scholarly journals Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines

2019 ◽  
Author(s):  
Yasset Perez-Riverol ◽  
Pablo Moreno
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Data Science ◽  
Proteomics Data ◽  
Computational Proteomics ◽  
New Approach ◽  
Large Scale Data ◽  
Desktop Application ◽  
Key Steps ◽  
Scale Data

AbstractThe recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, the bioinformatics analysis is becoming an increasingly complex and convoluted process involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are targeted and design for single desktop application limiting the scalability and reproducibility of the data analysis. In this paper we overview the key steps of metabolomic and proteomics data processing including main tools and software use to perform the data analysis. We discuss the combination of software containers with workflows environments for large scale metabolomics and proteomics analysis. Finally, we introduced to the proteomics and metabolomics communities a new approach for reproducible and large-scale data analysis based on BioContainers and two of the most popular workflows environments: Galaxy and Nextflow.

scholarly journals Large-Scale Data Analysis Using Heuristic Methods

Informatica ◽  
2011 ◽  
Vol 22 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Gintautas Dzemyda ◽  
Leonidas Sakalauskas
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Heuristic Methods ◽  
Large Scale Data ◽  
Scale Data

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

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