Articlegold2019

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Polyane Wercelens · Waldeyr Mendes Cordeiro da Silva · Fernanda Hondo · Klayton Rodrigues de Castro · Maria Emília M. T. Walter · Aletéia Araújo +2 more

11 citations0/0 files storedEvolutionary Bioinformatics

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Scientific Computing and Data Management Cloud Computing and Resource Management Distributed and Parallel Computing Systems

Abstract

Scientific workflows can be understood as arrangements of managed activities executed by different processing entities. It is a regular Bioinformatics approach applying workflows to solve problems in Molecular Biology, notably those related to sequence analyses. Due to the nature of the raw data and the in silico environment of Molecular Biology experiments, apart from the research subject, 2 practical and closely related problems have been studied: reproducibility and computational environment. When aiming to enhance the reproducibility of Bioinformatics experiments, various aspects should be considered. The reproducibility requirements comprise the data provenance, which enables the acquisition of knowledge about the trajectory of data over a defined workflow, the settings of the programs, and the entire computational environment. Cloud computing is a booming alternative that can provide this computational environment, hiding technical details, and delivering a more affordable, accessible, and configurable on-demand environment for researchers. Considering this specific scenario, we proposed a solution to improve the reproducibility of Bioinformatics workflows in a cloud computing environment using both Infrastructure as a Service (IaaS) and Not only SQL (NoSQL) database systems. To meet the goal, we have built 3 typical Bioinformatics workflows and ran them on 1 private and 2 public clouds, using different types of NoSQL database systems to persist the provenance data according to the Provenance Data Model (PROV-DM). We present here the results and a guide for the deployment of a cloud environment for Bioinformatics exploring the characteristics of various NoSQL database systems to persist provenance data.

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References

2016The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update 2015Big Data: Astronomical or Genomical?2010Computational solutions to large-scale data management and analysis 2016A review of bioinformatic pipeline frameworks 2017Investigating reproducibility and tracking provenance – A genomic workflow case study 2013Capturing and querying workflow runtime provenance with PROV 2013Milieu: Lightweight and Configurable Big Data Provenance for Science 2018Managing Complex Workflows in Bioinformatics: An Interactive Toolkit With GPU Acceleration

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