Evaluating deterministic motif significance measures in protein databases

• Type: Article in International Scientific Journal
• Date: 2007

Authors

Ferreira, PG (Author) Other; Azevedo, PJ (Author) Other

Journal

Title: Algorithms for Molecular Biology

Vol. 2

Final page: 16

ISSN: 1748-7188

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 17 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Informatics

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-5Q7

DOI: 10.1186/1748-7188-2-16

Abstract (EN): Background: Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations. Results: From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs. Conclusion: In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20