PMC:1524773 / 8542-9836 JSONTXT

{"project":"2_test","denotations":[{"id":"16737529-15608167-1692871","span":{"begin":838,"end":840},"obj":"15608167"}],"text":"The result of the algorithm can then be used to cluster the data in an automated and objective fashion. We are facing two problems here: First, the number of maximal cliques is strongly depending on the chosen c-value. Second, for many instances most of the sequences are elements of more than one maximal clique. Thus, we will give a heuristic based on the set of all maximal cliques that is able to find a reasonable clustering. We illustrate the algorithm using artificial as well as three different biological examples. The first of the biological data sets is an example of a repeat analysis, where a transposon from the nematode Pristionchus pacificus is compared with reads of BAC clone data covering about half of the genome of that organism. The second and third example are proteins that are compared with the UniProt database [11]. The article is structured as follows: in Section 2 we give a short review on results that are essential for the understanding of the two algorithms together with required definitions. Section 3 presents the first, simpler algorithm, followed by the description of the algorithm with a new, lower runtime complexity for c-max-tolerance graphs in Section 4. Section 5 gives some results on experiments and Section 6 summarizes and discusses our results."}