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Peak shape clustering reveals biological insights

Overview of attention for article published in BMC Bioinformatics, October 2015
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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8 X users

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57 Mendeley
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 57 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 54 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Student > Bachelor 8 14%
Researcher 7 12%
Student > Doctoral Student 6 11%
Student > Master 6 11%
Other 5 9%
Unknown 10 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 32%
Biochemistry, Genetics and Molecular Biology 17 30%
Computer Science 4 7%
Psychology 1 2%
Social Sciences 1 2%
Other 4 7%
Unknown 12 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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
#7,389,435
of 25,464,544 outputs
Outputs from BMC Bioinformatics
#2,660
of 7,704 outputs
Outputs of similar age
#85,946
of 295,503 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 158 outputs
Altmetric has tracked 25,464,544 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,704 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 64% 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 295,503 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 69% of its contemporaries.
We're also able to compare this research output to 158 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.