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Statistical method to compare massive parallel sequencing pipelines

Overview of attention for article published in BMC Bioinformatics, March 2017
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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1 Wikipedia page

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Title
Statistical method to compare massive parallel sequencing pipelines
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1552-9
Pubmed ID
Authors

MH. Elsensohn, N. Leblay, S. Dimassi, A. Campan-Fournier, A. Labalme, F. Roucher-Boulez, D. Sanlaville, G. Lesca, C. Bardel, P. Roy

Abstract

Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect. Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines). The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.

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The data shown below were collected from the profile of 1 X user 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 17%
Student > Ph. D. Student 6 17%
Other 5 14%
Student > Bachelor 5 14%
Student > Postgraduate 2 6%
Other 5 14%
Unknown 6 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 34%
Agricultural and Biological Sciences 9 26%
Medicine and Dentistry 4 11%
Computer Science 3 9%
Neuroscience 2 6%
Other 1 3%
Unknown 4 11%
Attention Score in Context

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 05 April 2017.
All research outputs
#7,012,526
of 22,958,253 outputs
Outputs from BMC Bioinformatics
#2,696
of 7,307 outputs
Outputs of similar age
#113,333
of 311,244 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 138 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,307 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 61% 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 311,244 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 62% of its contemporaries.
We're also able to compare this research output to 138 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 63% of its contemporaries.