Scalable Cloud-Based Data Analysis Software Systems for Big Data from Next Generation Sequencing

Szczerba, Monika; Wiewiórka, Marek; Okoniewski, Michał; Rybiński, Henryk

doi:10.1007/978-3-319-26989-4_11

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…Other features of the ecosystem include indexing and search capabilities similar to DataMed [ 51 ] and a metalearning framework for ranking and selection of the best predictive algorithms [ 52 ]. Many of the bioinformatics software tools that we have discussed in the previous section have been successfully deployed in cloud environments and can be adapted to the commons ecosystem, including Apache Spark, a successor to Apache Hadoop and MapReduce for data analysis of Next Generation Sequencing Data [ 53 ]. In addition, the data transfer and sharing component of the cloud-based commons ecosystem can include features discussed for the Globus Research Data Management Platform [ 54 ].…”

Section: Developing a Cloud-based Digital Ecosystem For Biomedical Rementioning

confidence: 99%

Cloud computing applications for biomedical science: A perspective

Navale

Bourne

2018

PLoS Comput Biol

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Biomedical research has become a digital data–intensive endeavor, relying on secure and scalable computing, storage, and network infrastructure, which has traditionally been purchased, supported, and maintained locally. For certain types of biomedical applications, cloud computing has emerged as an alternative to locally maintained traditional computing approaches. Cloud computing offers users pay-as-you-go access to services such as hardware infrastructure, platforms, and software for solving common biomedical computational problems. Cloud computing services offer secure on-demand storage and analysis and are differentiated from traditional high-performance computing by their rapid availability and scalability of services. As such, cloud services are engineered to address big data problems and enhance the likelihood of data and analytics sharing, reproducibility, and reuse. Here, we provide an introductory perspective on cloud computing to help the reader determine its value to their own research.

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Section: Developing a Cloud-based Digital Ecosystem For Biomedical Rementioning

confidence: 99%

Cloud computing applications for biomedical science: A perspective

Navale

Bourne

2018

PLoS Comput Biol

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“…We have decided to further improve the performance by implementing this algorithm in a distributed framework using Spark. Apache Spark is a new framework for distributed parallel computation can speed up the iterative applications, such as machine learning, when the data is cached in memory [19]. In this research work, we attempt to address this issue by training multiple models using Spark, and combining their output results.…”

Section: Accelerating the Classifier -Sparkmentioning

confidence: 99%

A Distributed Tree-based Ensemble Learning Approach for Efficient Structure Prediction of Protein

Xavier¹,

Ramkumar²

2017

IJIES

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Knowledge of a protein's secondary structure, in turn, contributes to our understanding of the functions of the protein is vital to many aspects of living organisms such as those of enzymes, hormones, and structural material, etc. It also helps in designing new drugs for critical disease. In this paper, we have advocated a distributed approach to identify the Protein Secondary Structures using an ensemble method on protein primary sequences. The Ensemble based Random Forest algorithm has been adopted to build the three-way predictive model. Based on the amino acid features of each protein and decision tree parameters, the classification model allows us to assign protein structures as 'α helix', 'β sheet', or a coil. Also the proposed model is implemented in a distributed computing environment, SPARK. Experiments have been carried out using cross-validation tests on RS126 and CB513 benchmark datasets. Our results clearly confirm that ensemble approach in classifying protein secondary structures scores better accuracy with improved performance when it will be implemented in the distributed environment.

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“…Advanced big data analytics frameworks accelerate the storage and analysis of big omics data by facilitating the provision of scalable analytic infrastructures, such as the Hadoop Distributed File System (HDFS) for storage and the Spark Machine Learning libraries (MLlib) for analysis. 1 So as to cater advanced bio-data analytics, big data and cloud computing technologies need to be tightly integrated and applied in a uniform fashion. Cloud computing has been demonstrated to be reliably scalable for the analysis of genomic data over single machines, as well as clusters and public cloud infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Enabled Big Multi-Omics Data Analytics

Koppad

Annappa

Gkoutos

et al. 2021

Bioinform Biol Insights

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High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.

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Scalable Cloud-Based Data Analysis Software Systems for Big Data from Next Generation Sequencing

Cited by 6 publications

References 51 publications

Cloud computing applications for biomedical science: A perspective

Cloud computing applications for biomedical science: A perspective

A Distributed Tree-based Ensemble Learning Approach for Efficient Structure Prediction of Protein

Cloud Computing Enabled Big Multi-Omics Data Analytics

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