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npInv: accurate detection and genotyping of inversions using long read sub-alignment

Overview of attention for article published in BMC Bioinformatics, July 2018
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Title
npInv: accurate detection and genotyping of inversions using long read sub-alignment
Published in
BMC Bioinformatics, July 2018
DOI 10.1186/s12859-018-2252-9
Pubmed ID
Authors

Haojing Shao, Devika Ganesamoorthy, Tania Duarte, Minh Duc Cao, Clive J. Hoggart, Lachlan J. M. Coin

Abstract

Detection of genomic inversions remains challenging. Many existing methods primarily target inzversions with a non repetitive breakpoint, leaving inverted repeat (IR) mediated non-allelic homologous recombination (NAHR) inversions largely unexplored. We present npInv, a novel tool specifically for detecting and genotyping NAHR inversion using long read sub-alignment of long read sequencing data. We benchmark npInv with other tools in both simulation and real data. We use npInv to generate a whole-genome inversion map for NA12878 consisting of 30 NAHR inversions (of which 15 are novel), including all previously known NAHR mediated inversions in NA12878 with flanking IR less than 7kb. Our genotyping accuracy on this dataset was 94%. We used PCR to confirm the presence of two of these novel inversions. We show that there is a near linear relationship between the length of flanking IR and the minimum inversion size, without inverted repeats. The application of npInv shows high accuracy in both simulation and real data. The results give deeper insight into understanding inversion.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 18%
Student > Master 9 18%
Researcher 8 16%
Student > Bachelor 5 10%
Student > Doctoral Student 4 8%
Other 1 2%
Unknown 13 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 33%
Agricultural and Biological Sciences 12 24%
Computer Science 4 8%
Environmental Science 2 4%
Physics and Astronomy 1 2%
Other 1 2%
Unknown 13 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 May 2022.
All research outputs
#14,421,028
of 23,096,849 outputs
Outputs from BMC Bioinformatics
#4,771
of 7,328 outputs
Outputs of similar age
#185,531
of 327,048 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 106 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,328 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.