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Systematic comparison of monoclonal versus polyclonal antibodies for mapping histone modifications by ChIP-seq

Overview of attention for article published in Epigenetics & Chromatin, November 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 536)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

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48 tweeters
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1 Facebook page

Citations

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19 Dimensions

Readers on

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68 Mendeley
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Title
Systematic comparison of monoclonal versus polyclonal antibodies for mapping histone modifications by ChIP-seq
Published in
Epigenetics & Chromatin, November 2016
DOI 10.1186/s13072-016-0100-6
Pubmed ID
Authors

Michele Busby, Catherine Xue, Catherine Li, Yossi Farjoun, Elizabeth Gienger, Ido Yofe, Adrianne Gladden, Charles B. Epstein, Evan M. Cornett, Scott B. Rothbart, Chad Nusbaum, Alon Goren

Abstract

The robustness of ChIP-seq datasets is highly dependent upon the antibodies used. Currently, polyclonal antibodies are the standard despite several limitations: They are non-renewable, vary in performance between lots and need to be validated with each new lot. In contrast, monoclonal antibody lots are renewable and provide consistent performance. To increase ChIP-seq standardization, we investigated whether monoclonal antibodies could replace polyclonal antibodies. We compared monoclonal antibodies that target five key histone modifications (H3K4me1, H3K4me3, H3K9me3, H3K27ac and H3K27me3) to their polyclonal counterparts in both human and mouse cells. Overall performance was highly similar for four monoclonal/polyclonal pairs, including when we used two distinct lots of the same monoclonal antibody. In contrast, the binding patterns for H3K27ac differed substantially between polyclonal and monoclonal antibodies. However, this was most likely due to the distinct immunogen used rather than the clonality of the antibody. Altogether, we found that monoclonal antibodies as a class perform equivalently to polyclonal antibodies for the detection of histone post-translational modifications in both human and mouse. Accordingly, we recommend the use of monoclonal antibodies in ChIP-seq experiments.

Twitter Demographics

The data shown below were collected from the profiles of 48 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 1%
Italy 1 1%
Switzerland 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 25%
Student > Bachelor 13 19%
Researcher 12 18%
Student > Master 5 7%
Student > Doctoral Student 4 6%
Other 6 9%
Unknown 11 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 41%
Agricultural and Biological Sciences 15 22%
Medicine and Dentistry 3 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Chemistry 2 3%
Other 6 9%
Unknown 12 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 03 September 2021.
All research outputs
#1,147,394
of 20,398,654 outputs
Outputs from Epigenetics & Chromatin
#17
of 536 outputs
Outputs of similar age
#26,518
of 312,898 outputs
Outputs of similar age from Epigenetics & Chromatin
#3
of 58 outputs
Altmetric has tracked 20,398,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 536 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has done particularly well, scoring higher than 96% 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 312,898 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.