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De novo ChIP-seq analysis

Overview of attention for article published in Genome Biology, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)

Mentioned by

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15 X users
facebook
1 Facebook page

Citations

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10 Dimensions

Readers on

mendeley
79 Mendeley
citeulike
5 CiteULike
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Title
De novo ChIP-seq analysis
Published in
Genome Biology, September 2015
DOI 10.1186/s13059-015-0756-4
Pubmed ID
Authors

Xin He, A. Ercument Cicek, Yuhao Wang, Marcel H. Schulz, Hai-Son Le, Ziv Bar-Joseph

Abstract

Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 3%
Germany 1 1%
Sweden 1 1%
United Kingdom 1 1%
Hungary 1 1%
China 1 1%
New Zealand 1 1%
Spain 1 1%
United States 1 1%
Other 0 0%
Unknown 69 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 28%
Student > Ph. D. Student 20 25%
Student > Master 9 11%
Professor 7 9%
Student > Bachelor 5 6%
Other 8 10%
Unknown 8 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 47%
Biochemistry, Genetics and Molecular Biology 18 23%
Computer Science 10 13%
Mathematics 1 1%
Immunology and Microbiology 1 1%
Other 3 4%
Unknown 9 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 08 April 2020.
All research outputs
#4,592,553
of 25,371,288 outputs
Outputs from Genome Biology
#2,726
of 4,467 outputs
Outputs of similar age
#55,712
of 286,056 outputs
Outputs of similar age from Genome Biology
#59
of 83 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 38th percentile – i.e., 38% 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 286,056 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.