Facilitating long non-coding RNAs (lncRNAs) annotation using FEELnc and its application to the dog transcriptome

Thomas Derrien (IGDR)
Thursday, January 24, 2019 - 10:30 to 12:00
Room Aurigny
Talk abstract: 

Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks consists in correctly identifying the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, I will present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model. FEElnc, freely available (https://github.com/tderrien/FEELnc), moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs. Finally, I will develop the use of FEELnc to characterize lncRNAs in the domestic dogs (Canis lupus familiaris) and to pinpoint lncRNAs involved in diseases.