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Identifying differential transcription factor binding in ChIP-seq

Overview of attention for article published in Frontiers in Genetics, April 2015
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  • 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 (65th percentile)

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Title
Identifying differential transcription factor binding in ChIP-seq
Published in
Frontiers in Genetics, April 2015
DOI 10.3389/fgene.2015.00169
Pubmed ID
Authors

Dai-Ying Wu, Danielle Bittencourt, Michael R. Stallcup, Kimberly D. Siegmund

Abstract

ChIP seq is a widely used assay to measure genome-wide protein binding. The decrease in costs associated with sequencing has led to a rise in the number of studies that investigate protein binding across treatment conditions or cell lines. In addition to the identification of binding sites, new studies evaluate the variation in protein binding between conditions. A number of approaches to study differential transcription factor binding have recently been developed. Several of these methods build upon established methods from RNA-seq to quantify differences in read counts. We compare how these new approaches perform on different data sets from the ENCODE project to illustrate the impact of data processing pipelines under different study designs. The performance of normalization methods for differential ChIP-seq depends strongly on the variation in total amount of protein bound between conditions, with total read count outperforming effective library size, or variants thereof, when a large variation in binding was studied. Use of input subtraction to correct for non-specific binding showed a relatively modest impact on the number of differential peaks found and the fold change accuracy to biological validation, however a larger impact might be expected for samples with more extreme copy number variations between them. Still, it did identify a small subset of novel differential regions while excluding some differential peaks in regions with high background signal. These results highlight proper scaling for between-sample data normalization as critical for differential transcription factor binding analysis and suggest bioinformaticians need to know about the variation in level of total protein binding between conditions to select the best analysis method. At the same time, validation using fold-change estimates from qRT-PCR suggests there is still room for further method improvement.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 1%
United States 2 1%
Australia 1 <1%
Canada 1 <1%
Italy 1 <1%
Unknown 142 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 28%
Researcher 27 18%
Student > Master 20 13%
Student > Bachelor 14 9%
Professor > Associate Professor 9 6%
Other 19 13%
Unknown 19 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 40%
Biochemistry, Genetics and Molecular Biology 37 25%
Medicine and Dentistry 7 5%
Computer Science 6 4%
Immunology and Microbiology 4 3%
Other 13 9%
Unknown 23 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 07 August 2019.
All research outputs
#5,648,169
of 22,800,560 outputs
Outputs from Frontiers in Genetics
#1,614
of 11,762 outputs
Outputs of similar age
#66,357
of 264,547 outputs
Outputs of similar age from Frontiers in Genetics
#38
of 111 outputs
Altmetric has tracked 22,800,560 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,762 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 86% 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 264,547 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 111 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 65% of its contemporaries.