↓ Skip to main content

Monovar: single-nucleotide variant detection in single cells

Overview of attention for article published in Nature Methods, April 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
13 news outlets
blogs
1 blog
twitter
64 X users
patent
2 patents
facebook
2 Facebook pages

Citations

dimensions_citation
154 Dimensions

Readers on

mendeley
234 Mendeley
citeulike
4 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
Monovar: single-nucleotide variant detection in single cells
Published in
Nature Methods, April 2016
DOI 10.1038/nmeth.3835
Pubmed ID
Authors

Hamim Zafar, Yong Wang, Luay Nakhleh, Nicholas Navin, Ken Chen

Abstract

Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.

X Demographics

X Demographics

The data shown below were collected from the profiles of 64 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 234 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Indonesia 1 <1%
Czechia 1 <1%
Sweden 1 <1%
China 1 <1%
United Kingdom 1 <1%
Unknown 227 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 62 26%
Researcher 43 18%
Student > Master 26 11%
Student > Bachelor 20 9%
Student > Doctoral Student 14 6%
Other 32 14%
Unknown 37 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 62 26%
Agricultural and Biological Sciences 62 26%
Computer Science 26 11%
Medicine and Dentistry 13 6%
Neuroscience 6 3%
Other 22 9%
Unknown 43 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 137. 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 20 February 2024.
All research outputs
#304,743
of 25,530,891 outputs
Outputs from Nature Methods
#322
of 5,380 outputs
Outputs of similar age
#5,530
of 313,828 outputs
Outputs of similar age from Nature Methods
#4
of 84 outputs
Altmetric has tracked 25,530,891 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,380 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.5. This one has done particularly well, scoring higher than 94% 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 313,828 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 84 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 96% of its contemporaries.