RT Journal Article SR Electronic T1 FEELnc: A tool for Long non-coding RNAs annotation and its application to the dog transcriptome JF bioRxiv FD Cold Spring Harbor Laboratory SP 064436 DO 10.1101/064436 A1 V Wucher A1 F Legeai A1 B Hédan A1 G Rizk A1 L Lagoutte A1 T Leeb A1 V Jagannathan A1 E Cadieu A1 A David A1 H Lohi A1 S Cirera A1 M Fredholm A1 N Botherel A1 P Leegwater A1 C Le Béguec A1 H Fieten A1 C Johansson A1 J Johnsson A1 LUPA consortium A1 J Alifoldi A1 C André A1 K Lindblad-Toh A1 C Hitte A1 T Derrien YR 2016 UL http://biorxiv.org/content/early/2016/07/18/064436.abstract 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.