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ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline

Overview of attention for article published in BMC Bioinformatics, October 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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8 X users

Citations

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106 Dimensions

Readers on

mendeley
132 Mendeley
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2 CiteULike
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Title
ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1274-4
Pubmed ID
Authors

Qian Qin, Shenglin Mei, Qiu Wu, Hanfei Sun, Lewyn Li, Len Taing, Sujun Chen, Fugen Li, Tao Liu, Chongzhi Zang, Han Xu, Yiwen Chen, Clifford A. Meyer, Yong Zhang, Myles Brown, Henry W. Long, X. Shirley Liu

Abstract

Transcription factor binding, histone modification, and chromatin accessibility studies are important approaches to understanding the biology of gene regulation. ChIP-seq and DNase-seq have become the standard techniques for studying protein-DNA interactions and chromatin accessibility respectively, and comprehensive quality control (QC) and analysis tools are critical to extracting the most value from these assay types. Although many analysis and QC tools have been reported, few combine ChIP-seq and DNase-seq data analysis and quality control in a unified framework with a comprehensive and unbiased reference of data quality metrics. ChiLin is a computational pipeline that automates the quality control and data analyses of ChIP-seq and DNase-seq data. It is developed using a flexible and modular software framework that can be easily extended and modified. ChiLin is ideal for batch processing of many datasets and is well suited for large collaborative projects involving ChIP-seq and DNase-seq from different designs. ChiLin generates comprehensive quality control reports that include comparisons with historical data derived from over 23,677 public ChIP-seq and DNase-seq samples (11,265 datasets) from eight literature-based classified categories. To the best of our knowledge, this atlas represents the most comprehensive ChIP-seq and DNase-seq related quality metric resource currently available. These historical metrics provide useful heuristic quality references for experiment across all commonly used assay types. Using representative datasets, we demonstrate the versatility of the pipeline by applying it to different assay types of ChIP-seq data. The pipeline software is available open source at https://github.com/cfce/chilin . ChiLin is a scalable and powerful tool to process large batches of ChIP-seq and DNase-seq datasets. The analysis output and quality metrics have been structured into user-friendly directories and reports. We have successfully compiled 23,677 profiles into a comprehensive quality atlas with fine classification for users.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Hungary 1 <1%
United States 1 <1%
Sweden 1 <1%
Unknown 128 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 23%
Student > Ph. D. Student 25 19%
Student > Master 12 9%
Student > Bachelor 12 9%
Student > Doctoral Student 11 8%
Other 19 14%
Unknown 23 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 47 36%
Agricultural and Biological Sciences 32 24%
Computer Science 11 8%
Medicine and Dentistry 7 5%
Engineering 3 2%
Other 8 6%
Unknown 24 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 20 July 2017.
All research outputs
#7,622,789
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,932
of 7,454 outputs
Outputs of similar age
#112,780
of 324,232 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 137 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 59% 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 324,232 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 64% of its contemporaries.
We're also able to compare this research output to 137 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 66% of its contemporaries.