Open access · CC-BY
via OpenAlex
Transcriptomics technologies
Rohan G. T. Lowe, Neil J. Shirley, Mark R. Bleackley, Stephen K. Dolan, Thomas Shafee
PLoS Computational Biology · 2017 · ▲ 1,155 citations
Abstract
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst noncoding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell. The first attempts to study the whole transcriptome began in the early 1990s, and technological advances since the late 1990s have made transcriptomics a widespread discipline. Transcriptomics has been defined by repeated technological innovations that transform the field. There are two key contemporary techniques in the field: microarrays, which quantify a set of predetermined sequences, and RNA sequencing (RNA-Seq), which uses high-throughput sequencing to capture all sequences. Measuring the expression of an organism's genes in different tissues, conditions, or time points gives information on how genes are regulated and reveals details of an organism's biology. It can also help to infer the functions of previously unannotated genes. Transcriptomic 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 assays.
◌ CITATION ONLY
Full text is not openly licensed for redistribution here. Read it at the source:
Provenance
- Source
- OpenAlex
- DOI
- 10.1371/journal.pcbi.1005457
- Canonical
- link ↗
- Fetched
- 2026-07-28 MST
Cite this
APA
Lowe, R.G.T., Shirley, N.J., Bleackley, M.R., Dolan, S.K., & Shafee, T. (2017). Transcriptomics technologies. <em>PLoS Computational Biology</em>. https://doi.org/10.1371/journal.pcbi.1005457
Vancouver
Lowe RGT, Shirley NJ, Bleackley MR, Dolan SK, Shafee T. Transcriptomics technologies. PLoS Computational Biology. 2017. doi:10.1371/journal.pcbi.1005457.
BibTeX
@article{rohan2017Transc,
title = {Transcriptomics technologies},
author = {Rohan G. T. Lowe and Neil J. Shirley and Mark R. Bleackley and Stephen K. Dolan and Thomas Shafee},
journal = {PLoS Computational Biology},
year = {2017},
doi = {10.1371/journal.pcbi.1005457},
}
Research neighborhood
References, citing works, and semantically nearest findings. Click a node to open it.
Related findings
Science China Life Sciences 2013
Open access · CC-BY
Transcriptomics: Advances and approaches
Frontiers in Neuroscience 2026
Open access · CC-BY
Identifying transcriptomic signatures that mediate the causal effect of genotype on Alzheimer's disease
bioRxiv (Cold Spring Harbor Laboratory) 2025
Preprint · CC-BY
Coordinated shifts in gene expression and regulation during mole-rat evolution
eLife 2026
Preprint · CC-BY
Gene function and expression profiling in yeast spores, killifish diapause embryos, and their post-dormant offspring cells
eLife 2026
Preprint · CC-BY
Gene function and expression profiling in yeast spores, killifish diapause embryos, and their post-dormant offspring cells
Aging 2016
Open access · CC-BY