Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a long procedure and the obtained dissimilarity results is not a metric. Recently the normalized compression distance was introduced as a method to calculate the distance between two generic digital objects, and it seems a suitable way to compare genomic strings. In this paper the clustering and the mapping, obtained using a SOM, with the traditional evolutionary distance and the compression distance are compared in order to understand if the two distances sets are similar. The first results indicate that the two distances catch different aspects of the genomic sequences and further investigations are needed to obtain a definitive result.

LA ROSA, M., RIZZO, R., URSO, A., GAGLIO, S. (2008). Comparison of genomic sequences clustering using Normalized Compression Distance and Evolutionary Distance. Lecture Notes in Artificial Intelligence, 2008.

Comparison of genomic sequences clustering using Normalized Compression Distance and Evolutionary Distance

LA ROSA, Massimo;GAGLIO, Salvatore
2008

Abstract

Genomic sequences are usually compared using evolutionary distance, a procedure that implies the alignment of the sequences. Alignment of long sequences is a long procedure and the obtained dissimilarity results is not a metric. Recently the normalized compression distance was introduced as a method to calculate the distance between two generic digital objects, and it seems a suitable way to compare genomic strings. In this paper the clustering and the mapping, obtained using a SOM, with the traditional evolutionary distance and the compression distance are compared in order to understand if the two distances sets are similar. The first results indicate that the two distances catch different aspects of the genomic sequences and further investigations are needed to obtain a definitive result.
LA ROSA, M., RIZZO, R., URSO, A., GAGLIO, S. (2008). Comparison of genomic sequences clustering using Normalized Compression Distance and Evolutionary Distance. Lecture Notes in Artificial Intelligence, 2008.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/48454
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? ND
social impact