AProvBio: An architecture for data provenance in bioinformatics workflows using graph database

Almeida, Rodrigo; Silva, Waldeyr da; Castro, Klayton; Walter, Maria Emília M. T.; Araújo, Aletéia; Holanda, Maristela; Lifschitz, Sérgio

doi:10.1109/bibm.2017.8217989

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PROV-DM has already been shown to be suitable for storing data sources from Bioinformatics workflows, 36,42 even though it was not created for this purpose explicitly. With its components, PROV-DM is capable of, respectively, dealing with agents, entities, activities, and the precedence at which they were created, used, or ended.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Wercelens

Silva

Hondo

et al. 2019

Evol Bioinform Online

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Likewise, Almeida et al 42 conducted a study on the Graph-Oriented model, in which researchers presented AProvBio, an architecture that can perform the data provenance of scientific experiments in Bioinformatics automatically, using PROV-DM and Neo4J.…”

Section: Related Workmentioning

confidence: 99%