Finding sequence motifs with Bayesian models incorporating positional information: an application to transcription factor bindin

Background: Biologically active sequence motifs often have positional preferences with respect to a genomic landmark. For example, many known transcription factor binding sites (TFBSs) occur within an interval [-300, 0] bases upstream of a transcription start site (TSS). Although some programs for identifying sequence motifs exploit positional information, most of them model it only implicitly and with ad hoc methods, making them unsuitable for general motif searches. Results: A-GLAM, a user-friendly computer program for identifying sequence motifs, now incorporates a Bayesian model systematically combining sequence and positional information. AGLAM's predictions with and without positional information were compared on two human TFBS datasets, each containing sequences corresponding to the interval [-2000, 0] bases upstream of a known TSS. A rigorous statistical analysis showed that positional information significantly improved the prediction of sequence motifs, and an extensive ...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Nak-Kyeong Kim, Kannan Tharakaraman, Leonardo Mariño-Ramírez, John L. Spouge
• Keywords: BMCBI 2008, Positional, Positional Information, Sequence Motifs