Motifs discovery is a fundamental task in molecular biology since it is well known that proteins sharing the same motifs belong to the same domains and perform the same cellular activity. Many algorithms have been developed to detect frequent motifs given a set of proteins. However, this information is of limited use: to superimpose a single set of frequent motifs over the whole dataset is often an oversimplification that overlooks important pieces of information. MotifsLinker is a de novo framework allowing one to cluster the input proteins and to associate them with their characteristic motifs. In this way a richer set of motifs can be individuated, each one specialized on a portion of the sequences in the whole dataset.
MotifsLinker
CORDERO, Francesca;VISCONTI, ALESSIA;BOTTA, Marco
2009-01-01
Abstract
Motifs discovery is a fundamental task in molecular biology since it is well known that proteins sharing the same motifs belong to the same domains and perform the same cellular activity. Many algorithms have been developed to detect frequent motifs given a set of proteins. However, this information is of limited use: to superimpose a single set of frequent motifs over the whole dataset is often an oversimplification that overlooks important pieces of information. MotifsLinker is a de novo framework allowing one to cluster the input proteins and to associate them with their characteristic motifs. In this way a richer set of motifs can be individuated, each one specialized on a portion of the sequences in the whole dataset.
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