Improving the performance of Smith–Waterman sequence algorithm on GPU using shared memory for biological protein sequences

Prasad, D. Venkata Vara; Jaganathan, Suresh

doi:10.1007/s10586-018-2421-7

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…For detecting orthologs of SV proteins in AIV proteins, four different scores were calculated between each of the 294 SV proteins and each of the 74 AIV proteins. Primary structure similarity score: Smith-Waterman sequence alignment [ 45 ] was performed on all 21756 sequence pairs without any opening gap penalty but with −2 penalty score for the extension gap. Blocks substitution matrix 62 (BLOSUM62) was considered as scoring matrix.…”

Section: Methodsmentioning

confidence: 99%

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SARS-CoV-2-human protein-protein interaction network

Khorsand

Savadi

Naghibzadeh

2020

Informatics in Medicine Unlocked

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

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Section: Methodsmentioning

confidence: 99%

“…Primary structure similarity score: Smith-Waterman sequence alignment [ 45 ] was performed on all 21756 sequence pairs without any opening gap penalty but with −2 penalty score for the extension gap. Blocks substitution matrix 62 (BLOSUM62) was considered as scoring matrix.…”

Section: Methodsmentioning

confidence: 99%

SARS-CoV-2-human protein-protein interaction network

Khorsand

Savadi

Naghibzadeh

2020

Informatics in Medicine Unlocked

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“…They produce huge amounts of genetic data that require fast analysis in various phases of molecular profiling, medical diagnostics, and treatment of patients that suffer from serious diseases. However, although very useful, these methods additionally increase the huge gap between the number of known genetic sequences (DNA sequences), protein sequences [38,61,79] (which are encoded by genes in DNA, see Fig. 1), and 3D protein structures [36,52].…”

Section: Introductionmentioning

confidence: 99%

Spark-IDPP: high-throughput and scalable prediction of intrinsically disordered protein regions with Spark clusters on the Cloud

2018

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Intrinsically disorder proteins (IDPs) constitute a significant part of proteins that exist and act in cells of living organisms. IDPs play key roles in central cellular processes and some of them are closely related to various human diseases, like cancer or neurodegenerative disorders. Identification of IDPs and studying their structural characteristics have become an important part of structural bioinformatics and structural genomics. However, growing amount of genomic and protein sequences in public repositories pose a pressure on existing methods for identification of IDPs. Large volumes of protein amino acid sequences need to be analyzed in terms of propensity to form disordered regions, and this task requires novel tools and scalable platforms to cope with this big biological data challenge. In this paper, we show how the identification of disordered regions of 3D protein structures can be efficiently accelerated with the use of Apache Spark cluster established and scaled on the public Cloud. For this purpose, we propose Spark-based meta-predictor (Spark-IDPP), which enables efficient prediction of disordered regions of proteins on a large-scale. Results of our performance tests show that, for large data sets, our method achieves almost linear speedup, when scaling out the computations on the 32-node Spark cluster located in the Azure cloud. This proves that through appropriate partitioning of data and by increasing the degree of parallelism, we can significantly improve efficiency of IDP predictions. Additionally, by using several basic predictors, aggregating their ranks in various consensus modes, and filtering the final outcome with a dedicated fuzzy filter, the Spark-IDPP increases the quality of predictions.

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