↓ Skip to main content

Tools for T-RFLP data analysis using Excel

Overview of attention for article published in BMC Bioinformatics, November 2014
Altmetric Badge

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 (81st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
14 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
56 Mendeley
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
Tools for T-RFLP data analysis using Excel
Published in
BMC Bioinformatics, November 2014
DOI 10.1186/s12859-014-0361-7
Pubmed ID
Authors

Nils Johan Fredriksson, Malte Hermansson, Britt-Marie Wilén

Abstract

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 2 4%
Sudan 1 2%
Malaysia 1 2%
Germany 1 2%
United States 1 2%
Unknown 50 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 27%
Researcher 11 20%
Student > Master 10 18%
Student > Bachelor 5 9%
Professor > Associate Professor 3 5%
Other 6 11%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 52%
Biochemistry, Genetics and Molecular Biology 7 13%
Environmental Science 5 9%
Computer Science 2 4%
Immunology and Microbiology 2 4%
Other 4 7%
Unknown 7 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 June 2021.
All research outputs
#3,334,999
of 18,908,606 outputs
Outputs from BMC Bioinformatics
#1,380
of 6,462 outputs
Outputs of similar age
#44,571
of 246,732 outputs
Outputs of similar age from BMC Bioinformatics
#107
of 467 outputs
Altmetric has tracked 18,908,606 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,462 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 78% 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 246,732 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 81% of its contemporaries.
We're also able to compare this research output to 467 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.