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Xander: employing a novel method for efficient gene-targeted metagenomic assembly

Overview of attention for article published in Microbiome, August 2015
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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 (94th percentile)

Mentioned by

blogs
1 blog
twitter
42 tweeters
googleplus
1 Google+ user

Citations

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68 Dimensions

Readers on

mendeley
195 Mendeley
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Title
Xander: employing a novel method for efficient gene-targeted metagenomic assembly
Published in
Microbiome, August 2015
DOI 10.1186/s40168-015-0093-6
Pubmed ID
Authors

Qiong Wang, Jordan A. Fish, Mariah Gilman, Yanni Sun, C. Titus Brown, James M. Tiedje, James R. Cole

Abstract

Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes. We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences. Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

Twitter Demographics

The data shown below were collected from the profiles of 42 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 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 8%
Brazil 3 2%
Belgium 2 1%
Estonia 2 1%
Argentina 1 <1%
United Kingdom 1 <1%
Sweden 1 <1%
Chile 1 <1%
Denmark 1 <1%
Other 4 2%
Unknown 164 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 49 25%
Student > Ph. D. Student 49 25%
Student > Master 16 8%
Student > Bachelor 16 8%
Student > Doctoral Student 12 6%
Other 29 15%
Unknown 24 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 88 45%
Biochemistry, Genetics and Molecular Biology 31 16%
Environmental Science 19 10%
Computer Science 11 6%
Immunology and Microbiology 4 2%
Other 14 7%
Unknown 28 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 30. 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 04 May 2016.
All research outputs
#923,576
of 19,293,994 outputs
Outputs from Microbiome
#306
of 1,168 outputs
Outputs of similar age
#14,359
of 244,705 outputs
Outputs of similar age from Microbiome
#1
of 1 outputs
Altmetric has tracked 19,293,994 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.0. This one has gotten more attention than average, scoring higher than 73% 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 244,705 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 94% of its contemporaries.
We're also able to compare this research output to 1 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