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PlasmoTFBM: An Intelligent Queriable Database for Predicted Transcription Factor Binding Motifs in Plasmodium falciparum

  • Chengyong Yang
  • Erliang Zeng
  • Kalai Mathee
  • Giri Narasimhan

Abstract

There is very little information available with regard to gene regulatory circuitries in Plasmodium falciparum. In an attempt to discover transcription factor binding motifs (TFBMs) in P. falciparum, we considered two approaches. In the first approach, gene expression data from asexual intraerythrocytic developmental cycle generated every hour for 48 hour post-infection were fed into the ISA (Iterative Signature Algorithm), which outputs modules composed of sets of genes associated with co-regulating conditions. Putative TFBMs were discovered by applying the AlignACE program on the resulting gene sets. In the second approach, the MotifRegressor program was used to predict potential motifs associated with induced and repressed genes for each time point and then clustered based on the strength of their correlation to the gene expression (i.e., motif coefficients) across different time points. A total of 637 and 840 putative motifs were predicted by the MotifRegressor and ISA-AlignACE programs, respectively. All this information was uploaded into a database, thus making it easy to devise complex queries. Using published information on known motifs, we were able to validate some of our results. In addition, modules consisting of putative transcription factors and related genes were also investigated. This work provides a bioinformatics methodology to analyze transcription regulation and TFBMs across the whole genome. By constructing a comprehensive relational database and an intelligent, user-friendly query system, biologically meaningful conclusions can be drawn easily even by an investigator with no prior knowledge of databases.

Keywords

transcription factor regulatory elements motifs G-box SPE elements CPE elements var genes heat shock protein SERA EBA140 Plasmodium falciparum 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Chengyong Yang
    • 1
  • Erliang Zeng
    • 1
  • Kalai Mathee
    • 2
  • Giri Narasimhan
    • 1
  1. 1.Bioinformatics Research Group (BioRG), School of Computing and Informations SciencesFlorida International UniversityMiamiUSA
  2. 2.Department of Biological SciencesFlorida International UniversityMiamiUSA

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