Evidence-ranked motif identification.


Autoria(s): Georgiev, S; Boyle, AP; Jayasurya, K; Ding, X; Mukherjee, S; Ohler, U
Data(s)

2010

Identificador

http://www.ncbi.nlm.nih.gov/pubmed/20156354

gb-2010-11-2-r19

Genome Biol, 2010, 11 (2), pp. R19 - ?

http://hdl.handle.net/10161/10579

1474-760X

Idioma(s)

ENG

en_US

Relação

Genome Biol

10.1186/gb-2010-11-2-r19

http://hdl.handle.net/10161/4393

10161/4393

Genome biology

Tipo

Journal Article

Cobertura

England

Resumo

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.

Formato

R19 - ?

Palavras-Chave #Animals #Computational Biology #DNA, Fungal #Genome, Human #Genome-Wide Association Study #Humans #Mice #Oligonucleotide Array Sequence Analysis #Sequence Analysis, DNA