scholarly journals GeNNet: An Integrated Platform for Unifying Scientific Workflow Management and Graph Databases for Transcriptome Data Analysis

2016 ◽  
Author(s):  
Raquel L. Costa ◽  
Luiz M. R. Gadelha ◽  
Marcelo Ribeiro-Alves ◽  
Fabio Porto
Keyword(s):  
Regulatory Networks ◽  
Workflow Management ◽  
Scientific Workflow ◽  
Biological Data ◽  
Scientific Workflows ◽  
Graph Database ◽  
Graph Databases ◽  
Biological Databases ◽  
Transcriptome Data ◽  
Daunting Task

AbstractBackgroundThere 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 may additionally be integrated with other biological databases, such as Protein-Protein Interactions and annotations. 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 managingthe resulting data and its respective metadata are challenging tasks. Running in-silico experiments to structure and compose the information as needed for analysis is a daunting task. Different programsmay 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.ResultsWe present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. GeNNet includes pre-loaded biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and geneset enrichment analysis. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment. 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 resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene regulatory networks.ConclusionsGeNNet is the first platform to integrate the analytical process of transcriptome data with graph database. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers as well can add new functionality to each component of GeNNet. The resulting data allows for testing previous hypotheses about an experiment as well as exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms.

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

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3509 ◽  
Author(s):  
Raquel L. Costa ◽  
Luiz Gadelha ◽  
Marcelo Ribeiro-Alves ◽  
Fábio Porto
Keyword(s):  
Protein Interactions ◽  
Gene Annotation ◽  
Scientific Workflow ◽  
Biological Data ◽  
Scientific Workflows ◽  
Gene Set Enrichment Analysis ◽  
Graph Database ◽  
Graph Databases ◽  
Biological Databases ◽  
Transcriptome Data

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 resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.

scholarly journals Applying graph database technology for analyzing perturbed co-expression networks in cancer

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Claire M Simpson ◽  
Florian Gnad
Keyword(s):  
Relational Databases ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Database Management System ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Representations ◽  
Rnaseq Data ◽  
Database Technology ◽  
Speed Accuracy

Abstract Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

scholarly journals Tripal and Galaxy: supporting reproducible scientific workflows for community biological databases

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Shawna Spoor ◽  
Connor Wytko ◽  
Brian Soto ◽  
Ming Chen ◽  
Abdullah Almsaeed ◽  
...  
Keyword(s):  
Workflow Management ◽  
Application Programming Interface ◽  
Scientific Workflow ◽  
Biological Databases ◽  
Web Based ◽  
Job Management ◽  
The Galaxy ◽  
Application Programming ◽  
Computational Resources ◽  
Programming Interface

Abstract Online biological databases housing genomics, genetic and breeding data can be constructed using the Tripal toolkit. Tripal is an open-source, internationally developed framework that implements FAIR data principles and is meant to ease the burden of constructing such websites for research communities. Use of a common, open framework improves the sustainability and manageability of such as site. Site developers can create extensions for their site and in turn share those extensions with others. One challenge that community databases often face is the need to provide tools for their users that analyze increasingly larger datasets using multiple software tools strung together in a scientific workflow on complicated computational resources. The Tripal Galaxy module, a ‘plug-in’ for Tripal, meets this need through integration of Tripal with the Galaxy Project workflow management system. Site developers can create workflows appropriate to the needs of their community using Galaxy and then share those for execution on their Tripal sites via automatically constructed, but configurable, web forms or using an application programming interface to power web-based analytical applications. The Tripal Galaxy module helps reduce duplication of effort by allowing site developers to spend time constructing workflows and building their applications rather than rebuilding infrastructure for job management of multi-step applications.

Integrating Scientific Workflows with Scientific Gateways: A Bioinformatics Experiment in the Brazilian National High-Performance Computing Network

2020 ◽  
Author(s):  
Maria Luiza Mondelli ◽  
Marcelo Monteiro Galheigo ◽  
Vivivan Medeiros ◽  
Bruno F. Bastos ◽  
Antônio Tadeu Azevedo Gomes ◽  
...  
Keyword(s):  
High Performance ◽  
Workflow Management ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Management Systems ◽  
Workflow Management Systems ◽  
Sequencing Technologies ◽  
Geographically Distributed ◽  
Performance Computing ◽  
High Performance Computation

Bioinformatics experiments are rapidly and constantly evolving due improvements in sequencing technologies. These experiments usually demand high performance computation and produce huge quantities of data. They also require different programs to be executed in a certain order, allowing the experiments to be modeled as workflows. However, users do not always have the infrastructure needed to perform these experiments. Our contribution is the integration of scientific workflow management systems and grid-enabled scientific gateways, providing the user with a transparent way to run these workflows in geographically distributed computing resources. The availability of the workflow through the gateway allows for a better usability of these experiments.

scholarly journals The role of machine learning in scientific workflows

2019 ◽  
Vol 33 (6) ◽  
pp. 1128-1139 ◽  
Author(s):  
Ewa Deelman ◽  
Anirban Mandal ◽  
Ming Jiang ◽  
Rizos Sakellariou
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Autonomous Vehicles ◽  
Workflow Management ◽  
Scientific Productivity ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Materials Development ◽  
Smart Agriculture

Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.

scholarly journals SciPipe - A workflow library for agile development of complex and dynamic bioinformatics pipelines

2018 ◽  
Author(s):  
Samuel Lampa ◽  
Martin Dahlö ◽  
Jonathan Alvarsson ◽  
Ola Spjuth
Keyword(s):  
Machine Learning ◽  
Dynamic Scheduling ◽  
Workflow Management ◽  
Scientific Workflow ◽  
Biological Data ◽  
Agile Development ◽  
Complex Nature ◽  
Reusable Components ◽  
Programming Library ◽  
Machine Leaning

AbstractBackgroundThe complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation and aid reproducibility of analyses. Many contemporary workflow tools are specialized and not designed for highly complex workflows, such as with nested loops, dynamic scheduling and parametriza-tion, which is common in e.g. machine learning.FindingsSciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on Flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development, and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX and PDF on-demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline.ConclusionsSciPipe provides a solution for agile development of complex and dynamic pipelines, espe-cially in machine leaning, through a flexible programming API suitable for scientists used to programming or scripting.

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