Automatic GO annotation of Long Non-coding RNAs

SPETALE FLAVIO EZEQUIEL; MURILLO JAVIER; VANINA VILLANOVA; BULACIO PILAR; TAPIA, ELIZABETH

Queretaro

Simposio; ISCB-LA SoIBio BioNetMX 2020; 2020

The study of long non-coding RNAs (lncRNAs), > 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Hence, current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based Machine Learning method called FGGA-lnc for the automatic Gene Ontology (GO) annotation of lncRNAs across the three GO sub-domains. We build upon FGGA (Factor Graph GO Annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. The set of likely inconsistent GO annotations is then polished by the message passing machinery embodied in the factor graph model of the target ontology. Evaluations of FGGA-lnc on zebrafish lncRNA data showed promising results suggesting it as a candidate to satisfy the huge demand of functional annotations arising from high-throughput sequencing technologies.