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MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing

Overview of attention for article published in BMC Bioinformatics, December 2016
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
20 tweeters

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
72 Mendeley
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Title
MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing
Published in
BMC Bioinformatics, December 2016
DOI 10.1186/s12859-016-1358-1
Pubmed ID
Authors

Hardik I. Parikh, Vishal N. Koparde, Steven P. Bradley, Gregory A. Buck, Nihar U. Sheth

Abstract

Recent advances in next-generation sequencing have revolutionized genomic research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq platform from Illumina, Inc., is being used to characterize the composition and dynamics of extremely complex/diverse microbial communities. For this analysis on the Illumina platform, merging and quality filtering of paired-end reads are essential first steps in data analysis to ensure the accuracy and reliability of downstream analysis. We have developed the Merging and Filtering Tool (MeFiT) to combine these pre-processing steps into one simple, intuitive pipeline. MeFiT invokes CASPER (context-aware scheme for paired-end reads) for merging paired-end reads and provides users the option to quality filter the reads using the traditional average Q-score metric or using a maximum expected error cut-off threshold. MeFiT provides an open-source solution that permits users to merge and filter paired end illumina reads. The tool has been implemented in python and the source-code is freely available at https://github.com/nisheth/MeFiT .

Twitter Demographics

The data shown below were collected from the profiles of 20 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 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Estonia 2 3%
India 1 1%
Brazil 1 1%
Australia 1 1%
United Kingdom 1 1%
France 1 1%
Spain 1 1%
United States 1 1%
Unknown 63 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 31%
Student > Ph. D. Student 10 14%
Student > Master 8 11%
Other 7 10%
Student > Doctoral Student 5 7%
Other 10 14%
Unknown 10 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 39%
Biochemistry, Genetics and Molecular Biology 18 25%
Engineering 3 4%
Computer Science 3 4%
Immunology and Microbiology 2 3%
Other 6 8%
Unknown 12 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 06 December 2016.
All research outputs
#1,275,331
of 16,519,376 outputs
Outputs from BMC Bioinformatics
#341
of 5,953 outputs
Outputs of similar age
#40,345
of 394,416 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 424 outputs
Altmetric has tracked 16,519,376 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,953 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 394,416 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 89% of its contemporaries.
We're also able to compare this research output to 424 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 91% of its contemporaries.