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SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines

Overview of attention for article published in BMC Genomics, March 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

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9 tweeters
facebook
1 Facebook page

Citations

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5 Dimensions

Readers on

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47 Mendeley
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Title
SV-AUTOPILOT: optimized, automated construction of structural variation discovery and benchmarking pipelines
Published in
BMC Genomics, March 2015
DOI 10.1186/s12864-015-1376-9
Pubmed ID
Authors

Wai Yi Leung, Tobias Marschall, Yogesh Paudel, Laurent Falquet, Hailiang Mei, Alexander Schönhuth, Tiffanie Yael Maoz

Abstract

Many tools exist to predict structural variants (SVs), utilizing a variety of algorithms. However, they have largely been developed and tested on human germline or somatic (e.g. cancer) variation. It seems appropriate to exploit this wealth of technology available for humans also for other species. Objectives of this work included: a) Creating an automated, standardized pipeline for SV prediction. b) Identifying the best tool(s) for SV prediction through benchmarking. c) Providing a statistically sound method for merging SV calls. The SV-AUTOPILOT meta-tool platform is an automated pipeline for standardization of SV prediction and SV tool development in paired-end next-generation sequencing (NGS) analysis. SV-AUTOPILOT comes in the form of a virtual machine, which includes all datasets, tools and algorithms presented here. The virtual machine easily allows one to add, replace and update genomes, SV callers and post-processing routines and therefore provides an easy, out-of-the-box environment for complex SV discovery tasks. SV-AUTOPILOT was used to make a direct comparison between 7 popular SV tools on the Arabidopsis thaliana genome using the Landsberg (Ler) ecotype as a standardized dataset. Recall and precision measurements suggest that Pindel and Clever were the most adaptable to this dataset across all size ranges while Delly performed well for SVs larger than 250 nucleotides. A novel, statistically-sound merging process, which can control the false discovery rate, reduced the false positive rate on the Arabidopsis benchmark dataset used here by >60%. SV-AUTOPILOT provides a meta-tool platform for future SV tool development and the benchmarking of tools on other genomes using a standardized pipeline. It optimizes detection of SVs in non-human genomes using statistically robust merging. The benchmarking in this study has demonstrated the power of 7 different SV tools for analyzing different size classes and types of structural variants. The optional merge feature enriches the call set and reduces false positives providing added benefit to researchers planning to validate SVs. SV-AUTOPILOT is a powerful, new meta-tool for biologists as well as SV tool developers.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
France 1 2%
Norway 1 2%
Sweden 1 2%
United States 1 2%
Unknown 42 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 45%
Student > Ph. D. Student 11 23%
Student > Bachelor 5 11%
Student > Master 3 6%
Other 1 2%
Other 2 4%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 36%
Biochemistry, Genetics and Molecular Biology 10 21%
Computer Science 10 21%
Medicine and Dentistry 3 6%
Immunology and Microbiology 2 4%
Other 1 2%
Unknown 4 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 21 April 2015.
All research outputs
#869,761
of 5,033,220 outputs
Outputs from BMC Genomics
#761
of 4,591 outputs
Outputs of similar age
#37,146
of 148,508 outputs
Outputs of similar age from BMC Genomics
#62
of 214 outputs
Altmetric has tracked 5,033,220 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,591 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 83% 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 148,508 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 74% of its contemporaries.
We're also able to compare this research output to 214 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 71% of its contemporaries.