Clustering Rfam 10.1: Clans, Families, and Classes
1
Department of Computer Science, Institute of Exact Sciences, University of Brasília, Brasília 70910-900, Brazil
2
Department of Cellular Biology, Institute of Biology, University of Brasília, Brasília 70910-900, Brazil
3
Department of Mathematics, University of Brasília, Brasília 70910-900, Brazil
4
Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstraße 16-18, D-04107 Leipzig, Germany
5
Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig D-04103, Germany
6
Fraunhofer Institut für Zelltherapie und Immunologie–IZI Perlickstraße 1, D-04103 Leipzig, Germany
7
Institute for Theoretical Chemistry , University of Vienna, Währingerstraße 17, Wien A-1090, Austria
8
Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, DK-1870 Frederiksberg C, Denmark
9
Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM 87501, USA
*
Author to whom correspondence should be addressed.
Genes 2012, 3(3), 378-390; https://doi.org/10.3390/genes3030378
Received: 5 May 2012 / Revised: 4 June 2012 / Accepted: 15 June 2012 / Published: 5 July 2012
(This article belongs to the Special Issue Feature Paper 2012)
The Rfam database contains information about non-coding RNAs emphasizing their secondary structures and organizing them into families of homologous RNA genes or functional RNA elements. Recently, a higher order organization of Rfam in terms of the so-called clans was proposed along with its “decimal release”. In this proposition, some of the families have been assigned to clans based on experimental and computational data in order to find related families. In the present work we investigate an alternative classification for the RNA families based on tree edit distance. The resulting clustering recovers some of the Rfam clans. The majority of clans, however, are not recovered by the structural clustering. Instead, they get dispersed into larger clusters, which correspond roughly to well-described RNA classes such as snoRNAs, miRNAs, and CRISPRs. In conclusion, a structure-based clustering can contribute to the elucidation of the relationships among the Rfam families beyond the realm of clans and classes.
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Keywords:
Rfam; non-coding RNA; secondary structure; clans; clusters
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MDPI and ACS Style
Lessa, F.A.; Raiol, T.; Brigido, M.M.; Martins Neto, D.S.B.; Walter, M.E.M.T.; Stadler, P.F. Clustering Rfam 10.1: Clans, Families, and Classes. Genes 2012, 3, 378-390. https://doi.org/10.3390/genes3030378
AMA Style
Lessa FA, Raiol T, Brigido MM, Martins Neto DSB, Walter MEMT, Stadler PF. Clustering Rfam 10.1: Clans, Families, and Classes. Genes. 2012; 3(3):378-390. https://doi.org/10.3390/genes3030378
Chicago/Turabian StyleLessa, Felipe A.; Raiol, Tainá; Brigido, Marcelo M.; Martins Neto, Daniele S. B.; Walter, Maria Emília M. T.; Stadler, Peter F. 2012. "Clustering Rfam 10.1: Clans, Families, and Classes" Genes 3, no. 3: 378-390. https://doi.org/10.3390/genes3030378
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