Transcriptomik

Transcriptomik, or transcriptomics, is the field of study concerned with the transcriptome—the complete set of RNA transcripts produced by the genome under specific conditions. It seeks to catalog and quantify RNA species, understand transcriptional regulation, RNA processing, and the dynamics of gene expression across tissues, developmental stages, and environmental conditions. Transcriptomik encompasses both coding and non-coding RNAs, including messenger RNA, long non-coding RNA, microRNA, and pseudogenes, as well as alternative transcript isoforms generated by splicing.

High-throughput technologies form the basis of transcriptomik. RNA sequencing (RNA-seq) provides a quantitative snapshot of transcript abundance and can reveal novel transcripts, splice variants, and allelic expression. Microarrays were used historically. More recently, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics offer resolution at the level of individual cells or spatial locations within tissues. The data are processed by quality control, alignment to reference genomes, transcript assembly, and quantification, producing measures such as counts, transcripts per million (TPM), FPKM/RPKM, and differential expression statistics.

Applications include mapping tissue- and condition-specific expression patterns, identifying biomarkers, studying development and disease, and informing functional genomics and drug response research. Public data resources such as GEO, SRA, and ENCODE support reanalysis and meta-studies. Standards for reporting and metadata continue to evolve with MINSEQE and related guidelines.

Challenges include biases in library preparation, incomplete transcript annotation, isoform ambiguity, and batch effects. Ongoing advances in long-read sequencing, multi-omics integration, and computational methods aim to improve transcript identification, quantification, and interpretation.