Evidence-ranked motif identification

被引：71

作者：

Georgiev, Stoyan ^{[1
,2
]}

Boyle, Alan P. ^{[1
,2
]}

Jayasurya, Karthik ^{[1
,2
]}

Ding, Xuan ^{[1
]}

Mukherjee, Sayan ^{[1
,3
,4
,5
]}

Ohler, Uwe ^{[1
,3
,6
]}

机构：

[1] Duke Univ, Inst Genome Sci & Policy, Durham, NC 27708 USA

[2] Duke Univ, Program Computat Biol & Bioinformat, Durham, NC 27708 USA

[3] Duke Univ, Dept Comp Sci, Durham, NC 27708 USA

[4] Duke Univ, Dept Stat Sci, Durham, NC 27708 USA

[5] Duke Univ, Dept Math, Durham, NC 27708 USA

[6] Duke Univ, Dept Biostat & Bioinformat, Sch Med, Durham, NC 27708 USA

来源：

GENOME BIOLOGY | 2010年 / 11卷 / 02期

关键词：

FACTOR-BINDING SITES; TRANSCRIPTION-FACTOR; REGULATORY ELEMENTS; CHROMATIN-IMMUNOPRECIPITATION; HUMAN GENOME; NONCODING SEQUENCES; DNA MICROARRAYS; DISCOVERY; GENES; SEQ;

D O I：

10.1186/gb-2010-11-2-r19

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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.

引用

页数：17

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