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ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data

Overview of attention for article published in BMC Genomics, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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35 X users
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1 Google+ user

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277 Mendeley
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Title
ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data
Published in
BMC Genomics, March 2018
DOI 10.1186/s12864-018-4559-3
Pubmed ID
Authors

Jianhong Ou, Haibo Liu, Jun Yu, Michelle A. Kelliher, Lucio H. Castilla, Nathan D. Lawson, Lihua Julie Zhu

Abstract

ATAC-seq (Assays for Transposase-Accessible Chromatin using sequencing) is a recently developed technique for genome-wide analysis of chromatin accessibility. Compared to earlier methods for assaying chromatin accessibility, ATAC-seq is faster and easier to perform, does not require cross-linking, has higher signal to noise ratio, and can be performed on small cell numbers. However, to ensure a successful ATAC-seq experiment, step-by-step quality assurance processes, including both wet lab quality control and in silico quality assessment, are essential. While several tools have been developed or adopted for assessing read quality, identifying nucleosome occupancy and accessible regions from ATAC-seq data, none of the tools provide a comprehensive set of functionalities for preprocessing and quality assessment of aligned ATAC-seq datasets. We have developed a Bioconductor package, ATACseqQC, for easily generating various diagnostic plots to help researchers quickly assess the quality of their ATAC-seq data. In addition, this package contains functions to preprocess aligned ATAC-seq data for subsequent peak calling. Here we demonstrate the utilities of our package using 25 publicly available ATAC-seq datasets from four studies. We also provide guidelines on what the diagnostic plots should look like for an ideal ATAC-seq dataset. This software package has been used successfully for preprocessing and assessing several in-house and public ATAC-seq datasets. Diagnostic plots generated by this package will facilitate the quality assessment of ATAC-seq data, and help researchers to evaluate their own ATAC-seq experiments as well as select high-quality ATAC-seq datasets from public repositories such as GEO to avoid generating hypotheses or drawing conclusions from low-quality ATAC-seq experiments. The software, source code, and documentation are freely available as a Bioconductor package at https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 277 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 277 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 27%
Researcher 63 23%
Student > Master 24 9%
Student > Bachelor 20 7%
Student > Doctoral Student 14 5%
Other 24 9%
Unknown 57 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 94 34%
Agricultural and Biological Sciences 69 25%
Immunology and Microbiology 10 4%
Medicine and Dentistry 9 3%
Neuroscience 8 3%
Other 22 8%
Unknown 65 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 23 May 2023.
All research outputs
#1,478,838
of 25,736,439 outputs
Outputs from BMC Genomics
#256
of 11,316 outputs
Outputs of similar age
#31,882
of 345,874 outputs
Outputs of similar age from BMC Genomics
#3
of 188 outputs
Altmetric has tracked 25,736,439 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 11,316 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 97% 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 345,874 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 90% of its contemporaries.
We're also able to compare this research output to 188 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 98% of its contemporaries.