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Automatic generation of pseudoknotted RNAs taxonomy.
Quadrini, Michela; Tesei, Luca; Merelli, Emanuela.
Affiliation
  • Quadrini M; School of Sciences and Technology, University of Camerino, Via Madonna delle Carceri 7, 62032, Camerino, MC, Italy.
  • Tesei L; School of Sciences and Technology, University of Camerino, Via Madonna delle Carceri 7, 62032, Camerino, MC, Italy. luca.tesei@unicam.it.
  • Merelli E; School of Sciences and Technology, University of Camerino, Via Madonna delle Carceri 7, 62032, Camerino, MC, Italy.
BMC Bioinformatics ; 23(Suppl 6): 575, 2023 Jun 15.
Article in En | MEDLINE | ID: mdl-37322429
ABSTRACT

BACKGROUND:

The ability to compare RNA secondary structures is important in understanding their biological function and for grouping similar organisms into families by looking at evolutionarily conserved sequences such as 16S rRNA. Most comparison methods and benchmarks in the literature focus on pseudoknot-free structures due to the difficulty of mapping pseudoknots in classical tree representations. Some approaches exist that permit to cluster pseudoknotted RNAs but there is not a general framework for evaluating their performance.

RESULTS:

We introduce an evaluation framework based on a similarity/dissimilarity measure obtained by a comparison method and agglomerative clustering. Their combination automatically partition a set of molecules into groups. To illustrate the framework we define and make available a benchmark of pseudoknotted (16S and 23S) and pseudoknot-free (5S) rRNA secondary structures belonging to Archaea, Bacteria and Eukaryota. We also consider five different comparison methods from the literature that are able to manage pseudoknots. For each method we clusterize the molecules in the benchmark to obtain the taxa at the rank phylum according to the European Nucleotide Archive curated taxonomy. We compute appropriate metrics for each method and we compare their suitability to reconstruct the taxa.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / RNA Limits: Humans Language: En Journal: BMC Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: Italy

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Algorithms / RNA Limits: Humans Language: En Journal: BMC Bioinformatics Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: Italy