Transcriptomics technologies: Difference between revisions

Revision as of 05:25, 20 May 2024

Revision as of 05:35, 20 May 2024

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==== A transcriptome based aging clock ====

Aging-related preventive interventions are not possible without personal aging speed measurement. The most up to date and complex way to measure aging rate is by using varying biomarkers of human aging is based on the utilization of deep neural networks which may be trained on any type of omics biological data to predict the subject's age. Aging has been shown to be a strong driver of transcriptome changes.<ref name="clock">{{cite journal | vauthors = Meyer DH, Schumacher B | year = 2020 | title = BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy | journal = Aging Cell| volume = 20 | issue = 3 | pages = e13320 | doi = 10.1111/acel.13320 | pmid = 33656257 | pmc = 7963339 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Fleischer JG, Schulte R, Tsai HH, Tyagi S, Ibarra A, Shokhirev MN, Navlakha S | year = 2018 | title = Predicting age from the transcriptome of human dermal fibroblasts | journal = Genome Biology | volume = 19 | issue = 1| page = 221 | doi = 10.1186/s13059-018-1599-6 | pmid = 30567591 | pmc = 6300908 | doi-access = free }}</ref> Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. However an approach that uses temporal scaling and binarization of transcriptomes to define a gene set that predicts biological age with an accuracy allowed to reach an assessment close to the theoretical limit.<ref name="clock" />

=== Non-coding RNA ===