↓ Skip to main content

Peak shape clustering reveals biological insights

Overview of attention for article published in BMC Bioinformatics, October 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
48 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Peak shape clustering reveals biological insights
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0787-6
Pubmed ID
Authors

Marzia A. Cremona, Laura M. Sangalli, Simone Vantini, Gaetano I. Dellino, Pier Giuseppe Pelicci, Piercesare Secchi, Laura Riva

Abstract

ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently restricted to the evaluation of signal intensity and the detection of enriched regions (peaks) in the genome. Other features of peak shape are almost always neglected, despite the remarkable differences shown by ChIP-seq for different proteins, as well as by distinct regions in a single experiment. We hypothesize that statistically significant differences in peak shape might have a functional role and a biological meaning. Thus, we design five indices able to summarize peak shapes and we employ multivariate clustering techniques to divide peaks into groups according to both their complexity and the intensity of their coverage function. In addition, our novel analysis pipeline employs a range of statistical and bioinformatics techniques to relate the obtained peak shapes to several independent genomic datasets, including other genome-wide protein-DNA maps and gene expression experiments. To clarify the meaning of peak shape, we apply our methodology to the study of the erythroid transcription factor GATA-1 in K562 cell line and in megakaryocytes. Our study demonstrates that ChIP-seq profiles include information regarding the binding of other proteins beside the one used for precipitation. In particular, peak shape provides new insights into cooperative transcriptional regulation and is correlated to gene expression.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 4%
United States 1 2%
Unknown 45 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Student > Bachelor 7 15%
Student > Doctoral Student 6 13%
Researcher 6 13%
Student > Master 5 10%
Other 3 6%
Unknown 7 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 35%
Agricultural and Biological Sciences 16 33%
Computer Science 3 6%
Social Sciences 1 2%
Medicine and Dentistry 1 2%
Other 3 6%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 09 November 2015.
All research outputs
#2,674,441
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#1,251
of 4,195 outputs
Outputs of similar age
#62,903
of 250,479 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 145 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 69% of its peers.
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 250,479 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.