several steps with the first priority is to make sure the code work correctly. It follows by determination of query and subject length (n and m). hen, the size of the dynamic matrix H needs to set up at the size of (n+1) x (m+1). he initialization step will take up with inserting initial value or zero to the first row and column of H i, j matrix. he remaining cell score were calculated iteratively using Equation (1). In addition, the integer matrix function is useful for SW software version since it can keep track the highest score of H i, j cell. Finally, the output will give the highest score. 12x 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 It shows that the increasing numbers of cells are proportional to the complete runtime of the matrix. Meanwhile, the runtime is decreasing at the rate of 3.67 to 1.7 at the same time. 4 Result and Discussion he software version was tested by aligning a pair DN sequences with identical length of m columns and n rows. he identical lengths are tested at ranging from 1 to 64 base-pair. he result for SW software implementation is shown in able I. able I: omputation ime of SW implementation Number of Number of Base-Pair ells Software Version Runtime (ms) 2x x x x x raph 1: ime Versus Number of ells ISBN:

5 he runtime of cell recorded at average of.3492ms to.45ms per cell as shown in raph 1. he maximum runtime per cell recorded during 2x2 base-pair test while the minimum runtime was recorded in 5x5 base-pair test. 5 onclusion his paper presented a SW software version in FP. he implementation was carried out using low cost EP4E115F297 FP. hirty two tests with ranging from 2x2 base-pair until 64x64 basepair were conducted to measure software version runtime. It shows that, the runtime is reducing from 3.67 to 1.7 times less during the test which is viceversa with number of cells. Meanwhile, the average runtime for each cell is ranging from.3492 to.45 per cell. herefore, we can conclude that the runtime is depending on the iterative computational method used. cknowledgment he authors would like to acknowledge the Ministry of Science, echnology and Innovation (MOSI) and Faculty of Electrical Engineering, Universiti eknologi MR (UiM) for providing financial support under Science Fund rant (1-RMI/SF 16/6/2 (17/212)) and laboratory facilities. References: [1] L. Hasan, Z. l-rs, and S. Vassiliadis, Hardware acceleration of sequence alignment algorithms-an overview, 27 International onference on Design & echnology of Integrated Systems in Nanoscale Era, pp , 27. [2] S. B. Needleman and. D. Wunsch, general method applicable to the search for similarities in the amino acid sequence of two proteins., Journal of molecular biology, vol. 48, no. 3, pp , Mar [3] M. S. Waterman, Identification of ommon Molecular Subsequences Identification of ommon Molecular Subsequences, pp , [5] S. ltschul, W. ish, and W. Miller, Basic local alignment search tool, Journal of molecular biology, vol. 215, no. 3 (199): [6] E. F. D. O. Sandes,.. M.. De Melo, and S. Member, Retrieving Smith-Waterman lignments with Optimizations for Megabase Biological Sequences Using PU, 213, vol. 24, no. 5, pp [7] L. Hasan, M. Kentie, and Z. l-rs, PUaccelerated protein sequence alignment., onference proceedings : nnual International onference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. onference, vol. 211, pp , Jan [8] D. Honbo,. grawal, and. houdhary, Efficient Pairwise Statistical Significance Estimation using FPs. [9]. K. Hudek and D.. Brown, FES: sensitive local alignment with multiple rates of evolution., IEEE/M transactions on computational biology and bioinformatics / IEEE, M, vol. 8, no. 3, pp , 211. [1] Z. Nawaz, K. Bertels, and H. Ekin Sumbul, Fast Smith-Waterman hardware implementation, 21 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1 4, pr. 21. [11] L. Hasan, Y. Khawaja, and. Bais, Systolic rray rchitecture for the Smith- Waterman lgorithm with High Performance ell Design., IDIS European onf. Data Mining, p. 8, 28. [12] S.. M. l Junid, N. Md ahir, Z. bd Majid, Z. Othman, and K. K. Mohd Shariff, Reducing memory complexity using data minimization technique on FP, in 212 International onference on omputer & Information Science (IIS), 212, pp [4] D. Lipman and W. Pearson, Rapid and sensitive protein similarity searches, Science, p. 7, ISBN:

6 ppendix Query Sequence Hi-1,j-1 Subject Sequence SEQMP + OMPROR Match/mismatch score Si,j upperleft LU left Opening/extension gap d + Hi-1,j upper OMPROR Opening/extension gap d + Hi,j-1 OMPROR Hi,j Fig. 7: SW software version architecture ISBN: