transcript quantification
Definition
Transcript quantification is the computational process of measuring the abundance of RNA transcripts in a biological sample from high-throughput sequencing data. This technique estimates expression levels for each gene or transcript isoform, typically reported as counts, FPKM (Fragments Per Kilobase Million), or TPM (Transcripts Per Million). Modern methods like Salmon, Kallisto, and RSEM use alignment-free or alignment-based approaches to map sequencing reads to reference transcriptomes. Accurate quantification is essential for differential expression analysis, identifying biomarkers, understanding cellular states, and revealing regulatory mechanisms. The process accounts for technical biases including read length, sequencing depth, and transcript length to enable meaningful comparisons across samples and conditions.
Visualize transcript quantification in Nodes Bio
Researchers can visualize transcript quantification results as gene expression networks in Nodes Bio, where nodes represent genes and edge weights reflect co-expression patterns or correlation strength. Differential expression data can be overlaid onto pathway networks to identify dysregulated biological processes. Users can integrate quantification data with protein-protein interaction networks to explore how transcriptional changes propagate through cellular systems and identify key regulatory hubs.
Visualization Ideas:
- Co-expression networks showing genes with correlated expression patterns across samples
- Differential expression overlays on KEGG or Reactome pathway networks
- Gene regulatory networks connecting transcription factors to their quantified target genes
Example Use Case
A cancer research team performs RNA-seq on tumor samples from patients with varying treatment responses. Using transcript quantification, they measure expression levels of 20,000 genes across 100 samples. They identify 500 differentially expressed genes between responders and non-responders. By visualizing these genes in network context, they discover that upregulated transcripts cluster around immune checkpoint pathways, while downregulated genes are enriched in DNA repair mechanisms. This network-level insight reveals that treatment efficacy correlates with immune activation signatures, guiding biomarker development for patient stratification.