Title |
De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets
|
---|---|
Published in |
BMC Genomics, December 2014
|
DOI | 10.1186/1471-2164-15-1047 |
Pubmed ID | |
Authors |
Meng Niu, Ehsan S Tabari, Zhengchang Su |
Abstract |
In eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 11% |
United States | 1 | 11% |
Unknown | 7 | 78% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 67% |
Scientists | 2 | 22% |
Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 4% |
France | 2 | 4% |
Unknown | 45 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 33% |
Researcher | 13 | 27% |
Student > Master | 5 | 10% |
Other | 4 | 8% |
Professor | 2 | 4% |
Other | 5 | 10% |
Unknown | 4 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 22 | 45% |
Biochemistry, Genetics and Molecular Biology | 17 | 35% |
Computer Science | 5 | 10% |
Medicine and Dentistry | 2 | 4% |
Veterinary Science and Veterinary Medicine | 1 | 2% |
Other | 1 | 2% |
Unknown | 1 | 2% |