Provenance in bioinformatics workflows

Paula, Renato De; Holanda, Maristela; Gomes, Luciana S. A.; Lifschitz, Sérgio; Walter, Maria Tereza Machado Teles

doi:10.1186/1471-2105-14-s11-s6

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…De Paula et al 36 proposed the management of data provenance for genome projects using PROV-DM. Their results demonstrated PROV-DM as a suitable model for storing properties for each execution of Bioinformatics workflows, and one which also provided graphical representation for the large volumes of data generated by genome projects using the Entity Collections.…”

Section: Related Workmentioning

confidence: 99%

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Wercelens

Silva

Hondo

et al. 2019

Evol Bioinform Online

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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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Section: Related Workmentioning

confidence: 99%

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

show abstract

“…We argue that integrated web applications, involving scientific workflows and databases, can hide the complexity of underlying scientific software by abstracting away cumbersome aspects, such as managing files and setting command-line parameters, leading to increased productivity for scientists. One important aspect of enabling reproducible computational analyses is keeping track of the computational environment components, i.e., operating system, libraries, software packages and their respective versions (de Paula et al, 2013).…”

Section: /18mentioning

confidence: 99%

Peer Review #2 of "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis (v0.1)"

2017

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There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The

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“…One emerging issue is keeping track of all of the tools, workflows, and parameters used in a bioinformatics project. To ensure data integrity, reproducibility and optimization of workflow performance, data provenance models, such as BioQ (Saccone et al 2012), SemPoD (Jayapandian et al 2012), and PROV-DM model (de Paula et al 2013), have been developed to track the origin of the biological data and the processes used to analyze it.…”

Section: Dataset Analysismentioning

confidence: 99%

Incorporating computational resources in a cancer research program

2014

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Recent technological advances have transformed cancer genetics research. These advances have served as the basis for the generation of a number of richly annotated datasets relevant to the cancer geneticist. In addition, many of these technologies are now within reach of smaller laboratories to answer specific biological questions. Thus, one of the most pressing issues facing an experimental cancer biology research program in genetics is incorporating data from multiple sources to annotate, visualize, and analyze the system under study. Fortunately, there are several computational resources to aid in this process. However, a significant effort is required to adapt a molecular biology-based research program to take advantage of these datasets. Here, we discuss the lessons learned in our laboratory and share several recommendations to make this transition effectively. This article is not meant to be a comprehensive evaluation of all the available resources, but rather highlight those that we have incorporated into our laboratory and how to choose the most appropriate ones for your research program.

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Provenance in bioinformatics workflows

Cited by 17 publications

References 19 publications

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Bioinformatics Workflows With NoSQL Database in Cloud Computing

Peer Review #2 of "GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis (v0.1)"

Incorporating computational resources in a cancer research program

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