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VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data

Overview of attention for article published in Microbiome, July 2017
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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 (95th percentile)

Mentioned by

1 blog
99 tweeters
3 Wikipedia pages
1 video uploader


175 Dimensions

Readers on

497 Mendeley
2 CiteULike
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VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data
Published in
Microbiome, July 2017
DOI 10.1186/s40168-017-0283-5
Pubmed ID

Jie Ren, Nathan A. Ahlgren, Yang Young Lu, Jed A. Fuhrman, Fengzhu Sun


Identifying viral sequences in mixed metagenomes containing both viral and host contigs is a critical first step in analyzing the viral component of samples. Current tools for distinguishing prokaryotic virus and host contigs primarily use gene-based similarity approaches. Such approaches can significantly limit results especially for short contigs that have few predicted proteins or lack proteins with similarity to previously known viruses. We have developed VirFinder, the first k-mer frequency based, machine learning method for virus contig identification that entirely avoids gene-based similarity searches. VirFinder instead identifies viral sequences based on our empirical observation that viruses and hosts have discernibly different k-mer signatures. VirFinder's performance in correctly identifying viral sequences was tested by training its machine learning model on sequences from host and viral genomes sequenced before 1 January 2014 and evaluating on sequences obtained after 1 January 2014. VirFinder had significantly better rates of identifying true viral contigs (true positive rates (TPRs)) than VirSorter, the current state-of-the-art gene-based virus classification tool, when evaluated with either contigs subsampled from complete genomes or assembled from a simulated human gut metagenome. For example, for contigs subsampled from complete genomes, VirFinder had 78-, 2.4-, and 1.8-fold higher TPRs than VirSorter for 1, 3, and 5 kb contigs, respectively, at the same false positive rates as VirSorter (0, 0.003, and 0.006, respectively), thus VirFinder works considerably better for small contigs than VirSorter. VirFinder furthermore identified several recently sequenced virus genomes (after 1 January 2014) that VirSorter did not and that have no nucleotide similarity to previously sequenced viruses, demonstrating VirFinder's potential advantage in identifying novel viral sequences. Application of VirFinder to a set of human gut metagenomes from healthy and liver cirrhosis patients reveals higher viral diversity in healthy individuals than cirrhosis patients. We also identified contig bins containing crAssphage-like contigs with higher abundance in healthy patients and a putative Veillonella genus prophage associated with cirrhosis patients. This innovative k-mer based tool complements gene-based approaches and will significantly improve prokaryotic viral sequence identification, especially for metagenomic-based studies of viral ecology.

Twitter Demographics

The data shown below were collected from the profiles of 99 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 497 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 497 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 119 24%
Researcher 91 18%
Student > Master 67 13%
Student > Bachelor 50 10%
Other 18 4%
Other 67 13%
Unknown 85 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 122 25%
Biochemistry, Genetics and Molecular Biology 120 24%
Immunology and Microbiology 36 7%
Environmental Science 26 5%
Computer Science 22 4%
Other 56 11%
Unknown 115 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 60. 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 19 June 2020.
All research outputs
of 17,355,315 outputs
Outputs from Microbiome
of 1,030 outputs
Outputs of similar age
of 273,322 outputs
Outputs of similar age from Microbiome
of 3 outputs
Altmetric has tracked 17,355,315 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,030 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.8. This one has done well, scoring higher than 88% 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 273,322 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 95% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them