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GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service

Overview of attention for article published in BMC Genomics, January 2018
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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5 tweeters

Citations

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5 Dimensions

Readers on

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35 Mendeley
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Title
GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-017-4334-x
Pubmed ID
Authors

Yiqi Wang, Gen Li, Mark Ma, Fazhong He, Zhuo Song, Wei Zhang, Chengkun Wu

Abstract

Whole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing. We developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55× WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5× whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5× WGS analysis task in 2.4 min at a cost of $3.6. WGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://github.com/Genetalks/wgs_analysis_demo .

Twitter Demographics

The data shown below were collected from the profiles of 5 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 17%
Researcher 6 17%
Student > Master 4 11%
Other 3 9%
Student > Bachelor 2 6%
Other 5 14%
Unknown 9 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 20%
Medicine and Dentistry 6 17%
Agricultural and Biological Sciences 5 14%
Computer Science 3 9%
Chemical Engineering 1 3%
Other 4 11%
Unknown 9 26%

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 25 January 2018.
All research outputs
#3,440,783
of 12,416,981 outputs
Outputs from BMC Genomics
#2,161
of 7,358 outputs
Outputs of similar age
#113,996
of 339,770 outputs
Outputs of similar age from BMC Genomics
#6
of 22 outputs
Altmetric has tracked 12,416,981 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,358 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 70% 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 339,770 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 65% of its contemporaries.
We're also able to compare this research output to 22 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 72% of its contemporaries.