Cloud4Psi: cloud computing for 3D protein structure similarity searching

Mrozek, Dariusz; Małysiak-Mrozek, Bożena; Kłapciński, Artur

doi:10.1093/bioinformatics/btu389

Cited by 38 publications

(22 citation statements)

References 11 publications

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“…In structural alignment, [39] observed that performance degraded slightly after 8 mappers was increased to 30. Furthermore, this trend has also been observed on the MS Azure platform we have discussed for comparison -in scaling Cloud4Psi, also a structural alignment application [51], observed that horizontal scaling resulted in performance degradation as a result of an increase in nodes sharing a virtual disk, and that vertical scaling resulted in performance degradation as a result of increased CPU utilisation (due to more processes running per node).…”

Section: Discussionsupporting

confidence: 65%

“…A similar bioinformatics SaaS (Software-as-a-Service) for structural alignment of proteins, has been developed for the Microsoft Azure platform -Cloud4Psi developed by [51]. Their service utilises three newer algorithms that are implemented in the BioJava project, and which are derived from CE (jCE), and FATCAT (jFATCAT-rigid and jFATCAT-flexible) [60].…”

Section: Structural Alignmentmentioning

confidence: 99%

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The application of Hadoop in Structural Bioinformatics

Alnasir

Shanahan

2018

Preprint

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The paper reviews the use of the Hadoop platform in Structural Bioinformatics applications. Specifically, we review a number of implementations using Hadoop of high-throughput analyses, e.g. ligand-protein docking and structural alignment, and their scalability in comparison with other batch schedulers and MPI. 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. We do note there is some evidence that MPI 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 AWS) increases and approaches such as the Workflow Definition Language are taken up.

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Section: Discussionsupporting

confidence: 65%

Section: Structural Alignmentmentioning

confidence: 99%

The application of Hadoop in Structural Bioinformatics

Alnasir

Shanahan

2018

Preprint

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“…Many hot issues in various sub-fields of bioinformatics were also solved with the use of Big Data ecosystems and Cloud computing, e.g., mapping nextgeneration sequence data to the human genome and other reference genomes, for use in a variety of biological analyzes including SNP discovery, genotyping and personal genomics [65], sequence analysis and assembly [17,30,34,35,47,62], multiple alignments of DNA and RNA sequences [86,91], codon analysis with local MapReduce aggregations [63], NGS data analysis [8], phylogeny [24,48], proteomics [37], analysis of proteinligand binding sites [23], and others. Regarding the analysis of 3D protein structures, it is worth mentioning several works, including Hazelhurst et al [20] and Małysiak-Mrozek et al [46] devoted to exploration of various atomic interactions within protein structures, works of Che-Lun Hung and Yaw-Ling Lin [25], and Mrozek et al [51,53,[55][56][57], devoted to comparison and alignment of 3D protein structures, and cloud-based system for 3D protein structure modeling presented in [54]. However, none of the mentioned works was focused on prediction of disordered regions.…”

Section: Related Workmentioning

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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“…The second group of approaches consists of solutions that utilize farms of computers or virtual machines to perform protein structure comparison. Examples of such solutions are MAS4PSi [27], Cloud4Psi [28,34], and CloudPSR [32]. All of them rely on fold-based methods for 3D protein structure comparison.…”

Section: Scalable Solutions For 3d Protein Structure Similarity Searcmentioning

confidence: 99%

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

Mrozek

Suwała

2018

Knowl Inf Syst

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Efficient computational solutions for identification of protein functions or finding structural homologs of proteins gain importance in the era of structural genomics and in the face of growing volumes of biological data. Structural alignments, which underlie these two processes, take a lot of time to complete, especially when performed for large collections of 3D protein structures. Fortunately, structural alignments can be carried out on well-separable and independent subsets of the whole macromolecular data repository, which perfectly fits the MapReduce processing paradigm of bringing computations to data. In this paper, we show how the protein function identification and finding structural homologs can be efficiently accelerated with the use of the MapReduce procedure executed on Hadoop cluster established in a virtualized compute environment or a private cloud. For this purpose, we propose Map-only processing pattern of the MapReduce procedure, which is formally defined in this paper. The solution that we show joins advantages of performing computations in small virtualized compute environments with large-scale computations in public clouds, thus allowing to perform structural alignments for a number of usage scenarios, including comparison of pairs of 3D protein structures during evaluation of predicted protein models, one-to-many comparisons while identifying possible functions of the given structure, or all-to-all alignments while investigating the divergence between known protein structures and classifying proteins by their fold. In this paper, we also present results of performance tests when scaling up nodes of the Hadoop cluster and increasing the degree of parallelism with the intention of improving efficiency of the computations. Keywords Bioinformatics • Big data • Proteins • Scalable computations and MapReduce • 3D protein structures • Cloud computing 148 D. Mrozek et al.

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Cloud4Psi: cloud computing for 3D protein structure similarity searching

Cited by 38 publications

References 11 publications

The application of Hadoop in Structural Bioinformatics

The application of Hadoop in Structural Bioinformatics

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

High-throughput and scalable protein function identification with Hadoop and Map-only pattern of the MapReduce processing model

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