title: Regulatory link mapping between organisms.
abstract: BACKGROUND
  Identification of gene regulatory networks is useful in understanding gene regulation in any organism. Some regulatory network information has already been determined experimentally for model organisms, but much less has been identified for non-model organisms, and the limited amount of gene expression data available for non-model organisms makes inference of regulatory networks difficult.
  
  
  RESULTS
  This paper proposes a method to determine the regulatory links that can be mapped from a model to a non-model organism. Mapping a regulatory network involves mapping the transcription factors and target genes from one genome to another. In the proposed method, Basic Local Alignment Search Tool (BLAST) and InterProScan are used to map the transcription factors, whereas BLAST along with transcription factor binding site motifs and the GALF-P tool are used to map the target genes. Experiments are performed to map the regulatory network data of S. cerevisiae to A. thaliana and analyze the results. Since limited information is available about gene regulatory network links, gene expression data is used to analyze results. A set of rules are defined on the gene expression experiments to identify the predicted regulatory links that are well supported.
  
  
  CONCLUSIONS
  Combining transcription factors mapped using BLAST and subfamily classification, together with target genes mapped using BLAST and binding site motifs, produced the best regulatory link predictions. More than two-thirds of these predicted regulatory links that were analyzed using gene expression data have been verified as correctly mapped regulatory links in the target genome.
authors.affiliation: Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada. rachita.sharma@unb.ca
authors.forename: Rachita
authors.initials: R
authors.lastname: Sharma
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Evans
authors.forename: Virendrakumar C
authors.initials: VC
authors.lastname: Bhavsar
mesh_terms: D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D005656:Fungal Proteins / Q000378:metabolism; D053263:Gene Regulatory Networks; D023281:Genomics; D010940:Plant Proteins / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D005656:Fungal Proteins; D010940:Plant Proteins; D016601:RNA-Binding Proteins; D014157:Transcription Factors; C415086:p67 protein, Raphanus sativus