2024-03-28T13:11:59Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/1289702024-03-15T10:57:20Zcom_10803_236col_10803_690278
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Non-coding RNA
Long non-coding RNA
Alignment
Comparative biology
Blast
Homology search
ARN no codificante
ARN largo no codificantes
Alineamiento
Biología comparativa
Búsqueda de homología
Detecting and comparing non-coding RNAs
[Barcelona] :
Universitat Pompeu Fabra,
2014
Accés lliure
http://hdl.handle.net/10803/128970
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Bussotti, Giovanni,
autor
Programa de doctorat en Biomedicina,
degree
1 recurs en línia (95 pàgines)
Tesi
Doctorat
Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut
2013
Universitat Pompeu Fabra. Departament de Ciències Experimentals i de la Salut
Tesis i dissertacions electròniques
Notredame, Cedric,
supervisor acadèmic
TDX
In recent years there has been a growing interest in the field of non-coding RNA. This surge is a direct consequence of the discovery of a huge number of new non-coding genes, and of the finding that many of these transcripts are involved in key cellular functions. In this context, accurately detecting and comparing RNA sequences becomes extremely important. Aligning nucleotide sequences is one of the main requisite when searching for homologous genes. Accurate alignments reveal evolutionary relationships, conserved regions and more generally, any biologically relevant pattern. Comparing RNA molecules is, however, a challenging task. The nucleotide alphabet is simpler and therefore less informative than that of proteins. Moreover for many non-coding RNAs, evolution is likely to be mostly constrained at the structure level and not on the sequence level. This results in a very poor sequence conservation impeding the comparison of these molecules. These difficulties define a context where new methods are urgently needed in order to exploit experimental results at their full potential.
These are the issues I have tried to address in my PhD. I have started by developing a novel algorithm able to reveal the homology relationship of distantly related ncRNA genes, and then I have applied the approach thus defined in combination with other sophisticated data mining tools to discover novel non-coding genes and generate genome-wide ncRNA predictions.
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