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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Evidence-ranked motif identification. | Georgiev et al. Genome Biology 2010 11 R19 http genomebiology.eom 2010 11 2 R19 Genome Biology METHOD Open Access Evidence-ranked motif identification Stoyan Georgiev1 2 Alan P Boyle1 2 Karthik Jayasurya1 2 Xuan Ding2 Sayan Mukherjee2 3 4 5 Uwe Ohler2 3 6 Abstract cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions. Background With the continuing growth and scale-up of genome and transcriptome sequencing of a large number of eukaryotes there has been increasing interest in gaining a better understanding of the functional connections between all the genes within a complex organism. Regulatory factors that control the activation or repression of a gene on the transcriptional or post-transcriptional level often recognize specific DNA or RNA sequence elements. One of the first steps towards understanding the functional characteristics of regulators such as transcription factors TFs is to obtain accurate representations of their preferred binding sites and the location of their occurrences which can then be utilized to identify candidate genes under direct regulatory influence of a TF. Regulatory elements tend to be short about 6 to 15 bp in eukaryotes and often highly degenerate which makes it difficult to distinguish them from the surrounding sequence which is orders of magnitude larger in size 1-3 . The task to identify a representation for a functional sequence element is commonly referred to as de novo motif finding. The motif finding problem has been traditionally phrased as the following Given
