| Title |
QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions
|
|---|---|
| Published in |
BMC Genomics, December 2013
|
| DOI | 10.1186/1471-2164-14-s8-s3 |
| Pubmed ID | |
| Authors |
Bin Liu, Jimmy Yi, Aishwarya SV, Xun Lan, Yilin Ma, Tim HM Huang, Gustavo Leone, Victor X Jin |
| Abstract |
Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 3% |
| Unknown | 30 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Researcher | 5 | 16% |
| Student > Master | 4 | 13% |
| Student > Bachelor | 3 | 10% |
| Student > Doctoral Student | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 55% |
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Computer Science | 2 | 6% |
| Unspecified | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 4 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 November 2014.
All research outputs
#20,242,136
of 22,769,322 outputs
Outputs from BMC Genomics
#9,265
of 10,639 outputs
Outputs of similar age
#267,254
of 307,100 outputs
Outputs of similar age from BMC Genomics
#378
of 450 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,639 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 307,100 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 450 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.