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MetaID: A novel method for identification and quantification of metagenomic samples

Overview of attention for article published in BMC Genomics, December 2013
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  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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4 X users

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56 Mendeley
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1 CiteULike
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Title
MetaID: A novel method for identification and quantification of metagenomic samples
Published in
BMC Genomics, December 2013
DOI 10.1186/1471-2164-14-s8-s4
Pubmed ID
Authors

Satish M Srinivasan, Chittibabu Guda

Abstract

Advances in next-generation sequencing (NGS) technology has provided us with an opportunity to analyze and evaluate the rich microbial communities present in all natural environments. The shorter reads obtained from the shortgun technology has paved the way for determining the taxonomic profile of a community by simply aligning the reads against the available reference genomes. While several computational methods are available for taxonomic profiling at the genus- and species-level, none of these methods are effective at the strain-level identification due to the increasing difficulty in detecting variation at that level. Here, we present MetaID, an alignment-free n-gram based approach that can accurately identify microorganisms at the strain level and estimate the abundance of each organism in a sample, given a metagenomic sequencing dataset.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Brazil 2 4%
Sweden 1 2%
Germany 1 2%
Spain 1 2%
Belgium 1 2%
Unknown 47 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 30%
Student > Ph. D. Student 12 21%
Student > Master 8 14%
Professor > Associate Professor 3 5%
Student > Bachelor 2 4%
Other 7 13%
Unknown 7 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 52%
Biochemistry, Genetics and Molecular Biology 11 20%
Computer Science 2 4%
Mathematics 1 2%
Arts and Humanities 1 2%
Other 3 5%
Unknown 9 16%
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 05 March 2014.
All research outputs
#15,169,543
of 25,373,627 outputs
Outputs from BMC Genomics
#5,391
of 11,244 outputs
Outputs of similar age
#178,741
of 320,288 outputs
Outputs of similar age from BMC Genomics
#89
of 207 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 320,288 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 207 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.