Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non-AUG codons located in the annotated 5 UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in-frame and out-of-frame translational start sites within the 5 UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near-cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http://service.bioinformatik.uni-saarland.de/pretis.