Transcriptomics technologies: Difference between revisions

Revision as of 14:28, 9 January 2025

Revision as of 13:36, 25 January 2025

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A major challenge in molecular biology is to understand how a single genome gives rise to a variety of cells. Another is how gene expression is regulated.

The first attempts to study whole transcriptomes began in the early 1990s. Subsequent technological advances since the late 1990s have repeatedly transformed the field and made transcriptomics a widespread discipline in biological sciences. There are two key contemporary techniques in the field: [[microarray]]s, which quantify a set of predetermined sequences, and [[RNA-Seq]], which uses [[DNA sequencing#Next-generation methods|high-throughput sequencing]] to record all transcripts. As the technology improved, the volume of data produced by each transcriptome experiment increased. As a result, data analysis methods have steadily been adapted to more accurately and efficiently analyse increasingly large volumes of data. Transcriptome databases gettinghave consequently been growing bigger and more useful as transcriptomes continue to be collected and shared by researchers. It would be almost impossible to interpret the information contained in a transcriptome without the knowledge of previous experiments.

Measuring the expression of an organism's [[gene]]s in different [[tissue (biology)|tissues]] or [[Environment (biophysical)|conditions]], or at different times, gives information on how genes are [[Regulation of gene expression|regulated]] and reveals details of an organism's biology. It can also be used to infer the [[Phenotype|functions]] of previously [[DNA annotation|unannotated]] genes. Transcriptome analysis has enabled the study of how gene expression changes in different organisms and has been instrumental in the understanding of human [[disease]]. An analysis of gene expression in its entirety allows detection of broad coordinated trends which cannot be discerned by more targeted [[assay]]s.