Controlling the differential expression of many thousands genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks. Cis-Regulatory Modules are fundamental units of such networks. To detect Cis-Regulatory Modules “in-silico” a key step is the discovery of recurrent clusters of DNA binding sites for sets of cooperating Transcription Factors. Composite motif is the term often adopted to refer to these clusters of sites. In this paper we describe CMF, a new efficient combinatorial method for the problem of detecting composite motifs, given in input a description of the binding affinities for a set of transcription factors. Testing with known benchmark data, we attain statistically significant better performance against nine state-of-the-art competing methods.
CMF: a Combinatorial Tool to Find Composite Motifs / Leoncini, Mauro; Montangero, Manuela; M., Pellegrini; PANUCIA TILLAN, Karina. - STAMPA. - 7997:(2013), pp. 196-208. (Intervento presentato al convegno 7th International Conference on Learning and Intelligent Optimization, LION 7 tenutosi a Catania, ita nel 7-11 Gennaio 2013) [10.1007/978-3-642-44973-4_21].
CMF: a Combinatorial Tool to Find Composite Motifs
LEONCINI, Mauro;MONTANGERO, Manuela;PANUCIA TILLAN, Karina
2013
Abstract
Controlling the differential expression of many thousands genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks. Cis-Regulatory Modules are fundamental units of such networks. To detect Cis-Regulatory Modules “in-silico” a key step is the discovery of recurrent clusters of DNA binding sites for sets of cooperating Transcription Factors. Composite motif is the term often adopted to refer to these clusters of sites. In this paper we describe CMF, a new efficient combinatorial method for the problem of detecting composite motifs, given in input a description of the binding affinities for a set of transcription factors. Testing with known benchmark data, we attain statistically significant better performance against nine state-of-the-art competing methods.File | Dimensione | Formato | |
---|---|---|---|
paper44-final.pdf
Accesso riservato
Tipologia:
Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione
159.3 kB
Formato
Adobe PDF
|
159.3 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris