|ZFIN ID: ZDB-PUB-181108-3|
Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics
Noviello, T.M.R., Di Liddo, A., Ventola, G.M., Spagnuolo, A., D'Aniello, S., Ceccarelli, M., Cerulo, L.
|Source:||BMC Bioinformatics 19: 407 (Journal)|
|Registered Authors:||D'Aniello, Salvatore|
|Keywords:||Homology, Long ncRNA, String similarity|
|PubMed:||30400819 Full text @ BMC Bioinformatics|
Noviello, T.M.R., Di Liddo, A., Ventola, G.M., Spagnuolo, A., D'Aniello, S., Ceccarelli, M., Cerulo, L. (2018) Detection of long non-coding RNA homology, a comparative study on alignment and alignment-free metrics. BMC Bioinformatics. 19:407.
Background Long non-coding RNAs (lncRNAs) represent a novel class of non-coding RNAs having a crucial role in many biological processes. The identification of long non-coding homologs among different species is essential to investigate such roles in model organisms as homologous genes tend to retain similar molecular and biological functions. Alignment-based metrics are able to effectively capture the conservation of transcribed coding sequences and then the homology of protein coding genes. However, unlike protein coding genes the poor sequence conservation of long non-coding genes makes the identification of their homologs a challenging task.
Results In this study we compare alignment-based and alignment-free string similarity metrics and look at promoter regions as a possible source of conserved information. We show that promoter regions encode relevant information for the conservation of long non-coding genes across species and that such information is better captured by alignment-free metrics. We perform a genome wide test of this hypothesis in human, mouse, and zebrafish.
Conclusions The obtained results persuaded us to postulate the new hypothesis that, unlike protein coding genes, long non-coding genes tend to preserve their regulatory machinery rather than their transcribed sequence. All datasets, scripts, and the prediction tools adopted in this study are available at https://github.com/bioinformatics-sannio/lncrna-homologs .