data pipeline
Definition
A data pipeline is an automated series of computational processes that extract, transform, and load (ETL) biological data from raw sources into formats suitable for analysis and visualization. In life sciences, pipelines integrate heterogeneous datasets from genomics, proteomics, clinical records, and literature databases, applying quality control, normalization, and annotation steps. Effective pipelines ensure reproducibility, scalability, and data integrity while handling large-scale omics data. They typically include validation checkpoints, error handling, and metadata tracking. For network biology, pipelines prepare interaction data, calculate network metrics, and generate graph structures that reveal biological relationships and system-level insights.
Visualize data pipeline in Nodes Bio
Researchers use Nodes Bio to visualize the output of data pipelines as interactive network graphs. Pipeline-processed datasets containing protein interactions, gene co-expression, or metabolic pathways can be imported and explored as connected nodes and edges. Users can trace data provenance, identify processing artifacts, and validate pipeline outputs by examining network topology, detecting unexpected connections, or confirming known biological relationships within the visual framework.
Visualization Ideas:
- Pipeline workflow diagrams showing data transformation stages as connected process nodes
- Quality control networks displaying sample relationships and batch effects
- Multi-omics integration networks showing merged datasets from different pipeline sources
Example Use Case
A cancer genomics team develops a data pipeline that integrates TCGA mutation data, STRING protein interactions, and DrugBank compound information. The pipeline filters for mutations in specific tumor types, maps them to affected proteins, and identifies drug targets. Raw sequencing data undergoes quality control, variant calling, and functional annotation before network construction. The resulting multi-layer network reveals druggable proteins connected to frequently mutated genes, enabling the team to prioritize combination therapy candidates for experimental validation.