Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques. Genome Res

Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland 20878, USA.
Genome Research (Impact Factor: 13.85). 01/2007; 16(12):1455-64. DOI: 10.1101/gr.4140006
Source: PubMed

ABSTRACT Fields such as genomics and systems biology are built on the synergism between computational and experimental techniques. This type of synergism is especially important in accomplishing goals like identifying all functional transcription factor binding sites in vertebrate genomes. Precise detection of these elements is a prerequisite to deciphering the complex regulatory networks that direct tissue specific and lineage specific patterns of gene expression. This review summarizes approaches for in silico, in vitro, and in vivo identification of transcription factor binding sites. A variety of techniques useful for localized- and high-throughput analyses are discussed here, with emphasis on aspects of data generation and verification.

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Available from: Peggy J Farnham, Dec 18, 2013
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    • " many of these motifs , they can be observed at thousands of places in the genome based on random permutation , thus leading to many false positive predictions . The second disadvantage is that , even if a sequence motif actually corresponds to a CRE , this does not convey information about the activity level of the CRE in a particular cell type ( Elnitski et al . , 2006 ) . The recently developed ChIP - seq technology allows us to address both these shortcomings by exploiting the second characteristic of CRE , which is the marked absence of nucleosomes in these regions ( Mathelier et al . , 2015 ) ( Figures 1B , 3 ) . When inactive , the genomic region corresponding to a CRE is packed into nucleosomes "
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    Frontiers in Genetics 05/2015; 6:188. DOI:10.3389/fgene.2015.00188
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    • "Recently, several algorithms have been developed to identify DNA motifs in a given set of sequences and to determine if they are over-represented compared to that expected by chance [33]. The integration of these computational analyses with experimental techniques is becoming fundamental to identify genome-scale regulatory elements [35], [36], [37]. Examples of recent studies using motif analysis at a genomic scale include genome-wide identification of estrogen receptor binding sites [38], identification of CTCF-binding sites in the human genome [39] and identification of motifs associated with aberrant CpG island methylation [40]. "
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    PLoS ONE 06/2014; 9(6):e100194. DOI:10.1371/journal.pone.0100194 · 3.23 Impact Factor
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    • "However, the data obtained in every ChIP-seq experiment demonstrate TF binding specific to cell line or tissue used and strongly dependant on environmental conditions [37]; [38]; [39]. Moreover, the loss of some tissue-specific features (for example as a result of cell immortalization) or different environmental changes may alter the genome wide pattern of TF binding inherent to differentiated cells of living organism [33]; [40]. Since it is known that specific spatial-temporal patterns of gene expression is controlled by combinatorial binding of different TF sets to regulatory units [27]; [41]; [42] it seems promising to search for these regions by integrative analysis of as many as possible of different ChIP-seq data. "
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    PLoS ONE 10/2013; 8(10):e78833. DOI:10.1371/journal.pone.0078833 · 3.23 Impact Factor
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