Parallel processing cell score design of linear gap penalty smith-waterman algorithm

Junid, Syed Abdul Mutalib Al; Idros, Mohd Faizul Md; Razak, Abdul; Osman, Fairul Nazmie; Tahir, Nooritawati Md

doi:10.1109/cspa.2017.8064969

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the research got the results from the application of the Smith Waterman method. Then the conclusions and suggestions of this research are drawn [32,33].…”

Section: Conclusion Drawingmentioning

confidence: 99%

Application of the Smith Waterman and Jukes Cantor Algorithm in the Arrangement of the SARS CoV-2 Virus

Yulianto

Tafrikan

Amalia

et al. 2022

J. Nat. Scien. & Math. Res.

View full text Add to dashboard Cite

In early 2020, the world was shocked by an outbreak of a new pneumonia that started in Wuhan, Hubei Province, which then spread rapidly to more than 190 countries and territories. This outbreak was named coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of this disease has had a wide social and economic impact. There is still a lot of controversy surrounding this disease, including in the aspects of diagnosis, treatment, and prevention. Therefore, a study was carried out on studies related to COVID-19 that have been widely published since the beginning of 2020 until the end of March 2020.So to overcome this problem, the Smith Waterman Jukes Cantor Algorithm was made to align Covid19 by taking the a pair of DNA and RNA sequencesto align protein sequences. From this alignment, the percentage of identical and mutations will be known. The identical percentage in the genetic code will prove that although the symptoms caused by the disease are almost the same, the protein sequences are not necessarily the same. Based on the simulation results of the distance between sequences that produce a phylogenetic tree using the jukes cantor method, it was obtained that 4 groups of 26 sequences were divided into groups, namely, group 1 consists of 16 sequences, group 2 consists of 6 sequences, group 3 consists of 2 sequences, group 4 consists of 2 sequences. Based on these groups, it turns out that the China Wuhan sequence (sequence MT291826) is located in group 1 and other countries that are almost similar to the sequence in China Wuhan, namely the country of Timoe Leste with the sequence MT641766 also located in group 1.©2022 JNSMR UIN Walisongo. All rights reserved.

show abstract

“…After the research got the results from the application of the Smith Waterman method. Then the conclusions and suggestions of this research are drawn [32,33].…”

Section: Conclusion Drawingmentioning

confidence: 99%

Application of the Smith Waterman and Jukes Cantor Algorithm in the Arrangement of the SARS CoV-2 Virus

Yulianto

Tafrikan

Amalia

et al. 2022

J. Nat. Scien. & Math. Res.

View full text Add to dashboard Cite

show abstract

“…The second is the usage of either high-performance compute or consumer-grade GPUs as accelerators [20,21]. The last approach is the usage of FPGA-accelerators which, although they are not as fast as systems based on high-performance GPUs, tend to be more energy-efficient [22][23][24]. However, these systems have started to reach their limits in terms of performance, therefore, current research focuses on heterogeneous and cluster-based systems, e.g., multi-GPU clusters [25] or CPU-GPU co-systems [26].…”

Section: Related Workmentioning

confidence: 99%

Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

et al. 2020

View full text Add to dashboard Cite

This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m ∗ n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.

show abstract

“…At present, most of the research on alignment algorithms focus on specific problems ( Isa et al, 2014 ; Cattaneo et al, 2015 ; Chattopadhyay et al, 2015 ; Huo et al, 2016 ) or specific algorithm optimization ( Farrar, 2007 ; Houtgast et al, 2017 ; Junid et al, 2017 ) in the field of sequence similarity analysis, but less on the whole problem domain, so it is difficult to get an algorithm component library with a higher level of abstraction and suitable for the whole field of sequence similarity analysis. To some extent, this leads to the redundancy of the sequence alignment algorithm and the errors that may be caused by the artificial selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multiple Sequences Alignment Algorithm Generation via Components Assembly Under PAR Framework

Shi

2021

Front. Genet.

View full text Add to dashboard Cite

As a key algorithm in bioinformatics, sequence alignment algorithm is widely used in sequence similarity analysis and genome sequence database search. Existing research focuses mainly on the specific steps of the algorithm or is for specific problems, lack of high-level abstract domain algorithm framework. Multiple sequence alignment algorithms are more complex, redundant, and difficult to understand, and it is not easy for users to select the appropriate algorithm; some computing errors may occur. Based on our constructed pairwise sequence alignment algorithm component library and the convenient software platform PAR, a few expansion domain components are developed for multiple sequence alignment application domain, and specific multiple sequence alignment algorithm can be designed, and its corresponding program, i.e., C++/Java/Python program, can be generated efficiently and thus enables the improvement of the development efficiency of complex algorithms, as well as accuracy of sequence alignment calculation. A star alignment algorithm is designed and generated to demonstrate the development process.

show abstract

Parallel processing cell score design of linear gap penalty smith-waterman algorithm

Cited by 6 publications

References 14 publications

Application of the Smith Waterman and Jukes Cantor Algorithm in the Arrangement of the SARS CoV-2 Virus

Application of the Smith Waterman and Jukes Cantor Algorithm in the Arrangement of the SARS CoV-2 Virus

Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

Efficient Multiple Sequences Alignment Algorithm Generation via Components Assembly Under PAR Framework

Contact Info

Product

Resources

About