An accurate identification of gene promoters remains an important challenge. Computational approaches for this problem rely on promoter sequence attributes that are believed to be critical for transcription initiation. Here we report a probabilistic model that captures two important properties of promoters, not used by previous methods, viz., the location preference and co-occurrence of promoter elements. Additionally, we found that many of the position-specific DNA elements are strongly linked with the function of the gene product. For instance, a highly conserved motif CCTTT at -1 position is strongly associated with protein synthesis, cellular and tissue development. Our comparative analysis of promoter classes reveals that the promoters devoid of CpG islands are more conserved and have fewer alternative transcription start sites. The discovered links between promoter elements and gene function allows us to infer genetic networks from promoter elements. The web server for the PSPA promoter predictor is available at /PSPA.