Transzkriptomikai

Transzkriptomikai, commonly known in English as transcriptomics, is the branch of molecular biology that studies the transcriptome—the complete set of RNA transcripts produced by the genome under specific conditions. It examines gene expression, regulation, RNA processing, and turnover to provide a snapshot of cellular function and state.

The primary technology is RNA sequencing (RNA-seq), which yields quantitative measurements of coding and noncoding RNAs across the genome. Microarrays were an earlier basis for expression profiling. Recent advances include single-cell transcriptomics, which measures gene expression at cellular resolution and reveals cellular heterogeneity, developmental trajectories, and rare cell populations.

Analyses typically involve quality control, alignment or pseudo-alignment, transcript quantification, normalization, and downstream interpretation such as differential expression or isoform analysis. Common outputs include raw counts and normalized measures like TPM or FPKM. Widely used software includes STAR and HISAT2 for alignment, Salmon and Kallisto for quantification, and DESeq2, edgeR, or limma for differential expression analysis. For single-cell data, pipelines such as Cell Ranger and analysis frameworks like Seurat or Scanpy are commonly employed.

Applications of transzkriptomikai span developmental biology, cancer research, neuroscience, and plant science, supporting biomarker discovery, drug response studies, and crop improvement. The field increasingly integrates transcriptomic data with other omics to infer regulatory networks and functional pathways.

Challenges include technical biases, batch effects, annotation quality, isoform complexity, and data integration across platforms. Data are deposited in repositories such as GEO, ArrayExpress, and ENCODE, with standard formats including FASTQ, BAM, GTF/GFF, and expression matrices.