TAILIEUCHUNG - Báo cáo sinh học: " GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge. | Carvalho and Oliveira Algorithms for Molecular Biology 2011 6 13 http content 6 1 13 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access GRISOTTO A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge Alexandra M Carvalho1 and Arlindo L Oliveira2 Abstract Background Position-specific priors PSP have been used with success to boost EM and Gibbs sampler-based motif discovery algorithms. PSP information has been computed from different sources including orthologous conservation DNA duplex stability and nucleosome positioning. The use of prior information has not yet been used in the context of combinatorial algorithms. Moreover priors have been used only independently and the gain of combining priors from different sources has not yet been studied. Results We extend RISOTTO a combinatorial algorithm for motif discovery by post-processing its output with a greedy procedure that uses prior information. PSP s from different sources are combined into a scoring criterion that guides the greedy search procedure. The resulting method called GRISOTTO was evaluated over 156 yeast TF ChIP-chip sequence-sets commonly used to benchmark prior-based motif discovery algorithms. Results show that GRISOTTO is at least as accurate as other twelve state-of-the-art approaches for the same task even without combining priors. Furthermore by considering combined priors GRISOTTO is considerably more accurate than the state-of-the-art approaches for the same task. We also show that PSP s improve GRISOTTO ability to retrieve motifs from mouse ChiP-seq data indicating that the proposed algorithm can be applied to data from a different technology and for a higher eukaryote. Conclusions The conclusions of this work are twofold. First post-processing the output of combinatorial algorithms by incorporating prior information leads to a very efficient and effective motif discovery method. Second combining priors from different sources is .

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