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© The Author(s). 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. ; We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with read clustering. ReorientExpress enables long-read transcriptomics in non-model organisms and samples without a genome reference without using additional technologies and is available at https://github.com/comprna/reorientexpress ; Part of this work was funded by the Spanish Government and FEDER with grants BIO2017-85364-R and MDM-2014-0370 and by Catalan Government (AGAUR) with grant SGR2017-1020. JI was supported by a PhD grant from FCT (Fundação para a Ciência e a Tecnologia) Portugal. IdlR had funding from an FPI grant from the Spanish Government (PRE2018-083413). ; info:eu-repo/semantics/publishedVersion