Motif-Based Method for the Genome-Wide Prediction of Eukaryotic Gene Clusters

  • Thomas Wolf
  • Vladimir Shelest
  • Ekaterina Shelest
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)

Abstract

Genomic clustering of functionally interrelated genes is not unusual in eukaryotes. In such clusters, co-localized genes are co-regulated and often belong to the same pathway. However, biochemical details are still unknown in many cases, hence computational prediction of clusters’ structures is beneficial for understanding their functions. Yet, in silico detection of eukaryotic gene clusters (eGCs) remains a challenging task. We suggest a novel method for eGC detection based on consideration of cluster-specific regulatory patterns. The basic idea is to differentiate cluster from non-cluster genes by regulatory elements within their promoter sequences using the density of cluster-specific motifs’ occurrences (which is higher within the cluster region) as an additional distinguishing feature. The effectiveness of the method was demonstrated by successful re-identification of functionally characterized clusters. It is also applicable to the detection of yet unknown eGCs. Additionally, the method provides valuable information about the binding sites for cluster-specific regulators.

Keywords

eukaryotic gene clusters transcription regulation secondary metabolites transcription factor binding sites 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Thomas Wolf
    • 1
  • Vladimir Shelest
    • 1
  • Ekaterina Shelest
    • 1
  1. 1.Leibniz Institute for Natural Product Research and Infection Biology e. V., Research group Systems Biology /BioinformaticsHans-Knöll-Institute (HKI)JenaGermany

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