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Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data

Overview of attention for article published in BMC Bioinformatics, October 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
37 Mendeley
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Title
Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1833-3
Pubmed ID
Authors

Jianing Gao, Changlin Wan, Huan Zhang, Ao Li, Qiguang Zang, Rongjun Ban, Asim Ali, Zhenghua Yu, Qinghua Shi, Xiaohua Jiang, Yuanwei Zhang

Abstract

Copy number variations (CNVs) are the main genetic structural variations in cancer genome. Detecting CNVs in genetic exome region is efficient and cost-effective in identifying cancer associated genes. Many tools had been developed accordingly and yet these tools lack of reliability because of high false negative rate, which is intrinsically caused by genome exonic bias. To provide an alternative option, here, we report Anaconda, a comprehensive pipeline that allows flexible integration of multiple CNV-calling methods and systematic annotation of CNVs in analyzing WES data. Just by one command, Anaconda can generate CNV detection result by up to four CNV detecting tools. Associated with comprehensive annotation analysis of genes involved in shared CNV regions, Anaconda is able to deliver a more reliable and useful report in assistance with CNV-associate cancer researches. Anaconda package and manual can be freely accessed at http://mcg.ustc.edu.cn/bsc/ANACONDA/ .

Twitter Demographics

The data shown below were collected from the profiles of 6 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 16%
Student > Master 6 16%
Student > Ph. D. Student 6 16%
Researcher 5 14%
Professor > Associate Professor 3 8%
Other 4 11%
Unknown 7 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 27%
Agricultural and Biological Sciences 7 19%
Computer Science 6 16%
Engineering 2 5%
Business, Management and Accounting 1 3%
Other 3 8%
Unknown 8 22%

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 09 October 2017.
All research outputs
#9,658,617
of 17,829,815 outputs
Outputs from BMC Bioinformatics
#3,176
of 6,281 outputs
Outputs of similar age
#122,518
of 288,032 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 32 outputs
Altmetric has tracked 17,829,815 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,281 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 47th percentile – i.e., 47% 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 288,032 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 56% of its contemporaries.
We're also able to compare this research output to 32 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 65% of its contemporaries.