Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a user-friendly interactive Shiny app.