scholarly journals FastqCleaner: an interactive Bioconductor application for quality-control, filtering and trimming of FASTQ files

2018 ◽  
Author(s):  
Leandro Gabriel Roser ◽  
Fernán Agüero ◽  
Daniel Oscar Sánchez
Keyword(s):  
Quality Control ◽  
Data Analysis ◽  
The Novel ◽  
Web Environment ◽  
Ngs Data Analysis ◽  
Novel Concept ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Analysis Platform

AbstractBackgroundExploration and processing of FASTQ files are the first steps in state-of-the-art data analysis workflows of Next Generation Sequencing (NGS) platforms. The large amount of data generated by these technologies has put a challenge in terms of rapid analysis and visualization of sequencing information. Recent integration of the R data analysis platform with web visual frameworks has stimulated the development of user-friendly, powerful, and dynamic NGS data analysis applications.ResultsThis paper presents FastqCleaner, a Bioconductor visual application for both quality-control (QC) and pre-processing of FASTQ files. The interface shows diagnostic information for the input and output data and allows to select a series of filtering and trimming operations in an interactive framework. FastqCleaner combines the technology of Bioconductor for NGS data analysis with the data visualization advantages of a web environment.ConclusionsFastqCleaner is an user-friendly, offline-capable tool that enables access to advanced Bioconductor infrastructure. The novel concept of a Bioconductor interactive application that can be used without the need for programming skills, makes FastqCleaner a valuable resource for NGS data analysis.

scholarly journals A Microservice-Based Big Data Analysis Platform for Online Educational Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Modular Design ◽  
Science Research ◽  
Big Data Analysis ◽  
Research Field ◽  
Traditional Work ◽  
Educational Applications ◽  
Analysis Platform

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

scholarly journals BDAP: A Big Data Analysis Platform Based On Spark

2018 ◽  
Vol 1060 ◽  
pp. 012023
Author(s):  
Zhixiang Wang ◽  
Yao Bu ◽  
Demeng Bai ◽  
Bin Wu ◽  
Jiafeng Qin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Analysis Platform
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