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SNP calling by sequencing pooled samples

Overview of attention for article published in BMC Bioinformatics, September 2012
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  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

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6 X users

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258 Mendeley
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3 CiteULike
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Title
SNP calling by sequencing pooled samples
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-239
Pubmed ID
Authors

Emanuele Raineri, Luca Ferretti, Anna Esteve-Codina, Bruno Nevado, Simon Heath, Miguel Pérez-Enciso

Abstract

Performing high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) ∝ 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read - or, more likely, none - from a true singleton.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Austria 4 2%
United Kingdom 3 1%
Belgium 2 <1%
Brazil 2 <1%
Sweden 2 <1%
Norway 1 <1%
Ireland 1 <1%
Netherlands 1 <1%
Other 7 3%
Unknown 228 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 83 32%
Student > Ph. D. Student 75 29%
Student > Master 24 9%
Other 12 5%
Student > Bachelor 11 4%
Other 30 12%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 162 63%
Biochemistry, Genetics and Molecular Biology 28 11%
Computer Science 16 6%
Medicine and Dentistry 7 3%
Engineering 4 2%
Other 13 5%
Unknown 28 11%
Attention Score in Context

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 27 September 2012.
All research outputs
#7,546,261
of 23,023,224 outputs
Outputs from BMC Bioinformatics
#3,042
of 7,316 outputs
Outputs of similar age
#56,603
of 171,207 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 94 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,316 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 171,207 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 94 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 53% of its contemporaries.