Transkriptoms

Transkriptoms, also called transcriptomes, denote the complete set of RNA transcripts produced by the genome in a given cell, tissue, or organism under a specific condition. The transcriptome includes messenger RNA as well as various non-coding RNA species such as ribosomal RNA, transfer RNA, microRNAs, and long noncoding RNAs. Because transcription is dynamic, the transcriptome changes with developmental stage, tissue type, environmental stimuli, and disease state.

Transcriptome profiling is performed by technologies such as RNA sequencing (RNA-Seq), which captures sequence information and transcript abundance without prior assumptions, and microarrays, which rely on predefined probes. More recently, single-cell RNA-Seq and long-read sequencing enable analysis of expression in individual cells and identification of full-length isoforms, respectively.

Data analysis typically involves aligning reads to a reference genome, assembling transcripts, and quantifying expression at the gene or transcript level. Analyses may report differential gene expression, differential transcript usage, splice variants, and novel transcripts. Normalization methods such as TPM or FPKM are used for expression estimates, while count-based models (e.g., DESeq2, edgeR) are used for differential testing.

Reference transcriptomes are curated resources that support annotation and interpretation, including GENCODE, RefSeq, and Ensembl. They enable researchers to identify known transcripts and discover novel isoforms.

Applications include elucidating tissue-specific biology, developmental processes, cancer biology, and drug response; biomarker discovery; and personalized medicine. Limitations include incomplete annotations, challenges in isoform resolution, biases and batch effects in data, and dependence on sample quality and sequencing depth.