↓ Skip to main content

Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection

Overview of attention for article published in BMC Bioinformatics, February 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 X users

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
70 Mendeley
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
Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0957-1
Pubmed ID
Authors

Qi Zhang, Xin Zeng, Sam Younkin, Trupti Kawli, Michael P. Snyder, Sündüz Keleş

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies allow these experiments to be carried out with short (36-50 bps), long (75-100 bps), single-end, or paired-end reads, the impact of these read parameters on the downstream data analysis are not well understood. In this paper, we evaluate the effects of different read parameters on genome sequence alignment, coverage of different classes of genomic features, peak identification, and allele-specific binding detection. We generated 101 bps paired-end ChIP-seq data for many transcription factors from human GM12878 and MCF7 cell lines. Systematic evaluations using in silico variations of these data as well as fully simulated data, revealed complex interplay between the sequencing parameters and analysis tools, and indicated clear advantages of paired-end designs in several aspects such as alignment accuracy, peak resolution, and most notably, allele-specific binding detection. Our work elucidates the effect of design on the downstream analysis and provides insights to investigators in deciding sequencing parameters in ChIP-seq experiments. We present the first systematic evaluation of the impact of ChIP-seq designs on allele-specific binding detection and highlights the power of pair-end designs in such studies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Italy 1 1%
Korea, Republic of 1 1%
Unknown 66 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 24%
Student > Ph. D. Student 16 23%
Researcher 14 20%
Professor 3 4%
Other 2 3%
Other 6 9%
Unknown 12 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 30%
Agricultural and Biological Sciences 20 29%
Computer Science 5 7%
Immunology and Microbiology 2 3%
Mathematics 2 3%
Other 5 7%
Unknown 15 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 February 2016.
All research outputs
#14,251,396
of 22,852,911 outputs
Outputs from BMC Bioinformatics
#4,730
of 7,292 outputs
Outputs of similar age
#156,898
of 298,866 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 144 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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 298,866 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 144 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.