GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

Costa, Raquel L.; Gadelha, Luiz M. R.; Ribeiro-Alves, Marcelo; Porto, Fábio

doi:10.7717/peerj.3509

Cited by 10 publications

(9 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they do not integrate their data in a framework that allows scalable and detailed querying (e.g., quickly extracting all water table and temperature data from multiple sites into a single table, for scaling of water table-temperature relationships from individual sites to a broader geographical range). The field of bioinformatics is further along in this regard: for molecular meta-omic data, numerous databases (e.g., MIGS/MIMS, MIMAS, IMG/M, GeneLab) (Hermida et al, 2006;Field et al, 2008;Gattiker et al, 2009;Chen et al, 2019;Ray et al, 2019) and integrative data management platforms (e.g., KBase, MOD-CO, ODG, GeNNet, BioKNO, MGV, OMMS, mixOmics) (Sujansky, 2001;Symons & Nieselt, 2011;Perez-Arriaga et al, 2015;Yoon, Kim & Kim, 2017;Costa et al, 2017;Rohart et al, 2017;Guhlin et al, 2017;Manzoni et al, 2018;Arkin et al, 2018;Brandizi et al, 2018;Rambold et al, 2019) have been developed, and often include standardization of sample metadata to enable efficient data integration. Notable among these are KBase (https://kbase.us/) (Arkin et al, 2018), which provides "apps" through which users can process their data in a framework that tracks processing steps ("provenance") in an accessible format, and MOD-CO (Rambold et al, 2019), a bioinformatics data processing tool that includes a conceptual schema and data model to track metadata and workflows.…”

Section: Introductionmentioning

confidence: 99%

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

Bolduc¹,

Hodgkins²,

Varner³

et al. 2020

PeerJ

View full text Add to dashboard Cite

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project’s interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team’s modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB’s expandability and flexible architecture allow it to serve as an example ecosystems database.

show abstract

Section: Introductionmentioning

confidence: 99%

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

Bolduc¹,

Hodgkins²,

Varner³

et al. 2020

PeerJ

View full text Add to dashboard Cite

show abstract

“…However, they do not integrate their data in a framework that allows scalable and detailed querying (for example, quickly extracting all water table and temperature data from multiple sites into a single table, for scaling of water table-temperature relationships from individual sites to a broader geographical range). The field of bioinformatics is further along in this regard: for molecular meta-omic data, numerous databases (e.g., MIGS/MIMS, MIMAS, IMG/M, GeneLab) (Hermida et al, 2006;Field et al, 2008;Gattiker et al, 2009;Chen et al, 2019;Ray et al, 2019) and integrative data management platforms (e.g., KBase, MOD-CO, ODG, GeNNet, BioKNO, MGV, OMMS, mixOmics) (Sujansky, 2001;Symons & Nieselt, 2011;Perez-Arriaga et al, 2015;Yoon, Kim & Kim, 2017;Costa et al, 2017;Rohart et al, 2017;Guhlin et al, 2017;Manzoni et al, 2018;Arkin et al, 2018;Brandizi et al, 2018;Rambold et al, 2019) have been developed, and often include standardization of sample metadata to enable efficient data integration. Notable among these are KBase (https://kbase.us/) (Arkin et al, 2018), which provides "apps" through which users can process their data in a framework that tracks processing steps ("provenance") in an accessible format, and MOD-CO (Rambold et al, PeerJ reviewing PDF | (2019:10:42335:1:1:NEW 22 May 2020)…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research (v0.2)"

2020

View full text Add to dashboard Cite

Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in 'speaking' to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10-year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project's interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team's modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB's expandability and flexible architecture allow it to serve as an example

show abstract

“…A recent paper presents GeNNet 6 , which describes the rationale for scripted workflows and the use of graph databases in reproducible research. In this paper a scripted workflow in R 7 , use of Neo4j to store data and the use of Docker for reproducibility is described.…”

Section: Introductionmentioning

confidence: 99%

ANIMA: Association network integration for multiscale analysis

Deffur¹,

Wilkinson²,

Mayosi³

et al. 2018

Wellcome Open Res

View full text Add to dashboard Cite

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we describe ANIMA (association network integration for multiscale analysis), a network-based data integration method using clinical phenotype and microarray data as inputs. ANIMA is implemented in R and Neo4j and runs in Docker containers. In short, the build algorithm iterates over one or more transcriptomics datasets to generate a large, multipartite association network by executing multiple independent analytic steps (differential expression, deconvolution, modular analysis based on co-expression, pathway analysis) and integrating the results. Once the network is built, it can be queried directly using Cypher (a graph query language), or by custom functions that communicate with the graph database via language-specific APIs. We developed a web application using Shiny, which provides fully interactive, multiscale views of the data. Using our approach, we show that we can reconstruct multiple features of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behaviour in whole blood samples, both in single experiments as well in meta-analyses of multiple datasets.

show abstract

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

Cited by 10 publications

References 76 publications

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research

Peer Review #1 of "The IsoGenie database: an interdisciplinary data management solution for ecosystems biology and environmental research (v0.2)"

ANIMA: Association network integration for multiscale analysis

Contact Info

Product

Resources

About