Title |
ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking
|
---|---|
Published in |
BMC Bioinformatics, June 2013
|
DOI | 10.1186/1471-2105-14-188 |
Pubmed ID | |
Authors |
George Wu, Hongkai Ji |
Abstract |
ChIPx (i.e., ChIP-seq and ChIP-chip) is increasingly used to map genome-wide transcription factor (TF) binding sites. A single ChIPx experiment can identify thousands of TF bound genes, but typically only a fraction of these genes are functional targets that respond transcriptionally to perturbations of TF expression. To identify promising functional target genes for follow-up studies, researchers usually collect gene expression data from TF perturbation experiments to determine which of the TF targets respond transcriptionally to binding. Unfortunately, approximately 40% of ChIPx studies do not have accompanying gene expression data from TF perturbation experiments. For these studies, genes are often prioritized solely based on the binding strengths of ChIPx signals in order to choose follow-up candidates. ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED). |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 17% |
Luxembourg | 1 | 17% |
United States | 1 | 17% |
Norway | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 67% |
Scientists | 2 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 2% |
France | 1 | 2% |
Hong Kong | 1 | 2% |
Australia | 1 | 2% |
United Kingdom | 1 | 2% |
Denmark | 1 | 2% |
Unknown | 49 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 17 | 31% |
Student > Ph. D. Student | 11 | 20% |
Student > Master | 10 | 18% |
Professor > Associate Professor | 5 | 9% |
Student > Doctoral Student | 4 | 7% |
Other | 5 | 9% |
Unknown | 3 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 28 | 51% |
Biochemistry, Genetics and Molecular Biology | 13 | 24% |
Computer Science | 4 | 7% |
Medicine and Dentistry | 4 | 7% |
Immunology and Microbiology | 1 | 2% |
Other | 2 | 4% |
Unknown | 3 | 5% |