AMD，一种使用有缺口共识逐步细化的自动化基序发现工具。
AMD, an automated motif discovery tool using stepwise refinement of gapped consensuses.
机构信息
Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
出版信息
PLoS One. 2011;6(9):e24576. doi: 10.1371/journal.pone.0024576. Epub 2011 Sep 12.
Abstract
Motif discovery is essential for deciphering regulatory codes from high throughput genomic data, such as those from ChIP-chip/seq experiments. However, there remains a lack of effective and efficient methods for the identification of long and gapped motifs in many relevant tools reported to date. We describe here an automated tool that allows for de novo discovery of transcription factor binding sites, regardless of whether the motifs are long or short, gapped or contiguous.
摘要
基序发现对于从高通量基因组数据（如 ChIP-chip/seq 实验）中破译调控代码至关重要。然而，在迄今为止报道的许多相关工具中，仍然缺乏有效和高效的方法来识别长的和有缺口的基序。我们在这里描述了一种自动化工具，它允许从头发现转录因子结合位点，无论基序是长的还是短的，有缺口的还是连续的。
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