RNA-Seq
Definition
RNA-Seq (RNA sequencing) is a high-throughput sequencing technology that quantifies the complete transcriptome by converting RNA molecules into cDNA libraries for next-generation sequencing. It measures gene expression levels, identifies novel transcripts, detects alternative splicing events, and discovers sequence variants. Unlike microarrays, RNA-Seq provides single-nucleotide resolution without prior knowledge of sequences, enabling discovery of previously unknown genes and isoforms. The technique generates millions of short reads that are mapped to reference genomes or assembled de novo, producing quantitative data on transcript abundance (measured in FPKM, TPM, or raw counts). RNA-Seq has revolutionized transcriptomics by providing unprecedented insights into cellular states, developmental processes, and disease mechanisms.
Visualize RNA-Seq in Nodes Bio
Researchers can visualize RNA-Seq differential expression data as gene regulatory networks, connecting transcription factors to their target genes based on expression correlations. Nodes Bio enables integration of RNA-Seq results with protein-protein interaction networks, pathway databases, and phenotypic data to identify key regulatory hubs and disease-associated modules. Users can map expression fold-changes onto network nodes to reveal coordinated transcriptional programs and potential therapeutic targets.
Visualization Ideas:
- Co-expression networks showing genes with correlated expression patterns across samples
- Differential expression networks with upregulated and downregulated genes connected to biological pathways
- Transcription factor regulatory networks linking TFs to their target genes based on expression data
Example Use Case
A cancer research team performs RNA-Seq on tumor samples versus normal tissue to identify dysregulated genes in triple-negative breast cancer. They discover 2,500 differentially expressed genes and need to understand functional relationships. By importing RNA-Seq data into Nodes Bio, they visualize co-expression networks and identify a cluster of upregulated genes involved in epithelial-mesenchymal transition. Network analysis reveals three hub transcription factors driving metastatic potential, which become prime candidates for targeted therapy development and validation experiments.