Genome-wide analysis of core promoter elements from conserved human and mouse orthologous pairs

• Published in: BMC bioinformatics 2006-03, Vol.7 (1), p.114-114, Article 114
• Main Authors: Jin, Victor X, Singer, Gregory A C, Agosto-Pérez, Francisco J, Liyanarachchi, Sandya, Davuluri, Ramana V
• Format: Article
• Language: English
• Subjects: Animals; Base Sequence; Chromosome Mapping - methods; Conserved Sequence - genetics; Evolution, Molecular; Genome, Human; Humans; Mice; Molecular Sequence Data; Promoter Regions, Genetic - genetics; Sequence Alignment; Sequence Analysis, DNA - methods; Sequence Homology, Nucleic Acid
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-7-114

The canonical core promoter elements consist of the TATA box, initiator (Inr), downstream core promoter element (DPE), TFIIB recognition element (BRE) and the newly-discovered motif 10 element (MTE). The motifs for these core promoter elements are highly degenerate, which tends to lead to a high false discovery rate when attempting to detect them in promoter sequences. In this study, we have performed the first analysis of these core promoter elements in orthologous mouse and human promoters with experimentally-supported transcription start sites. We have identified these various elements using a combination of positional weight matrices (PWMs) and the degree of conservation of orthologous mouse and human sequences--a procedure that significantly reduces the false positive rate of motif discovery. Our analysis of 9,010 orthologous mouse-human promoter pairs revealed two combinations of three-way synergistic effects, TATA-Inr-MTE and BRE-Inr-MTE. The former has previously been putatively identified in human, but the latter represents a novel synergistic relationship. Our results demonstrate that DNA sequence conservation can greatly improve the identification of functional core promoter elements in the human genome. The data also underscores the importance of synergistic occurrence of two or more core promoter elements. Furthermore, the sequence data and results presented here can help build better computational models for predicting the transcription start sites in the promoter regions, which remains one of the most challenging problems.