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. 2010;11(2):R19.
doi: 10.1186/gb-2010-11-2-r19. Epub 2010 Feb 15.

Evidence-ranked motif identification

Stoyan Georgiev  1 , Alan P Boyle, Karthik Jayasurya, Xuan Ding, Sayan Mukherjee, Uwe Ohler
Affiliations

V体育ios版 - Evidence-ranked motif identification

Stoyan Georgiev et al. Genome Biol. 2010.

Abstract (V体育官网)

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 VSports手机版. 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. .

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VSports在线直播 - Figures

Figure 1
Figure 1
cERMIT motif discovery algorithm. cERMIT starts with all possible 5-mer seeds and proceeds by independently 'evolving' each seed by increasing the enrichment of target sequences in the top of the evidence ranked list.
Figure 2
Figure 2
Motif discovery pipeline. Pipeline for motif discovery based on genome-wide evidence of regulation. Sequence reads are aligned to the reference genome and peak calling is executed to produce a set of putative regulatory regions (for example, DNaseI peaks) and corresponding evidence of regulation (for example, ChIP-seq peaks). As a final step in the pipeline, cERMIT is run on the preprocessed data to produce motif predictions that are best supported by the observed experimental evidence E.
Figure 3
Figure 3
Human ChIP-seq motif predictions. Motif predictions of cERMIT on six human ChIP-seq datasets: STAT1 [35], the insulator binding protein CTCF [36], SRF, GABP [37], FoxA1 [38], and NRSF [39]. The 'ensemble' column includes results from using the ensemble of all six datasets to define the space of regulatory regions (see text). The 'DNaseI' column includes cERMIT predictions when using open chromatin regions, as defined by DNaseI peaks, to be the set of putative regulatory regions. Literature position-specific scoring matrices (PSSMs) were extracted from TRANSFAC 2009.1. Asterisks indicate the optimal alignment of motif prediction to literature. CTCF, due to its ubiquitous binding, was recovered using the top 25,000 DNase peaks as input to cERMIT. All other datasets consider the top 5,000 peaks from each factor (in the two different scenarios).
Figure 4
Figure 4
Mouse ChIP-seq mouse. Motif predictions of cERMIT on mouse ChIP-seq data from [40]. The predictions of cERMIT use the 'ensemble' approach to define the set of putative regulatory regions (see text for details). Literature position-specific scoring matrices (PSSMs) were extracted from TRANSFAC 2009.1, except for CTCF [45], Klf4 [57], and Zfx (unknown). Asterisks are used to indicate the optimal alignment of motif prediction to literature. Each individual factor contributes (the top scoring) 5,000 peaks to the ensemble set of putative regulatory regions.
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