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D. Fusco

Publications and source records attributed to D. Fusco.

2 recordsLinked to original sources

Identity and divergence of protein domain architectures after the Yeast Whole Genome Duplication event

Analyzing the properties of duplicate genes during evolution is useful to understand the development of new cell functions. The yeast S. cerevisiae is a useful testing ground for this problem, because its duplicated genes with different evolutionary birth and destiny are well distinguishable. In particular, there is a clear detection for the occurrence of a Whole Genome Duplication (WGD) event in S. cerevisiae, and the genes derived from this event (WGD paralogs) are known. We studied WGD and non-WGD duplicates by two parallel analysis based on structural protein domains and on Gene Ontology annotation scheme respectively. The results show that while a large number of ``duplicable'' structural domains is shared in local and global duplications, WGD and non-WGD paralogs tend to have different functions. The reason for this is the existence of WGD and non-WGD specific domains with largely different functions. In agreement with the recent findings of Wapinski and collaborators (Nature 449, 2007), WGD paralogs often perform ``core'' cell functions, such as translation and DNA replication, while local duplications associate with ``peripheral'' functions such as response to stress. Our results also support the fact that domain architectures are a reliable tool to detect homology, as the domains of duplicates are largely invariant with date and nature of the duplication, while their sequences and also their functions might migrate.

q-bio.GN

DIA-MCIS. An Importance Sampling Network Randomizer for Network Motif Discovery and Other Topological Observables in Transcription Networks

Transcription networks, and other directed networks can be characterized by some topological observables such as for example subgraph occurrence (network motifs). In order to perform such kind of analysis, it is necessary to be able to generate suitable randomized network ensembles. Typically, one considers null networks with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Montecarlo developed by Chen et al. [1].

q-bio.QM