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Integration of "omics" Data and Phenotypic Data Within a Unified Extensible Multimodal Framework
Samir Das1,2, Xavier Lecours Boucher1,2, Christine Rogers1,2
1McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada.
Frontiers in Neuroinformatics
|January 12, 2019
Summary
Scientists developed a generalizable "omics" framework to automate and standardize complex data analysis. This framework integrates diverse datasets, streamlines workflows, and enhances data sharing for improved reproducibility in brain research.
Area of Science:
- Neuroinformatics
- Genetics and Epigenetics
- Computational Biology
Background:
- Omics data analysis is complex, segmented, and challenging to automate, often requiring manual interventions that reduce reliability and reproducibility.
- Heterogeneous datasets and cross-modal genomic analyses complicate standardization and data sharing, leading to time and resource inefficiencies for researchers.
Purpose of the Study:
- To design an automated, seamless process for handling (epi)genetic data, consolidating heterogeneous datasets into the LORIS open-source platform.
- To streamline data analysis, integrate results with provenance information, and facilitate reproducible sharing of analysis pipelines via high-performance computing (HPC) using the CBRAIN portal.
Main Methods:
- Development of a generalizable "omics" framework integrating multi-modal datasets (imaging, clinical, demographics, behavioral).
- Automation of analysis pipeline execution on HPC platforms, removing bioinformatic barriers.
- Implementation of standardized and transparent sharing of processing pipelines for computational consistency.
Main Results:
- The framework enables integration of diverse biological and multi-modal datasets.
- Automated pipeline launching on HPC platforms ensures standardization and computational consistency.
- Results are stored in a queryable web interface with visualization tools, enhancing usability and reproducibility.
Conclusions:
- The developed framework significantly reduces human error through automated analysis pipelines and seamless multimodal data linking.
- It facilitates brain research discovery by allowing investigators to focus on research rather than data handling.
- The framework promotes optimized reproducibility and efficient data sharing in complex omics research.