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Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information

Overview of attention for article published in BMC Genomics, May 2016
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  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

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6 tweeters

Citations

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3 Dimensions

Readers on

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27 Mendeley
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Title
Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information
Published in
BMC Genomics, May 2016
DOI 10.1186/s12864-016-2682-6
Pubmed ID
Authors

Martin Nettling, Hendrik Treutler, Jesus Cerquides, Ivo Grosse

Abstract

Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. ChIP-seq has become the major technology to uncover genomic regions containing those binding sites, but motifs predicted by traditional computational approaches using these data are distorted by a ubiquitous binding-affinity bias. Here, we present an approach for detecting and correcting this bias using inter-species information. We find that the binding-affinity bias caused by the ChIP-seq experiment in the reference species is stronger than the indirect binding-affinity bias in orthologous regions from phylogenetically related species. We use this difference to develop a phylogenetic footprinting model that is capable of detecting and correcting the binding-affinity bias. We find that this model improves motif prediction and that the corrected motifs are typically softer than those predicted by traditional approaches. These findings indicate that motifs published in databases and in the literature are artificially sharpened compared to the native motifs. These findings also indicate that our current understanding of transcriptional gene regulation might be blurred, but that it is possible to advance this understanding by taking into account inter-species information available today and even more in the future.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 4%
Hungary 1 4%
Germany 1 4%
Unknown 24 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 37%
Student > Master 5 19%
Student > Ph. D. Student 4 15%
Student > Bachelor 2 7%
Professor > Associate Professor 2 7%
Other 3 11%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 41%
Biochemistry, Genetics and Molecular Biology 6 22%
Computer Science 4 15%
Engineering 2 7%
Immunology and Microbiology 1 4%
Other 1 4%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 May 2016.
All research outputs
#1,872,223
of 7,693,543 outputs
Outputs from BMC Genomics
#1,726
of 5,574 outputs
Outputs of similar age
#86,485
of 268,214 outputs
Outputs of similar age from BMC Genomics
#68
of 200 outputs
Altmetric has tracked 7,693,543 research outputs across all sources so far. This one has received more attention than most of these and is in the 62nd percentile.
So far Altmetric has tracked 5,574 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 63% 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 268,214 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 66% of its contemporaries.
We're also able to compare this research output to 200 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 64% of its contemporaries.