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Recent experimental findings have shown fine and specific regulation of RNA degradation and the presence of various molecular machinery purposely devoted to this task, such as RNA binding proteins, non-coding RNAs, etc. A biological process can be studied by measuring time-courses of RNA abundance in response of internal and\/or external stimuli, using recent technologies, such as the microarrays or the Next Generation Sequencing devices. Unfortunately, the picture provided by looking only at the transcriptome abundance may not gain insight into its dynamic regulation. By contrast, independent simultaneous measurement of RNA expression and half-lives\u00a0could provide such valuable additional insight. A computational approach to the estimation of RNAs half-lives from RNA expression time profiles data,\u00a0can be a low-cost alternative to its\u00a0experimental measurement which may be also affected by various artifacts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Here we present a computational methodology, called StaRTrEK (STAbility Rates ThRough Expression Kinetics), able to estimate half-life values basing only on genome-wide gene expression time series without transcriptional inhibition. The StaRTrEK algorithm makes use of a simple first order kinetic model and of a<jats:inline-formula><jats:alternatives><jats:tex-math>$$l_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:msub><mml:mi>l<\/mml:mi><mml:mn>1<\/mml:mn><\/mml:msub><\/mml:math><\/jats:alternatives><\/jats:inline-formula>-norm regularized least square optimization approach to find its parameter values. Estimates provided by StaRTrEK are validated using\u00a0simulated data and three independent experimental datasets of two short (6 samples) and one long (48 samples) time-courses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We believe that our algorithm can be used as a fast valuable computational complement to time-course experimental gene expression\u00a0studies by adding a relevant kinetic property, i.e. the RNA half-life, with a strong biological interpretation, thus providing a dynamic picture of what is going in a cell during the biological process under study.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-022-04730-x","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T21:05:58Z","timestamp":1653080758000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition"],"prefix":"10.1186","volume":"23","author":[{"given":"Federica","family":"Conte","sequence":"first","affiliation":[]},{"given":"Federico","family":"Papa","sequence":"additional","affiliation":[]},{"given":"Paola","family":"Paci","sequence":"additional","affiliation":[]},{"given":"Lorenzo","family":"Farina","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"4730_CR1","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.coisb.2019.03.006","volume":"15","author":"W Ashworth","year":"2019","unstructured":"Ashworth W, Stoney PN, Yamamoto T. 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