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SAMQA: error classification and validation of high-throughput sequenced read data

Overview of attention for article published in BMC Genomics, August 2011
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1 tweeter

Citations

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10 Dimensions

Readers on

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65 Mendeley
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8 CiteULike
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1 Connotea
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Title
SAMQA: error classification and validation of high-throughput sequenced read data
Published in
BMC Genomics, August 2011
DOI 10.1186/1471-2164-12-419
Pubmed ID
Authors

Thomas Robinson, Sarah Killcoyne, Ryan Bressler, John Boyle

Abstract

The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 2 3%
Italy 1 2%
Mexico 1 2%
France 1 2%
Germany 1 2%
Brazil 1 2%
Spain 1 2%
Sweden 1 2%
Other 0 0%
Unknown 53 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 49%
Student > Ph. D. Student 7 11%
Other 4 6%
Student > Bachelor 4 6%
Student > Master 4 6%
Other 14 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 57%
Computer Science 14 22%
Biochemistry, Genetics and Molecular Biology 4 6%
Unspecified 3 5%
Engineering 3 5%
Other 4 6%

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 22 August 2011.
All research outputs
#9,906,293
of 12,373,620 outputs
Outputs from BMC Genomics
#5,684
of 7,296 outputs
Outputs of similar age
#70,208
of 90,092 outputs
Outputs of similar age from BMC Genomics
#12
of 26 outputs
Altmetric has tracked 12,373,620 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,296 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 12th percentile – i.e., 12% 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 90,092 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.