↓ Skip to main content

Identification of indels in next-generation sequencing data

Overview of attention for article published in BMC Bioinformatics, February 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

twitter
23 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
114 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Identification of indels in next-generation sequencing data
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0483-6
Pubmed ID
Authors

Aakrosh Ratan, Thomas L Olson, Thomas P Loughran, Webb Miller

Abstract

The discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases. We have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools. indelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz .

Twitter Demographics

The data shown below were collected from the profiles of 23 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 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 3 3%
France 2 2%
United States 2 2%
Korea, Republic of 1 <1%
Italy 1 <1%
Norway 1 <1%
Brazil 1 <1%
Netherlands 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 100 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 28%
Student > Ph. D. Student 27 24%
Student > Master 13 11%
Student > Bachelor 9 8%
Student > Doctoral Student 9 8%
Other 19 17%
Unknown 5 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 47%
Biochemistry, Genetics and Molecular Biology 33 29%
Computer Science 8 7%
Medicine and Dentistry 5 4%
Engineering 2 2%
Other 6 5%
Unknown 6 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 26 August 2015.
All research outputs
#1,869,861
of 18,034,577 outputs
Outputs from BMC Bioinformatics
#638
of 6,340 outputs
Outputs of similar age
#33,022
of 301,773 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 41 outputs
Altmetric has tracked 18,034,577 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,340 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 89% 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 301,773 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 41 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.