A fuzzy approach for studying combinatorial regulatory actions of transcription factors in yeast

Authors:
F. Javier Lopez;Carlos Cano;Fernando Garcia;Armando Blanco
Affiliations:
Department of Computer Science and A.I., University of Granada, Granada, Spain;Department of Computer Science and A.I., University of Granada, Granada, Spain;Department of Computer Science and A.I., University of Granada, Granada, Spain;Department of Computer Science and A.I., University of Granada, Granada, Spain
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

Citing 4
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Abstract

Eucaryotic gene control regions consists of a promoter plus regulatory DNA sequences which may appear distant from the gene promoter. Regulatory proteins (called transcription factors, TFs), coordinately bind to these regions and produce the correct gene expression patterns. However, most of previous works which study regulatory modules limit their attention to gene promoters. Taking advantage of the ability of fuzzy techniques to handle imprecision, inherent to TFBSs and regulatory-regions location data, a novel fuzzy approach is developed in this work to study significant co-occurrences of closely located TFBSs in the yeast whole-genome. Hence, we firstly obtained fuzzy groups of closely-located TFBSs in the genome by using a clustering algorithm. Then, a fuzzy frequent itemset mining algorithm was applied over the set of fuzzy groups to get significant co-occurrences of TFs. An integrative analysis using STRING revealed a number of significant TF combinations, many of them agreeing with previously published knowledge.