PT - JOURNAL ARTICLE AU - V Wucher AU - F Legeai AU - B Hédan AU - G Rizk AU - L Lagoutte AU - T Leeb AU - V Jagannathan AU - E Cadieu AU - A David AU - H Lohi AU - S Cirera AU - M Fredholm AU - N Botherel AU - P Leegwater AU - C Le Béguec AU - H Fieten AU - C Johansson AU - J Johnsson AU - LUPA consortium AU - J Alifoldi AU - C André AU - K Lindblad-Toh AU - C Hitte AU - T Derrien TI - FEELnc: A tool for Long non-coding RNAs annotation and its application to the dog transcriptome AID - 10.1101/064436 DP - 2016 Jan 01 TA - bioRxiv PG - 064436 4099 - http://biorxiv.org/content/early/2016/07/18/064436.short 4100 - http://biorxiv.org/content/early/2016/07/18/064436.full AB - Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. Among the plethora of reconstructed transcripts, one of the main bottlenecks consists in correctly identifying the different classes of RNAs, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. The program also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs. We used FEELnc on a real dataset comprising 20 new canine RNA-seq samples produced in the frame of the European LUPA consortium to expand the canine genome annotation and classified 10,374 novel lncRNAs and 58,640 new mRNA transcripts. FEELnc represents a standardized protocol for identifying and annotating lncRNAs and is freely accessible at https://github.com/tderrien/FEELnc.