↓ Skip to main content

A comparative study of ChIP-seq sequencing library preparation methods

Overview of attention for article published in BMC Genomics, October 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
26 Dimensions

Readers on

mendeley
109 Mendeley
citeulike
2 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
A comparative study of ChIP-seq sequencing library preparation methods
Published in
BMC Genomics, October 2016
DOI 10.1186/s12864-016-3135-y
Pubmed ID
Authors

Arvind Y. M. Sundaram, Timothy Hughes, Shea Biondi, Nathalie Bolduc, Sarah K. Bowman, Andrew Camilli, Yap C. Chew, Catherine Couture, Andrew Farmer, John P. Jerome, David W. Lazinski, Andrew McUsic, Xu Peng, Kamran Shazand, Feng Xu, Robert Lyle, Gregor D. Gilfillan

Abstract

ChIP-seq is the primary technique used to investigate genome-wide protein-DNA interactions. As part of this procedure, immunoprecipitated DNA must undergo "library preparation" to enable subsequent high-throughput sequencing. To facilitate the analysis of biopsy samples and rare cell populations, there has been a recent proliferation of methods allowing sequencing library preparation from low-input DNA amounts. However, little information exists on the relative merits, performance, comparability and biases inherent to these procedures. Notably, recently developed single-cell ChIP procedures employing microfluidics must also employ library preparation reagents to allow downstream sequencing. In this study, seven methods designed for low-input DNA/ChIP-seq sample preparation (Accel-NGS® 2S, Bowman-method, HTML-PCR, SeqPlex™, DNA SMART™, TELP and ThruPLEX®) were performed on five replicates of 1 ng and 0.1 ng input H3K4me3 ChIP material, and compared to a "gold standard" reference PCR-free dataset. The performance of each method was examined for the prevalence of unmappable reads, amplification-derived duplicate reads, reproducibility, and for the sensitivity and specificity of peak calling. We identified consistent high performance in a subset of the tested reagents, which should aid researchers in choosing the most appropriate reagents for their studies. Furthermore, we expect this work to drive future advances by identifying and encouraging use of the most promising methods and reagents. The results may also aid judgements on how comparable are existing datasets that have been prepared with different sample library preparation reagents.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 109 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 28%
Student > Ph. D. Student 27 25%
Student > Doctoral Student 9 8%
Student > Master 8 7%
Other 6 6%
Other 15 14%
Unknown 14 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 40 37%
Agricultural and Biological Sciences 35 32%
Immunology and Microbiology 4 4%
Computer Science 3 3%
Medicine and Dentistry 2 2%
Other 8 7%
Unknown 17 16%
Attention Score in Context

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 24 October 2016.
All research outputs
#12,968,953
of 22,893,031 outputs
Outputs from BMC Genomics
#4,575
of 10,670 outputs
Outputs of similar age
#157,313
of 316,323 outputs
Outputs of similar age from BMC Genomics
#86
of 226 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,670 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% 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 316,323 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 226 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 61% of its contemporaries.