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RADIS: analysis of RAD-seq data for interspecific phylogeny

Overview of attention for article published in Bioinformatics, June 2016
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Article details
Title
RADIS: analysis of RAD-seq data for interspecific phylogeny
Published in
Bioinformatics, June 2016
DOI 10.1093/bioinformatics/btw352
Pubmed ID
Authors
Abstract

In an attempt to make the processing of RAD-seq data easier and allow rapid and automated exploration of parameters/data for phylogenetic inference, we introduce the perl pipeline RADIS Users of RADIS can let their raw Illumina data be processed up to phylogenetic tree inference, or stop (and restart) the process at some point. Different values for key parameters can be explored in a single analysis (e.g. loci building, sample/loci selection), making possible a thorough exploration of data. RADIS relies on Stacks for demultiplexing of data, removing PCR duplicates and building individual and catalog loci. Scripts have been specifically written for trimming of reads and loci/sample selection. Finally, RAxML is used for phylogenetic inferences, though other software may be utilised. RADIS is written in perl, designed to run on Linux and Unix platforms. RADIS and its manual are freely available from http://www1.montpellier.inra.fr/CBGP/software/RADIS/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 1%
Poland 1 1%
Netherlands 1 1%
Ireland 1 1%
France 1 1%
Austria 1 1%
Unknown 86 93%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 26 28%
Researcher 25 27%
Student > Master 7 8%
Other 5 5%
Student > Bachelor 5 5%
Other 13 14%
Unknown 11 12%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 52 57%
Biochemistry, Genetics and Molecular Biology 16 17%
Environmental Science 5 5%
Nursing and Health Professions 1 1%
Computer Science 1 1%
Other 0 0%
Unknown 17 18%
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 03 October 2016.
All research outputs
#22,411,293
of 32,444,292 outputs
Outputs from Bioinformatics
#11,326
of 14,104 outputs
Outputs of similar age
#199,388
of 319,093 outputs
Outputs of similar age from Bioinformatics
#192
of 216 outputs
Altmetric has tracked 32,444,292 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,104 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 17th percentile – i.e., 17% 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 319,093 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 216 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.