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Fastq2vcf: a concise and transparent pipeline for whole-exome sequencing data analyses

Overview of attention for article published in BMC Research Notes, March 2015
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2 X users

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54 Mendeley
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Title
Fastq2vcf: a concise and transparent pipeline for whole-exome sequencing data analyses
Published in
BMC Research Notes, March 2015
DOI 10.1186/s13104-015-1027-x
Pubmed ID
Authors

Xiaoyi Gao, Jianpeng Xu, Joshua Starmer

Abstract

Whole-exome sequencing (WES) is a popular next-generation sequencing technology used by numerous laboratories with various levels of statistical and analytical expertise. Centralized databases, such as the Sequence Read Archive and the European Nucleotide Archive, allow data to be reanalyzed by independent labs to confirm results and derive additional insights. Access to new and shared data highlights the necessity for software that both lowers the statistical and analytical expertise required to generate results and promotes reproducible methodology among laboratories.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 4%
France 1 2%
United Kingdom 1 2%
Canada 1 2%
China 1 2%
United States 1 2%
Poland 1 2%
Unknown 46 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 31%
Student > Master 9 17%
Student > Ph. D. Student 7 13%
Student > Bachelor 7 13%
Student > Doctoral Student 3 6%
Other 4 7%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 41%
Biochemistry, Genetics and Molecular Biology 9 17%
Computer Science 6 11%
Engineering 3 6%
Nursing and Health Professions 1 2%
Other 4 7%
Unknown 9 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 01 May 2019.
All research outputs
#16,770,898
of 24,666,614 outputs
Outputs from BMC Research Notes
#2,458
of 4,442 outputs
Outputs of similar age
#159,007
of 263,697 outputs
Outputs of similar age from BMC Research Notes
#40
of 65 outputs
Altmetric has tracked 24,666,614 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,442 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 33rd percentile – i.e., 33% 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 263,697 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.