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Chen W, Yao C, Guo Y, Wang Y, Xue Z. pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP. BMC Bioinformatics 2020; 21:426. [PMID: 32993484 PMCID: PMC7526426 DOI: 10.1186/s12859-020-03757-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 09/16/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP. RESULTS Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2-5. CONCLUSIONS pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.
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Affiliation(s)
- Weiya Chen
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chun Yao
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yingzhong Guo
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yan Wang
- School of Life Science, Huazhong University of Science and Technology, Wuhan, China
| | - Zhidong Xue
- School of Software Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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Georgiev GD, Dodd KF, Chen BY. Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials. Algorithms Mol Biol 2020; 15:11. [PMID: 32489400 PMCID: PMC7247173 DOI: 10.1186/s13015-020-00168-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 04/15/2020] [Indexed: 11/10/2022] Open
Abstract
Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic efficiency and geometric precision are central to algorithmic design. To address these concerns, this paper presents pClay, the first algorithm to perform parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials as geometric solids. pClay was presented at the 2019 Workshop on Algorithms in Bioinformatics (WABI 2019) and is described in expanded detail here, especially with regard to the comparison of electrostatic isopotentials. Earlier methods have generally used parallelism to enhance computational throughput, pClay is the first algorithm to use parallelism to make arbitrarily high precision comparisons practical. It is also the first method to demonstrate that high precision comparisons of geometric solids can yield more precise structural inferences than algorithms that use existing standards of precision. One advantage of added precision is that statistical models can be trained with more accurate data. Using structural data from an existing method, a model of steric variations between binding cavities can overlook 53% of authentic steric influences on specificity, whereas a model trained with data from pClay overlooks none. Our results also demonstrate the parallel performance of pClay on both workstation CPUs and a 61-core Xeon Phi. While slower on one core, additional processor cores rapidly outpaced single core performance and existing methods. Based on these results, it is clear that pClay has applications in the automatic explanation of binding mechanisms and in the rational design of protein binding preferences.
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Affiliation(s)
- Georgi D. Georgiev
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
| | - Kevin F. Dodd
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
| | - Brian Y. Chen
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
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Mrozek D, Kwiendacz J, Malysiak-Mrozek B. Protein Construction-Based Data Partitioning Scheme for Alignment of Protein Macromolecular Structures Through Distributed Querying in Federated Databases. IEEE Trans Nanobioscience 2020; 19:102-116. [DOI: 10.1109/tnb.2019.2930494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment. Molecules 2019; 24:molecules24010179. [PMID: 30621295 PMCID: PMC6337464 DOI: 10.3390/molecules24010179] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 12/29/2018] [Accepted: 01/01/2019] [Indexed: 11/16/2022] Open
Abstract
Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.
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Alnasir JJ, Shanahan HP. The application of Hadoop in structural bioinformatics. Brief Bioinform 2018; 21:96-105. [PMID: 30462158 DOI: 10.1093/bib/bby106] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/20/2018] [Accepted: 10/05/2018] [Indexed: 11/13/2022] Open
Abstract
The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.
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Affiliation(s)
- Jamie J Alnasir
- Institute of Cancer Research, Old Brompton Road, London, United Kingdom
| | - Hugh P Shanahan
- Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom
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Hung CL, Chen WP, Hua GJ, Zheng H, Tsai SJJ, Lin YL. Cloud computing-based TagSNP selection algorithm for human genome data. Int J Mol Sci 2015; 16:1096-110. [PMID: 25569088 PMCID: PMC4307292 DOI: 10.3390/ijms16011096] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Accepted: 12/04/2014] [Indexed: 12/31/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.
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Affiliation(s)
- Che-Lun Hung
- Department of Computer Science and Communication Engineering, Providence University, Taichung 43301, Taiwan.
| | - Wen-Pei Chen
- Department of Applied Chemistry, Providence University, Taiwan 43301, Taiwan.
| | - Guan-Jie Hua
- Department of Computer Science, National Tsing Hua University, Hsinchu 30013, Taiwan.
| | - Huiru Zheng
- School of Computing and Mathematics, University of Ulster, Newtownabbey BT37 0QB, UK.
| | - Suh-Jen Jane Tsai
- Department of Applied Chemistry, Providence University, Taiwan 43301, Taiwan.
| | - Yaw-Ling Lin
- Department of Applied Chemistry, Providence University, Taiwan 43301, Taiwan.
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Mohammed EA, Far BH, Naugler C. Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends. BioData Min 2014; 7:22. [PMID: 25383096 PMCID: PMC4224309 DOI: 10.1186/1756-0381-7-22] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 10/18/2014] [Indexed: 12/23/2022] Open
Abstract
The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called "big data" challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. THE MAPREDUCE PROGRAMMING FRAMEWORK USES TWO TASKS COMMON IN FUNCTIONAL PROGRAMMING: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.
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Affiliation(s)
- Emad A Mohammed
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Behrouz H Far
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Pathology and Laboratory Medicine, University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada
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Hung CL, Hua GJ. Local alignment tool based on Hadoop framework and GPU architecture. BIOMED RESEARCH INTERNATIONAL 2014; 2014:541490. [PMID: 24955362 PMCID: PMC4052794 DOI: 10.1155/2014/541490] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2014] [Accepted: 04/14/2014] [Indexed: 11/17/2022]
Abstract
With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.
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Affiliation(s)
- Che-Lun Hung
- Department of Computer Science and Communication Engineering, Providence University, No. 200, Section 7, Taiwan Boulevard, Shalu District, Taichung 43301, Taiwan
| | - Guan-Jie Hua
- Department of Computer Science and Information Engineering, Providence University, No. 200, Section 7, Taiwan Boulevard, Shalu District, Taichung 43301, Taiwan
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Chang TH, Wu SL, Wang WJ, Horng JT, Chang CW. A novel approach for discovering condition-specific correlations of gene expressions within biological pathways by using cloud computing technology. BIOMED RESEARCH INTERNATIONAL 2014; 2014:763237. [PMID: 24579087 PMCID: PMC3919110 DOI: 10.1155/2014/763237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2013] [Revised: 11/18/2013] [Accepted: 12/15/2013] [Indexed: 11/18/2022]
Abstract
Microarrays are widely used to assess gene expressions. Most microarray studies focus primarily on identifying differential gene expressions between conditions (e.g., cancer versus normal cells), for discovering the major factors that cause diseases. Because previous studies have not identified the correlations of differential gene expression between conditions, crucial but abnormal regulations that cause diseases might have been disregarded. This paper proposes an approach for discovering the condition-specific correlations of gene expressions within biological pathways. Because analyzing gene expression correlations is time consuming, an Apache Hadoop cloud computing platform was implemented. Three microarray data sets of breast cancer were collected from the Gene Expression Omnibus, and pathway information from the Kyoto Encyclopedia of Genes and Genomes was applied for discovering meaningful biological correlations. The results showed that adopting the Hadoop platform considerably decreased the computation time. Several correlations of differential gene expressions were discovered between the relapse and nonrelapse breast cancer samples, and most of them were involved in cancer regulation and cancer-related pathways. The results showed that breast cancer recurrence might be highly associated with the abnormal regulations of these gene pairs, rather than with their individual expression levels. The proposed method was computationally efficient and reliable, and stable results were obtained when different data sets were used. The proposed method is effective in identifying meaningful biological regulation patterns between conditions.
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Affiliation(s)
- Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Shih-Lin Wu
- Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Wei-Jen Wang
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan
- Department of Biomedical Informatics, Asia University, Taichung 413, Taiwan
| | - Cheng-Wei Chang
- Department of Information Management, Hsing Wu University, New Taipei City 244, Taiwan
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Hung CL, Hua GJ. Cloud computing for protein-ligand binding site comparison. BIOMED RESEARCH INTERNATIONAL 2013; 2013:170356. [PMID: 23762824 PMCID: PMC3671236 DOI: 10.1155/2013/170356] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Accepted: 03/28/2013] [Indexed: 12/30/2022]
Abstract
The proteome-wide analysis of protein-ligand binding sites and their interactions with ligands is important in structure-based drug design and in understanding ligand cross reactivity and toxicity. The well-known and commonly used software, SMAP, has been designed for 3D ligand binding site comparison and similarity searching of a structural proteome. SMAP can also predict drug side effects and reassign existing drugs to new indications. However, the computing scale of SMAP is limited. We have developed a high availability, high performance system that expands the comparison scale of SMAP. This cloud computing service, called Cloud-PLBS, combines the SMAP and Hadoop frameworks and is deployed on a virtual cloud computing platform. To handle the vast amount of experimental data on protein-ligand binding site pairs, Cloud-PLBS exploits the MapReduce paradigm as a management and parallelizing tool. Cloud-PLBS provides a web portal and scalability through which biologists can address a wide range of computer-intensive questions in biology and drug discovery.
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Affiliation(s)
- Che-Lun Hung
- Department of Computer Science and Communication Engineering, Providence University, Taiwan Boulevard, Shalu District, Taichung 43301, Taiwan.
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