%0 Journal Article
%A Velluva, Akhil
%A Radtke, Maximilian
%A Horn, Susanne
%A Popp, Bernt
%A Platzer, Konrad
%A Gjermeni, Erind
%A Lin, Chen-Ching
%A Lemke, Johannes R.
%A Garten, Antje
%A Schöneberg, Torsten
%A Blüher, Matthias
%A Abou Jamra, Rami
%A Le Duc, Diana
%+ Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
The Leipzig School of Human Origins (IMPRS), Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
%T Phenotype-tissue expression and exploration (PTEE) resource facilitates the choice of tissue for RNA-seq-based clinical genetics studies : 
%G eng
%U https://hdl.handle.net/21.11116/0000-0009-7E41-1
%R 10.1186/s12864-021-08125-9
%7 2021-11-07
%D 2021
%8 07.11.2021
%* Review method: peer-reviewed
%X Background<br>RNA-seq emerges as a valuable method for clinical genetics. The transcriptome is “dynamic” and tissue-specific, but typically the probed tissues to analyze (TA) are different from the tissue of interest (TI) based on pathophysiology.<br><br>Results<br>We developed Phenotype-Tissue Expression and Exploration (PTEE), a tool to facilitate the decision about the most suitable TA for RNA-seq. We integrated phenotype-annotated genes, used 54 tissues from GTEx to perform correlation analyses and identify expressed genes and transcripts between TAs and TIs. We identified skeletal muscle as the most appropriate TA to inquire for cardiac arrhythmia genes and skin as a good proxy to study neurodevelopmental disorders. We also explored RNA-seq limitations and show that on-off switching of gene expression during ontogenesis or circadian rhythm can cause blind spots for RNA-seq-based analyses.<br><br>Conclusions<br>PTEE aids the identification of tissues suitable for RNA-seq for a given pathology to increase the success rate of diagnosis and gene discovery. PTEE is freely available at https://bioinf.eva.mpg.de/PTEE/
%J BMC Genomics
%V 22
%N 802
%I Springer Nature
%@ 1471-2164